ABSTRACT

CHIANG, MU-HUAN. Energy Optimization in Sensor Networks. (Under the direction of Gregory T. Byrd).

Recent advances in wireless communications and computing technology are enabling the emergence of low-

cost devices that incorporate sensing, processing, and communication functionalities. A large number of these devices

are deployed to create a sensor network for both monitoring and control purposes. Sensor networks are currently an

active research area mainly due to the potential of their applications. However, the operation of large scale sensor

networks still requires solutions to numerous technical challenges that stem primarily from the constraints imposed

by simple sensor devices. Among these challenges, the powerconstraint is the most critical one, since it involves not

only reducing the energy consumption of a single sensor but also maximizing the lifetime of an entire network. The

network lifetime can be maximized only by incorporating energy awareness into every stage of sensor network design

and operation, thus empowering the system with the ability to make dynamic tradeoffs among energy consumption,

system performance, and operational fidelity.

Optimizing the energy usage is a critical challenge for wireless sensor networks (WSNs). The requirements

of energy optimization schemes are as follows. (1)Low individual energy consumption: Sensor nodes can use up

their limited energy supply, carrying out computations andtransmission. In typical WSNs, nodes play a dual role

as both data sender and data router. Malfunctioning of some sensor nodes due to power failure can cause significant

topological changes and may require rerouting of packets and network reorganization. Therefore, reducing the energy

consumption of each sensor node is critical for WSNs. (2)Balanced energy usage: While minimizing the energy

consumption of individual sensor nodes is important, the energy status of the entire network should also be of the

same order. If certain nodes have much higher workload than others, these nodes will drain off their energy rapidly

and adversely impact the overall system lifetime. The workload of sensors should be balanced in order to achieve

longer system lifetime. (3)Low computation and communication overhead: The resource limitations imposed by

sensor hardware call for simple protocols that require minimal processing and a small memory footprint. The extra

computation and communication introduced by the energy optimization schemes must also be kept low. Otherwise,

energy required to perform the optimization schemes may outweigh the benefits.

This thesis concentrates on the energy optimization issuesin wireless sensor networks. We study the power

consumption characteristics of typical sensor platforms,and propose energy optimization schemes in network and

application level. We design distributed algorithms that reduce the amount of data traffic and unnecessary overhear-

ing waste in WSNs, and further propose load balancing mechanisms that alleviate the unbalanced energy usage and

prolong the effective system lifetime.

At the network level,Adaptive Aggregation Tree (AAT)is proposed to dynamically transform the routing

tree, using easily-obtained overheard information, to improve the aggregation efficiency. The local adaptivity of AAT

achieves significant energy reduction, compared to the shortest-path tree where aggregation occurs opportunistically.

We also proposeNeighborhood-Aware Density Control (NADC), which exploits the overheard information to reduce

the unnecessary overhearing waste along routing paths. In NADC, nodes observe their neighborhood and adapt their

participation in the multihop routing topology. By reducing the node density near the routing paths, the overhearing

waste can be reduced, and the extremely unbalanced energy usage among sensor nodes is also alleviated, which results

in a longer system lifetime. The unbalanced energy usage problem is further addressed at the application level, where

we proposeZone-Repartitioning (Z-R)for load balancing in data-centric storage systems. Z-R reduces the workload

of certain hot-spots by distributing their communication load to other nodes when the event frequency of certain areas

is much higher than the others.

Energy Optimization in Sensor Networks

by

Mu-Huan Chiang

A dissertation submitted to the Graduate Faculty ofNorth Carolina State University

in partial fulfillment of therequirements for the Degree of

Doctor of Philosophy

Computer Engineering

Raleigh, North Carolina

2007

Approved By:

Dr. Peng Ning Dr. Mihail Sichitiu

Dr. Gregory T. Byrd Dr. Alexander G. DeanChair of Advisory Committee

ii

Biography

Mu-Huan Chiang was born and raised in Taipei, Taiwan. After receiving his Master degree in Electrical

Engineering from National Cheng Kung University in 2001, heserved in the military as an Information Security

Officer in National Security Bureau, Taiwan from 2001 through 2003.

Chiang started his Ph.D. study in the Center for Efficient, Scalable and Reliable Computing (CESR) at

North Carolina State University in 2003. He also worked as a System Test Engineer Intern at CipherOptics Inc. in

2005 and 2006 at Raleigh, North Carolina. His research interests include sensor networks, wireless communication,

and computer architecture.

iii

Acknowledgements

First and foremost, I would like to thank my advisor, Dr. Gregory T. Byrd, for his encouragement, guidance,

and support for the years I spent in NC State. I am grateful to Dr. Sichitiu, Dr. Dean, Dr. Ning, and Dr. Reeves for

their valuable advice and constructive criticism which helped me through my research.

I would like to thank my family, especially my mother and father, for their support and encouragement over

the years. I thank my girlfriend, To-Wen, for her love and faith in me. I would also like to acknowledge my colleagues

(Yu-Kuen, Rob, Salil, Gary, and Chungsoo) for their helpinghands during my study at CESR lab.

iv

Contents

List of Figures vii

List of Tables ix

1 Introduction 11.1 Wireless Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . 11.2 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . 2

1.2.1 Sensor Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . 21.2.2 Network Construction and Maintenance . . . . . . . . . . . . .. . . . . . . . . . . . . . . . 31.2.3 Data Dissemination and Collection . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . 4

1.3 The Need for Energy Optimization in WSN . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . 41.3.1 Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . 41.3.2 State of the Research . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . 5

1.4 Contributions and Scope of this Thesis . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . 6

2 Energy Optimization Schemes in WSN 82.1 Low Power Sensor Architecture . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . 8

2.1.1 Power-Aware Computing . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . 82.1.2 Energy-Aware Software . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . 102.1.3 Energy-Efficient Radio Transceiver . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . 10

2.2 Energy-Efficient Communication . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . 112.2.1 Modulation Schemes . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . 112.2.2 Energy-Efficient MAC Protocols . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . 122.2.3 Energy-Reliability Tradeoff . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . 14

2.3 Lifetime-Aware Energy Management . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . 152.3.1 In-Network Processing . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . 152.3.2 Data-Centric Storage . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . 152.3.3 Traffic Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . 162.3.4 Topology Management . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . 16

3 Reduction of Communication Energy Consumption 183.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . 19

3.1.1 Query Processing in Sensor Networks . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . 193.1.2 Data aggregation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . 203.1.3 Aggregation Trees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . 20

3.2 Adaptive Aggregation Tree . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . 233.2.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . 233.2.2 Preliminary Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . 253.2.3 Non-Loop-Free AAT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . 263.2.4 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . 27

v

3.3 Implementation Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . 293.3.1 Acquisition of Neighbor Information . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . 293.3.2 Neighbor Table Management . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . 303.3.3 Implicit Acknowledgment . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . 31

3.4 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . 313.4.1 Computation Overhead . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . 313.4.2 Network Simulation Settings . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . 323.4.3 Packet Delivery Ratio . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . 323.4.4 Total Energy Consumption . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . 34

3.5 AAT on GPSR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . 353.5.1 GPSR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . 353.5.2 AAT on GPSR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . 363.5.3 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . 36

3.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . 37

4 Overhearing Reduction along Routing Paths 394.1 Unbalanced Energy Usage . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . 40

4.1.1 Preliminary analysis . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . 404.1.2 Reduce Overhearing on Routing Paths . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . 42

4.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . 424.2.1 Density Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . 424.2.2 Transmission Power Adjustment . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . 434.2.3 Balanced Energy Usage . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . 444.2.4 Location-Varying Deployment . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . 44

4.3 Neighborhood-Aware Density Control . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . 454.3.1 System Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . 454.3.2 Uniform Density Control . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . 454.3.3 Neighborhood Type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . 464.3.4 NADC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . 474.3.5 Tuning NADC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . 484.3.6 Implementation Issues . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . 49

4.4 Evaluation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . 494.4.1 Simulation Settings . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . 494.4.2 Unbalanced Energy Consumption . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . 504.4.3 System Performance and Lifetime . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . 514.4.4 Unbalanced Energy Usage . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . 524.4.5 Delay of Event Delivery . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . 534.4.6 NADC with AAT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . 54

4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . 56

5 Hot-spot Avoidance through Load-balancing 575.1 Background and Related Work . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . 58

5.1.1 Data-Centric Storage . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . 585.1.2 Geographic Hash Table . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . 595.1.3 GHT Scaling: Structured Replication . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . 595.1.4 Resilient Data-Centric Storage . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . 60

5.2 Mechanisms for Zone Repartitioning . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . 615.3 Analytical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . 625.4 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . 63

5.4.1 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . 635.4.2 Calculation of Energy Consumption . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . 645.4.3 Energy Consumption in Broadcast . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . 655.4.4 Simulation Environment Setting . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . 655.4.5 Load Distribution of Hot-Spots . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . 66

vi

5.4.6 Relationship between Event Frequency, Number of Queries, and Simulation Time . . . . . . . 675.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . 69

6 Conclusion 70

Bibliography 73

vii

List of Figures

1.1 Conceptual diagram of a wireless sensor network. . . . . . .. . . . . . . . . . . . . . . . . . . . . . 21.2 Tmote Sky: (top) front side, (bottom) back side of the node [48]. . . . . . . . . . . . . . . . . . . . . 3

2.1 Components of energy consumption in radio communication [25] . . . . . . . . . . . . . . . . . . . 102.2 Radio energy per bit as a function of packet size and modulation level, using typical radio parameters

for sensor networks [63]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . 11

3.1 A simple routing tree, from source nodes (grey) to sink (black). Messages are received and retrans-mitted by forwarding nodes (white). Dotted lines are links that are available, but are not in the routingtree. Aggregation reduces message count from 15 to 10. . . . . .. . . . . . . . . . . . . . . . . . . . 19

3.2 A minimal Steiner tree can use non-terminal (white) nodes to connect all terminal (black) nodes usingthe minimal number of edges. The minimal Steiner tree is indicated by heavy line segments. . . . . . 21

3.3 Snooping on neighborhood traffic. Node A overhears the message from X to Y, and can change itsparent from B to Y, to improve aggregation. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . 23

3.4 An example SPT and its corresponding AAT. Source nodes are represented by red circles. In AAT,nodes can only select parents in the same level or one level up, which is loop-free if there are no loopsin each level. The level in AAT is actually the level in SPT, which is not changed during the AATparent switching process. . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . 24

3.5 Three different aggregation trees for a sensor network.The network includes 1024 nodes with randomdisplacement from a320 × 320 grid. The green dotted lines represent the possible communicationlinks. The dark lines represent paths used in the aggregation tree. The sink node is in the middle ofthe network, and 100 randomly-selected source nodes are shown as circles. . . . . . . . . . . . . . . 25

3.6 The tree depth of ten random networks, and the corresponding tree cost (normalized to the cost of SPT). 263.7 The average tree cost and depth with different number source nodes (10 simulation runs each). The

difference between AAT and AAT-nlf is not obvious, in terms of both cost and depth. When thenumber of sources increases, the cost of AAT becomes very close to IKMB. The tree cost reduction ofAAT against SPT is around 34%, independent of the number of source nodes. The depth increase ofAAT over SPT increases gradually with the number of sources,from 25% to 83%. . . . . . . . . . . . 27

3.8 The packet sending/receiving process of AAT. The dark area shows the additional computation overhead. 323.9 Event delivery ratio versus link failure rate for AAT andSPT, with and without implicit acknowledge-

ments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . 333.10 The timing of event delivery for AAT and SPT, with and without acknowledgements. . . . . . . . . . 343.11 The components of energy consumption for communication for different link failure rates. The error

bar represents the variance on the total energy consumptionfor the 10 simulated networks. . . . . . . 353.12 The total communication energy consumption with different number of source nodes. The link failure

rate is 5%. The error bar represents the variance among the 10simulated networks. The energy savingof AAT is around 23%, independent of the number of source nodes. . . . . . . . . . . . . . . . . . . 36

3.13 The comparison of the original GPSR and GPSR with AAT. The result is the average of 10 randomnetworks. With different number of source nodes, the tree cost reduction is between 37% and 47%. . 37

viii

3.14 The energy consumption of GPSR and GPSR with AAT in networks with 5% link failure rate. Theenergy consumption for beacon packets are not included. Theerror bar represents the variance amongthe 10 simulated networks. The energy saving is around 31%, independent of the number of sourcenodes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . 38

4.1 The network model for analysis.Hmax is the maximum number of hops required to forward packetsfrom the outermost nodes to the base station. . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . 40

4.2 The average number of messages sent, received, and overheard by nodes in different levels, with andwithout aggregation, with fixedBt = 8 andPS(k) = 0.01. . . . . . . . . . . . . . . . . . . . . . . . . 41

4.3 The average number of messages sent, received, and overheard by nodes in different levels, with andwithout aggregation, with fixedHmax = 15 andPS(k) = 0.01. . . . . . . . . . . . . . . . . . . . . . . 42

4.4 State transitioning diagram of uniform density control. NB represents the number of neighbors, andTh represents the threshold value. . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . 46

4.5 Different neighborhood types in event-based sensor networks. The outermost rectangular area repre-sents the whole sensor field. . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . 47

4.6 Percentage of dead nodes in areas in different levels of the network. The parenthesized number repre-sents the total number of sensor nodes in each area. . . . . . . . .. . . . . . . . . . . . . . . . . . . 51

4.7 Four different lifetime indications of the simulation.. . . . . . . . . . . . . . . . . . . . . . . . . . . 524.8 Distribution of sensor nodes with different energy consumption. . . . . . . . . . . . . . . . . . . . . 534.9 The timing of event delivery from 20 randomly chosen source nodes in the network. Each source sends

one event at time 0. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . 534.10 The difference in the timing of event delivery from source nodes in different hops away from the base

station. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . 544.11 Four lifetime indications of sensor networks with Adaptive Aggregation Tree accommodated. . . . . . 554.12 The comparison of NADC and NADC with AAT. . . . . . . . . . . . . .. . . . . . . . . . . . . . . 554.13 Comparison of delivery delay from source nodes in different hops away from the base station. . . . . 55

5.1 Structured Replication . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . 605.2 Split-merge process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . 625.3 Energy consumption of each sensor node in GHT-4, GHT-16,and Z-R . . . . . . . . . . . . . . . . . 665.4 Total energy consumption with varyingfH andQ (fL = 1

2000 ) . . . . . . . . . . . . . . . . . . . . . 685.5 Total energy consumption in 10-minute simulations . . . .. . . . . . . . . . . . . . . . . . . . . . . 695.6 Total energy consumption in 30-minute simulations . . . .. . . . . . . . . . . . . . . . . . . . . . . 69

ix

List of Tables

1.1 Current consumption of Tmote Sky when active [48] . . . . . .. . . . . . . . . . . . . . . . . . . . 5

3.1 Mica2 current consumption model . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . 323.2 Energy consumption estimation for Fig. 3.8 . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . 323.3 Simulation settings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . 33

4.1 Power consumption (inmW) of Medusa II and Rockwell’s Wins nodes (with processor and sensor inactive mode) [63] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . 43

4.2 NADC states . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . 484.3 Network setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . 504.4 Statistics of the nodes’ energy usage in Fig. 4.8 . . . . . . .. . . . . . . . . . . . . . . . . . . . . . 52

5.1 Communication cost of External Storage (ES), Local Storage (LS), and Data-Centric Storage (DCS) . 595.2 Estimated total energy consumed by GHT and Z-R . . . . . . . . .. . . . . . . . . . . . . . . . . . 635.3 Simulation settings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . 665.4 Hot-spot and total energy consumption . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . 67

6.1 Proposed schemes for energy optimization . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . 71

1

Chapter 1

Introduction

Wireless sensor networks (WSN) [2] have emerged as a promising solution for various applications, in fields

as diverse as climate monitoring, tactical surveillance, vehicle tracking, and earthquake detection. With improvements

in sensor technology, it has become possible to build small sensor devices with a relatively high computational power

at a low cost. Yet, the sensor devices’ untethered nature also poses interesting design challenges.

Energy consumption is the most important factor to determine the life of a sensor network, because usually

sensor nodes are driven by batteries and have very low energysources. This makes energy optimization more com-

plicated in sensor networks; it involves not only reductionof energy consumption but also prolonging the life of the

network as much as possible. This must be done by having energy awareness in every aspect of design and operation

[34]. A single configuration may not fulfill the requirementsto work energy-efficiently in different environments.

Thus, mechanisms that can change adaptively in response to various situations are critical for energy efficiency in such

systems.

This thesis concentrates on the energy optimization issuesin wireless sensor networks. We study the power

consumption characteristics of typical sensor platforms,and propose energy optimization schemes in network and

application level. We design distributed algorithms that reduces the amount of data traffic and unnecessary overhearing

waste in sensor networks, and further propose load balancing mechanisms that alleviate the unbalanced energy usage

and prolong the effective system lifetime.

1.1 Wireless Sensor Networks

Wireless sensor networks are made up of a large number of inexpensive devices that are networked via

low power wireless communications. Recent technological advances in microelectronics, sensing, signal processing,

wireless communication and networking have made it possible to realize a dense network of inexpensive wireless

sensor nodes, each having sensing, computational and communication capabilities. These ubiquitous wireless sensor

networks create a new paradigm in the way we interact with ourenvironment. Proposed applications of sensor net-

2

Figure 1.1: Conceptual diagram of a wireless sensor network.

works include environmental monitoring, natural disasterprediction, homeland security, health care, manufacturing,

transportation, and home appliances and entertainment.

Conceptually, a wireless sensor network consists of a densenetwork of nodes as shown in Fig. 1.1. In typical

deployment scenarios, a few neighboring nodes lie within the communication radius of each node. Each sensor node

performs several functions such as (1) sensing the physicalparameters of its environment, (2) processing the raw data

locally to extract the feature of interest and (3) transmitting the information to its neighbors through a wireless link.

Unlike cellular networks or wireless LAN, there are no distributed base stations or access points in wireless sensor

networks. Hence, each node operates as a relay point to implement a multihop communication link by receiving data

from one of its neighbor, and then processing it before routing it to the next neighbor towards the destination. In some

cases, more functions such as data compression and encryption are also incorporated.

A common characteristic of many WSN application scenarios is that sensor nodes are deployed and tasked to

monitor the environment. In particular, when sensor nodes detect a significant event, they are not expected to respond

themselves. The prime reason is that some physical action isoften required like sounding an alarm, adjusting some

valves, or stopping an intruder, which would drain the batteries and increase the form factor significantly. A single

sink can only serve a limited number of sensor nodes, so largedeployments may include multiple sinks, each serving

a patch of the nodes.

1.2 Challenges

The advances in wireless sensor networks, while promising,also have challenges, such as resource limita-

tions, dynamic environment, and various application needs. These challenges and tradeoffs are discussed as follows.

1.2.1 Sensor Platform

For successful large scale deployment of wireless sensor networks, each node must have low power con-

sumption, low operating and system cost and a small form factor. Fig. 1.2 presents the Telos sensor node. As the

3

Figure 1.2: Tmote Sky: (top) front side, (bottom) back side of the node [48].

figure shows, the main components of a typical sensor node include an antenna and a radio frequency (RF) transceiver

to allow communication with other nodes, a memory unit, a CPU, the sensor unit, and the power source which is

usually provided by batteries. Although their limited sizemakes them attractive for use in a number of situations, the

small size limits battery capacity requiring every operation to be done efficiently. It also limits the radio transmission

range and suggests a small multihop transmission structure.

The power restrictions of sensor nodes are raised due to their small physical size and lack of wires. Since the

absence of wires results in lack of a constant power supply, not many power options exist. Sensor nodes are typically

battery-driven. The power is used for various operations ineach node, such as running the sensors, processing the

information gathered and data communication. An alternative power source of sensor nodes is energy scavenging from

ambient sources [56]. Although many different techniques are available to harvest energy from various environments

to power electronics, the amount of available raw energy (for example, sunlight, vibration, heat) and the surface area or

net mass that the sensor permits limit the power yield. Therefore, the derived energy from scavenging is still limited.

In the case of computational power, computations are linkedwith the available amount of power. Since

there is a limited amount of power, computations are constrained. The storage size is also limited due to the power

and physical size of sensor nodes.

1.2.2 Network Construction and Maintenance

Two key challenges in the deployment of WSNs are the large number of devices involved and the necessity

to embed them in a dynamic physical environment. For example, consider a habitat monitoring sensor network that

is to be deployed in a remote forest. Deployment of this network can be done by dropping a large number of sensor

nodes from a plane. In this example and in many other anticipated applications, we cannot expect the sensor field

to be deployed in a regular fashion (e.g., a linear array, 2D lattice). More importantly, uniform deployment does not

correspond to uniform connectivity owing to obstructions,interference, and other environmental factors. Thus the

4

deployed systems must be designed to operate under environmental dynamics while preserving the sensing coverage

and network connectivity.

In some WSN applications, the large number of elements in thenetwork and the harsh environment make

the maintenance operations (e.g., recharge depleted batteries, replace failed nodes) prohibitively expensive. In such

contexts some mechanisms are required to maintain the system performance and operational fidelity when sensor

nodes fail or deplete their limited energy.

1.2.3 Data Dissemination and Collection

Basic capabilities in WSN involve mechanisms of disseminating information over many nodes and collecting

data to a sink node. Due to the power restrictions and environmental dynamics of WSN, these mechanisms have to be

low power, scalable with the number of nodes, and fault tolerant to nodes that go up or down, or move in and out of

range. Moreover, some sensor nodes may fail or be blocked dueto lack of power, physical damage, or environmental

interference. If many nodes fail, some mechanisms must accommodate formation of new links and routes to the data

collection sinks. This may require actively adjusting transmit power to change the existing topology, or rerouting

packets through regions of the network where more energy is available.

1.3 The Need for Energy Optimization in WSN

One of the most important limitations of sensor networks is energy conservation. Wireless sensors operate

on limited power sources, therefore, their main focus is on power conservation through appropriate optimization of

communication and operation management. Energy awarenessmust be incorporated into groups of communicating

sensor nodes of the entire network as well as into the individual nodes. Especially for systems such as intrusion

detection and target tracking, where long periods of minimal sensing activity are intermixed with short periods of

intense sensor processing, workloads are highly irregularboth spatially and temporally.

The energy consumption in WSN involves three main components: Sensing Unit (Sensing transducer and

A/D converter), Communication Unit (transmission and receiver radio), and Computing/Processing Unit. Between

sensing, computational and communication, the power consumption needed for communication typically dominates a

node’s power budget. Table 1.1 shows a breakdown of the current consumption of a state-of-the-art sensor node when

active. The corresponding power consumption can be derivedby multiplying the current consumption with the voltage

supply (about 3V). The results indicate that the power consumption of transmitter and receiver is much higher than

the other components. To overcome this bottleneck, it is critical to reduce the power consumption for communication,

and the research in this thesis focuses on the optimization of energy usage for communication.

1.3.1 Requirements

Optimizing the energy usage is a critical challenge for WSNs. The requirements of energy optimization

schemes are delineated as follows.

5

Table 1.1: Current consumption of Tmote Sky when active [48]Components Active current consumption (mA) Condition

Transmitter 17.4 0 dBm output powerReceiver 19.7 -94 dBm Rx sensitivity

Microprocessor 0.5 3V supply, 1MHz clockSensors <0.03 -

Voltage regulator 0.02 -

• Low individual energy consumption — Sensor nodes can use up their limited energy supply, carrying out

computations and transmission. In a multihop WSN, nodes play a dual role as data sender and data router.

Malfunctioning of some sensor nodes due to power failure cancause significant topological changes and might

require rerouting of packets and reorganization of the network. Therefore, reducing the energy consumption of

each sensor node is critical for WSNs.

• Balanced energy usage— While minimizing the energy consumption of individual sensor nodes is important,

the energy status of the entire network should also be of the same order. If certain nodes have much higher

workload than others, these nodes will drain off their limited energy rapidly and adversely impact the lifetime

of the overall system. The workload of sensors should be balanced in order to achieve longer system lifetime.

• Low computation and communication overhead— The resource limitations imposed by typical node hard-

ware call for simple protocols that require minimal processing and have a small memory footprint. The extra

computation and communication introduced by the energy optimization schemes must also be kept low. Other-

wise, energy required to perform the optimization schemes may outweigh the benefits.

1.3.2 State of the Research

There already exist some efforts to overcome the challengesin reducing energy consumption and extending

system lifetime in wireless sensor networks. The proposed optimization approaches can be divided into three different

categories:

1. Low power sensor architecture[47, 51, 62]: As a first step towards incorporating energy awareness into the

network, it is necessary to develop hardware/software design methodologies and system architectures that enable

energy-aware design and operation of individual sensor nodes in the network.

2. Energy-efficient communication[59, 94, 49, 86]: While the optimization of individual sensor nodes reduces en-

ergy consumption, it is important for the communication between nodes to be conducted in an energy-efficient

manner as well. Since the wireless transmission of data accounts for a major portion of the total energy con-

sumption, optimization schemes that take into account the effect of inter-node communication yield significantly

higher energy savings. Furthermore, incorporating energyoptimization into the communication process enables

the diffusion of energy awareness from an individual sensornode to a group of communicating nodes, thereby

enhancing the lifetime of entire regions of the network.

3. Lifetime-aware energy management[63, 32, 13]: Incorporating energy awareness into individual nodes and

6

groups of communicating nodes alone does not solve the energy problem in sensor networks. The network as a

whole should be energy aware, for which the network-level global decisions should be energy aware.

Some state-of-the-art optimization schemes will be presented in Chapter 2.

1.4 Contributions and Scope of this Thesis

The previous sections have discussed the main challenges inwireless sensor networks and the need for

energy optimization schemes. The requirements of energy optimization schemes are (1) node’s individual energy

consumption has to be low to adapt to the limited energy source, (2) the workload of nodes should be kept of the

same order for balanced energy usage, and (3) the computation and communication overhead must be low to meet the

nodes’ resource limitations. Existing strategies are usually tightly coupled with network protocols and other system

functionality, and only provide point solutions which are insufficient for these highly energy-constrained systems.

Energy optimization, in the case of sensor networks, is muchmore complex, since it involves not only reducing the

energy consumption of a single sensor node but also maximizing the lifetime of an entire network. The network

lifetime can be maximized only by incorporating energy awareness into every stage of wireless sensor network design

and operation, thus empowering the system with the ability to make dynamic tradeoffs between energy consumption,

system performance, and operational fidelity.

The thesis focuses on energy optimization for wireless sensor networks from various perspectives. It con-

tributes to the advancement of energy optimization in the following two major thrusts:

1. Exploit overhearing effect to improve aggregation efficiency and network energy managementOwing to

the broadcast nature of the wireless channel, many nodes in the vicinity of a sender node may overhear its packet

transmissions even if they are not the intended recipients of these transmissions. This redundant reception results in

unnecessary expenditure of battery energy of the recipients. Especially in dense sensor networks, overhearing costs

can constitute a significant fraction of the total energy consumption.

Overhearing problem is common in wireless sensor networks.Although several WSN MAC protocols have

been proposed using short control packets to avoid overhearing long data packets, overhearing the control packets still

consumes considerable overhead energy. Since overhearingis difficult to avoid and sometimes necessary, we propose

distributed energy optimization schemes which exploit theoverhearing effect as an approach to gather the required

information.

Adaptive Aggregation Tree (AAT)is proposed to dynamically transform the routing tree, using easily-

obtained overheard information, to improve the efficiency of data aggregation. Based on the simple shortest-path

tree, AAT allows each node to adaptively choose a new parent if it appears to provide better opportunities for aggre-

gation. The local adaptivity of AAT successfully reduces the number of message transmissions and achieves a 20%

energy reduction, compared to the shortest-path tree whereaggregation occurs opportunistically.

Neighborhood-Aware Density Control (NADC)exploits the overheard information to reduce the unnecessary

overhearing energy consumption along routing paths. In NADC, nodes observe their neighborhood and dynamically

adapt their participation in the multihop network topology. By reducing the node density near the routing paths while

7

keeping the nodes involved in packet generation or forwarding in the active state, the overhearing waste can be reduced

without dramatically increasing the delay of event delivery.

2. Extend system lifetime through load balancing Due to the multihop routing nature of sensor networks, the

nodes along the routing paths tend to have heavier workload and deplete their energy sources faster than other nodes.

Especially in densely deployed sensor networks, the overhearing cost may aggravate the unbalanced energy usage

among sensor nodes. Such unbalanced energy usage leads to a premature loss of connectivity in the network and

negatively impacts the system lifetime.

In this thesis, the unbalanced energy usage problem is attacked from two perspectives. In the network level,

NADC reduces the unnecessary overhearing along routing paths and alleviates the unbalanced energy usage among

sensor nodes. In the application level, we proposeZone-Repartitioning (Z-R)for load balancing in data-centric storage

systems. Z-R reduces the energy consumption of certain hot-spots by distributing their communication load to other

nodes when the event frequency of certain areas is much higher than the others.

The remaining parts of the thesis elaborate on these contributions and are organized as follows. Chap-

ter 2 presents some state-of-the-art optimization schemes. Chapter 3 introduces Adaptive Aggregation Tree, which

improves the aggregation efficiency by dynamically transforming the structure of routing trees. The result shows

that AAT reduces the traffic in the network as well as the totalenergy consumption. In chapter 4, we illustrate

Neighborhood-Aware Density Control, a network-level optimization scheme that extends the system lifetime by re-

ducing the unnecessary overhearing along routing paths andalleviating the unbalanced energy usage among sensor

nodes. Chapter 5 describes Zone-Repartitioning, an application-level optimization scheme that balances the workload

among sensor nodes in data-centric storage systems.

8

Chapter 2

Energy Optimization Schemes in WSN

2.1 Low Power Sensor Architecture

Energy consumption in an arbitrary sensor node has in general the following components depending on the

operations performed within the node:

1. Sensing energy: Sensing energy must be dissipated in order to activate sensing circuitry and gather data from the

environment. The magnitude of this energy depends on the task that is assigned to the sensor. Different sensors

require different levels of energy during operation.

2. Communication energy: A node consumes communication energy while sending or forwarding data packets to

the base station. The communication energy includes transmission energy and receiving energy.

3. Computation energy: To operate the sensor node, the sensor’s processor/microcontroller must be activated.

Moreover, whenever data aggregation is performed, additional computations must be realized. Compared to the

previous items, computation energy is usually relatively low.

The low power design of sensor nodes is one of the most crucialaspects of sensor network energy optimization.

Ultimately, it is the sensor node hardware that consumes theenergy, so if the node itself is not energy-efficient,

no amount of higher layer optimization will yield desired results. While the sensing energy is dependent on the

sensing requirements in different applications, various approaches have been proposed to reduce the computation and

communication energy consumption.

2.1.1 Power-Aware Computing

Dynamic Power Management Dynamic power management (DPM) [8] is an effective tool in reducing system

power consumption without significantly degrading performance. The basic idea is to shut down devices when not

9

needed and wake them up when necessary. DPM, in general, is not a trivial problem. If the energy and performance

overheads in sleep state transition were negligible, then asimple greedy algorithm that makes the system enter the

deepest sleep state when idling would be perfect. However, in reality, sleep-state transitioning has the overhead of

storing processor state and turning off power. Waking up also takes a finite amount of time. Therefore, implementing

the correct policy for sleep-state transitioning is critical for DPM success.

Dynamic Voltage Scaling While shutdown techniques can yield substantial energy savings in idle system states,

additional energy savings are possible by optimizing the sensor node performance in the active state. Dynamic voltage

scaling (DVS) [96] is a popular approach to power and energy reduction in microprocessors. Many modern processors

are designed to operate at different voltage and frequency settings as an effective way to mange power usage. As

the processor frequency is reduced, the supply voltage can also be reduced. As shown by the equations below, the

reduction in frequency combined with a quadratic reductionfrom the supply voltage results in an approximately cubic

reduction of power consumption. However, with educed frequency, the time to complete the task increases, leading

to an overall quadratic reduction in the energy required to complete a task. DVS is therefore an effective method to

reduce the energy consumption of a processor.

Delay =1

f=

CsV dd

Idsat

∝ V dd

(V dd − V th)1.3

Power ∝ fV dd2

Energy ∝ V dd2

In most current processor designs, the voltage range is limited from full V dd to approximately halfV dd at

most. Research has indicated that extending the operating voltage range, even to subthreshold voltages, may improve

the energy efficiency for many processor designs. However, significant design effort is required to accommodate

operation over a wide range of voltage levels. Furthermore,the landscape for microarchitectural energy optimization

dramatically changes in the subthreshold domain, which limits the possible maximum savings DVS can approach [85].

Subthreshold-Voltage Circuits Many sensor network applications have low performance requirements and the pro-

cessors only execute low-performance tasks intermittently. Superthreshold (V dd > V th) circuit implementations are

sometimes too fast and energy-hungry for these applications [51]. Since optimal supply voltage to minimize energy

typically occurs in the subthreshold (V dd < V th) region with reduced performance, several subthreshold micropro-

cessors have been proposed trying to operate from energy scavenged from the ambient environment.

It places demands on all levels of design for a microprocessor to operate at power levels low enough to en-

able the use of an energy harvesting source [26]. A minimum energy design methodology that includes energy-aware

architectures and subthreshold circuits are required in order to function within the extremely low power requirements.

Although some design challenges remain for subthreshold circuits, they provide significant reductions in energy, mak-

ing them ideal for a lot of sensor network applications.

10

2.1.2 Energy-Aware Software

The OS is ideally poised to implement shutdown-based and DVS-based power management policies, since

it has global knowledge of the performance and fidelity requirements of all the applications and can directly control

the underlying hardware resources, fine tuning the available performance-energy control knobs. At the core of the OS

is a task scheduler, which is responsible for scheduling a given set of tasks to run on the system while ensuring that

timing constraints are satisfied. System lifetime can be increased considerably by incorporating energy awareness into

the task scheduling process.

TinyOS [28] is an operating system specifically designed to address the needs of wireless sensor networks.

It is based on an event driven execution engine that simultaneously provides efficiency and fine-grained concurrency.

TinyOS’s event based communication is encapsulated in a component model that allows state-machine based compo-

nents to be efficiently composed together into application-specific configurations. The TinyOS communication system

is based on the Active Messages communication paradigm taken from high-performance computing environments.

When data is received from the network, message-specific handlers are automatically invoked.

2.1.3 Energy-Efficient Radio Transceiver

While power management of embedded processors has been studied extensively, incorporating power aware-

ness into radio subsystems has remained relatively unexplored. Energy-efficient radio transceiver is extremely impor-

tant since wireless communication is a major power consumerduring system operation. As shown in Figure 2.1, the

power consumed by radio has two main components[25]:

1. An RF component that depends on the transmission distanceand modulation parameters.

2. An electronics component that accounts for the power consumed by the circuitry that performs frequency syn-

thesis, filtering, up-converting, etc.

Figure 2.1: Components of energy consumption in radio communication [25]

New methodologies and various energy-efficient transceiver designs have been proposed to fulfill the re-

quirements of wireless sensor networks [62, 29, 54]. Compared with other wireless systems, although the radio of a

wireless sensor has reduced data rate and shorter communication distance, the transceiver usually demands extremely

low power consumption, very high levels of integration, anda fast turn-on time for both the transmitter and receiver.

11

Figure 2.2: Radio energy per bit as a function of packet size and modulation level, using typical radio parameters forsensor networks [63].

2.2 Energy-Efficient Communication

While power management of individual sensor nodes reduces energy consumption, it is important for the

communication between nodes to be conducted in an energy efficient manner as well. Since the wireless communica-

tion of data accounts for a major portion of the total energy consumption, power management decisions that take into

account the effect of inter-node communication yield significantly higher energy savings.

2.2.1 Modulation Schemes

Besides the hardware architecture itself, the specific radio technology used in the wireless link between

sensor nodes plays an important role in energy consideration. The choice of modulation scheme greatly influences the

overall energy versus fidelity and latency tradeoff that is inherent to a wireless communication link. Fig. 2.2 plots the

communication per bit as a function of the packet size and themodulation level. The optimal modulation level for each

packet size is relatively high, close to 16-QAM (4 bits per symbol) for the radio parameters used. Higher modulation

levels might be unrealistic in resource constrained sensornodes. In these scenarios, a practical guideline for saving

energy is to transmit as fast as possible at the optimal setting, and shut off the transmitter during the idle period [86].

However, for reasons of peak-throughput, higher modulation levels than the optimal one may need to be

provided. Modulation scaling [72] is proposed to adaptively change the modulation level to lower the overall energy

consumption. When the instantaneous traffic load is lower than the peak value, transmissions can be slowed down,

possibly all the way to the optimal operating point. This technique introduces the notion of energy awareness in

communications by dynamically adapting the modulation level to match the instantaneous traffic load as part of the

radio power management.

12

2.2.2 Energy-Efficient MAC Protocols

Medium-access control (MAC) protocols for wireless networks manage the usage of the radio interface to

ensure efficient utilization of the shared bandwidth. MAC protocols designed for WSNs have an additional goal of

managing radio activity to conserve energy. While traditional MAC protocols must balance throughput, delay, and

fairness concerns, WSN MAC protocols place an emphasis on energy efficiency as well. There are several aspects of

a traditional MAC protocol that have a negative impact on wireless sensor networks, including:

• Collisions— When a transmitted packet is corrupted due to a collision, it has to be discarded. The follow-on

retransmission increases the energy consumption.

• Overhearing— Nodes receive packets that are destined to other nodes.

• Control packets overhead— Sending and receiving control packets consumes extra energy. Moreover, as more

nodes fail in the network, more control messages are required to reconfigure the system, resulting in more energy

consumption.

• Idle listening— Waiting to receive anticipated traffic that is never sent.

WSN MAC protocols generally reduce energy consumption by putting radios to a low-power sleep mode, either

periodically or whenever possible when a node is neither receiving nor transmitting. An important consequence is

that a node needs to be aware of its neighbors’ sleep/active schedules, since sending a message is only effective when

the destination node is awake. An obvious solution is to haveall nodes synchronize on one global schedule, so no

separate neighbor state is required, which maps well onto the resource limitations of typical sensor nodes. However,

grouping communication into small (active) periods requires precise time synchronization and may increase the chance

of collisions, so other forms of organization have been proposed.

The MAC protocols for sensor networks can generally be divided into two categories:schedule-basedand

contention-based. In schedule-based protocols, energy waste due to overhearing, collision, idle listening, and transi-

tions between different states can be minimized. In addition, schedule-based medium arbitration can enhance delay

predictability and limit packet drops due to interference and buffer overflow. However, the problem of scheduling

access to the medium is NP-hard [75, 65], making the scalability of schedule-based MAC schemes a major concern.

Distributed schedule-based medium arbitration typicallyintroduces excessive overhead, and maintaining clock syn-

chronization among nodes is essential to enforce the schedule – this is a non-trivial problem for resource-constrained

sensor nodes.

Contention-based protocols do not divide and pre-allocatethe channel into sub-channels for each node to

use. Instead, a common channel is shared by all nodes, and it is allocated on-demand. A contention mechanism

is employed to decide which node has the right to access the channel. These protocols are not as energy-efficient

as schedule-based protocols, but they have several advantages. First, they scale more easily across changes in node

density or traffic load. Second, they are more flexible as topologies change. Finally, they do not require fine-grained

time synchronization. Due to the highly dynamic and untethered nature of sensor networks, these advantages make

contention-based protocols the preferred choice, and theyhave been widely adopted.

Energy-efficiency is obtained in MAC protocols essentiallyby turning off the radio to sleep mode whenever

possible, to save on radio power consumption. These protocols can be categorized into the following two classes:

13

Asynchronous Sleep Techniques In these techniques nodes normally keep their radios in sleep mode as a default,

waking up briefly only to check for traffic or to send/receive messages. Nodes need to be able to sleep to save energy

when they do not have any communication activity and be awaketo participate in any necessary communications.

A hardware solution is to equip each sensor node with two radios [79, 73]. The primary radio is the main

data radio, which remains asleep by default. The secondary radio is a low-power wake-up radio used for signalling. If

the wake-up radio of a node receives a wake-up signal from another node, it responds by waking up the primary radio

to begin receiving. This ensures that the primary radio is active only when the node has data to send or receive. Since

the wake-up radio need not do much sophisticated signal processing, it can be designed to be extremely low power. A

tradeoff, however, is that all nodes in the broadcast range of the transmitting node may be woken up.

Another solution is to use preamble sampling or low-power listening, in which the receivers periodically

wake-up to sense the channel. If no activity is found, they goback to sleep. If a node wishes to transmit, it sends a

preamble signal prior to packet transmission. Upon detecting such a preamble, the receiving node will change to a

receiving mode. An implementation of this class of protocols is B-MAC [59] protocol. It provides a limited set of core

functionality and an interface that allows the core components to be tuned and configured depending on higher-layer

needs. The core B-MAC consists of the following features:

• Low-power listening (LPL), which implements the preamble-based wake-up to permit nodes to have sleep as

the default mode, helping to conserve energy. Different channel sampling durations and preamble durations can

be selected by the higher layers.

• Clear channel assessment (CCA), which determines whether the channel is busy or not by examining multiple

adjacent samples and using an appropriate outlier detection technique.

• Acknowledgements (ACK): If acknowledgements are enabled,a response is sent immediately after receiving

any unicast packet.

A desirable feature of B-MAC is that it is designed in a modular fashion and provides core functionality that enables

easy implementation of other more complex MAC protocols over B-MAC. For example, channel reservation signals

for RTS/CTS (Request to Send / Clear to Send) can be implemented above B-MAC using its control interfaces, but are

not part of the core B-MAC itself.

Low-power listening/preamble sampling has one potential shortcoming: the long preamble that the transmit-

ter needs to send can cause throughput reduction and energy waste. The WiseMAC [22] protocol builds upon preamble

sampling to correct this deficiency. Using additional contents of ACK packets, each node learns the periodic sampling

times of its neighboring nodes, and use this information to send a shorter wake-up preamble at just the right time.

Sleep-Scheduled TechniquesA typical protocol of this class of techniques is Sensor-MAC(S-MAC) [94]. Its basic

scheme is to put all sensor nodes into a low-duty-cycle mode –listen and sleep periodically. In S-MAC, sensor nodes

exchange and coordinate their sleep schedules periodically rather than randomly sleep on their own. To reduce control

overhead and simplify broadcasting, S-MAC encourages neighboring nodes to choose the same schedule, but it is not a

requirement. When sensor nodes are listening, they follow acontention rule to access the medium, which is similar to

the IEEE 802.11 DCF [19]. The duty cycle of this listen-sleepschedule provides for a guaranteed reduction in energy

14

consumption. However, low-duty-cycle operation reduces energy consumption at the cost of increased latency, since

a node can only start sending when the intended receiver is listening.

T-MAC [84] extends S-MAC by dynamically adjusting the duration between sleep intervals in which sensors

are awake based on communication of nearby neighbors. In order to reduce number for switching, T-MAC introduced

additional overhead control packets to prevent nodes from early sleep switching. D-MAC [42] addresses the latency

overhead of S-MAC for the convergecast communication pattern, be staggering receive/send slots according to the

level in the tree. It uses CSMA with ACKs to arbitrate betweenchildren, and schedules overflow slots whenever a

message is received.

Z-MAC [68] is a hybrid scheme that starts off as CSMA but switches to TDMA when the load increases.

Nodes run a distributed slot selection algorithm and the owner of a slot gets precedence by using as small random

back-off value. Since all nodes must listen to all slots, Z-MAC builds on LPL for energy efficiency.

2.2.3 Energy-Reliability Tradeoff

Reliable delivery of data is a classical design goal for all network infrastructures. Guaranteed packet delivery

is ensured by the careful selection of error free links, avoidance of overloaded nodes, and the detection and the recovery

from packet drops. There is usually a tradeoff between the control traffic overhead and the level of reliability.

Implicit Acknowledgement Due to environmental interference and packet collisions, link failures and packet losses

are inevitable in sensor networks. Although sensor networks may indeed tolerate a certain amount of data loss without

significantly affecting the aggregate, for the system to scale, the fraction of delivered packets should remain finite and

representative of the original pool. That means a certain degree of reliability is needed, especially when hop count

increases, because a single lost packet may result in loss ofa large amount of aggregated data.

A simple solution to improve packet delivery is to use explicit acknowledgement packets or signals. If the

sender does not receive an ack from the receiver within a timeout period, it assumes that either the data packet or the ack

is lost, and sends the data packet again. This solution is intuitive, and has proved to be robust in practice. However,

the extra energy required to send and receive the acks makes this scheme unattractive to sensor networks.Implicit

acknowledgement[9] reduces this extra energy consumption by embedding the acknowledgement into regular data

packets. When a node receives packets from its upstream nodes, instead of sending back explicit acknowledgement

packets, the acks are piggybacked on the aggregated data packet destined to the next hop. Based on the broadcasting

nature of wireless links, the upstream nodes can usually overhear this packet and get the acks.

Adaptive Error Correction Adaptive error correction schemes were proposed to reduce energy consumption while

maintaining the bit error rate (BER) specifications of the user [101, 39, 71]. For a given BER requirement, error

control schemes reduce the transmit power required to send apacket, at the cost of additional processing power at

the transmitter and receiver. This is especially useful forlong-distance transmissions to gateway nodes, which involve

large transmit power. Furthermore, the use of a good error control scheme minimizes the number of times a packet

retransmissions, thus reducing the power consumed at the transmitter as well as the receiver.

15

2.3 Lifetime-Aware Energy Management

Incorporating energy awareness into individual nodes and pairs of communicating nodes alone does not

solve the energy problem in sensor networks. There is significant research activity to minimize energy dissipation at

the system level to lengthen battery lifetimes for WSN applications.

2.3.1 In-Network Processing

Many applications in WSNs require processing of the raw datacollected by spatially distributed sensors.

The tradeoffs between communication and computation energy have shown thatin-network processing[44, 37] of the

sensed data is more energy efficient than transferring the raw data to the powerful base station for processing. With

in-network processing, data messages from neighboring nodes are combined into a single message on their way to the

base station. Depending on the type of data being collected,the processing can be simple (e.g., send the maximum

of all incoming data) or complex (e.g., compute a compressedor hashed version of incoming data). Even simply

concatenating individual data messages into a single, larger message can result in energy savings, because the startup

cost of a message transmission can be significant. The details of in-network processing and its related techniques will

be discussed in Chapter 3.

2.3.2 Data-Centric Storage

Current industrial and scientific uses of wireless sensor networks can be classified broadly along two di-

mensions: queries onlive data and queries onhistorical data. In live data querying, sensor samples are useful within

a small windows of time after they have been acquired. Querying historical data is required for applications that

need to mine sensor logs to detect unusual patterns, analyzehistorical trends, and develop better models of particular

events. While a large class of energy-efficient techniques have been proposed for querying live data, the management

of historical sensor data also requires energy-efficient solutions.

There are two models for designing such historical data querying systems. The first model treats sensor

data as a continuous stream that is aggregated within the network and then transmitted and archived outside the sensor

network. Once the data is collected, they can be stored in a traditional database, and queried using standard techniques.

While such a model is easy to deploy, these deployments can beshort-lived when high data rate sensors (e.g., camera,

acoustic, or vibration sensors) are used, since the data communication requirements overwhelm the available energy

resources.

The second model is to view the sensor network as a database that supports archival query processing,

where queries are pushed inside the network, possibly all the way to the remote sensors that archive data locally. In

this model, only the queried results, which may be a small portion of the total data, have to be transmitted out of the

network. Because local storage is typically more energy-efficient compared to communication [21], this tradeoff has

the potential to be more energy-efficient for querying archived data. This data management model is referred to as

data-centric storage (DCS). DCS will be discussed in detailin Chapter 5.

16

2.3.3 Traffic Distribution

Routing protocols for WSNs have been extensively studied inthe last few years. Many routing, power

management, and data dissemination protocols have been specifically designed for WSNs where energy awareness

is an essential design issue. Routing protocols in WSNs might differ depending on the application and network

architecture, and various energy optimization approacheshave been proposed for these routing protocols.

Some of these approaches were proposed to select paths that minimizes the total energy consumption. How-

ever, such a strategy does not always maximize the network lifetime. Consider a target-tracking application. While

forwarding the gathered and processed data the base station, it is desirable to avoid routes through regions of the

network that are running low on energy resources, thus preserving them for future, possibly critical detection and

communication tasks. So it is in general undesirable to continuously forward traffic via the same path, even though it

minimizes the energy, up to the point where the nodes on that path are depleted of energy, and the network connec-

tivity is compromised. Therefore, some energy-aware routing protocols have been proposed to spread the load more

uniformly over the network while trying to minimize the total energy consumption [74, 13].

The basic idea of energy-aware routing is that to increase oflifetime of networks, it may be necessary to

use sub-optimal paths occasionally. This ensures that the optimal path does not get depleted and the network degrades

gracefully as a whole rather than getting partitioned. To achieve this, multiple paths are found between source and

destinations, and each path is assigned a probability of being chosen, depending on the energy metric. By employing

this mechanism, the nodes in the primary path do not deplete their energy resources through continual use of the same

route, hence prolonging their lifetime.

2.3.4 Topology Management

The core functionality of a wireless sensor network dependson having a network topology that satisfies two

important objectives: coverage and connectivity.Coveragepertains to the application-specific quality of information

obtained from the environment by the networked sensor nodes. Connectivitypertains to the network topology over

which information routing can take place.

The problem of constructing an initial topology is the problem of node deployment. There are two major

methodologies for deployment: (a) structured placement and (b) random scattering of nodes. Particularly for small-

medium-scale deployments, where there are equipment cost constraints and a well-specified set of desired sensor

locations, structured placements are desirable. In other applications involving large-scale deployments of thousands

of inexpensive nodes, such as surveillance of remote environments, a random scattering of nodes may be the most

flexible and convenient option. Nodes may be over deployed, with redundancy for reasons of robustness or ease of

future maintenance [12].

Beyond initial deployment of sensor nodes, there are many scenarios in whichtopology controlis desired.

Topology control can be defined as the process of configuring or reconfiguring a network’s topology through tunable

parameters after deployment. There are three major tunableparameters for topology control:

Node Mobility In WSNs consisting of mobile nodes, such as robotic sensor networks [7], both coverage and con-

nectivity can be adapted by moving the nodes accordingly.

17

Transmission Power Control If the deployment density is already sufficient to guaranteethe required level of

coverage, the connectivity properties of the network can beadjusted by tuning the transmission power of the constituent

nodes. Power control is quite a complex and challenging cross-layer issue. Increasing radio transmission power has a

number of interrelated consequences – some of these are positive, others negative:

• It can extend the communication range, increasing the number of communicating neighboring nodes and im-

proving connectivity in the form of availability of end-to-end paths.

• For existing neighbors, it can improve link quality (in the absence of other interfering traffic).

• It can induce additional interference that reduces capacity and introduces congestion.

• It can cause an increase in the energy expended, due to the higher transmitting power and increased overhearing

waste.

Many algorithms have been developed for variable power-based topology control [64, 55]. Power control techniques

must provide connectivity, while taking into account diverse factors, including interference minimization and energy

reduction.

Density Control Density control is an approach that controls the density of working sensors at a desirable level. It

ensures only a subset of sensor nodes operate in the active mode, while meeting the sensing coverage or connectivity

requirements. Other nodes stay in the sleep mode to conserveenergy. Density control mechanisms will be discussed

in detail in Chapter 4.

18

Chapter 3

Reduction of Communication Energy

Consumption

In order to achieve reasonable lifetimes, sensor networks need to conserve energy, and an important com-

ponent of energy usage in these systems is the wireless communication among sensor nodes [27]. Therefore, much

research is focused on reducing the total number of messagesin the network.

One particular approach is to usein-network data aggregation[44, 37], in which data messages from neigh-

boring nodes are combined into a single message on their way to the data collection node. Depending on the type of

data being collected, the aggregation can be simple (e.g., send the maximum of all incoming data) or complex (e.g.,

compute a compressed or hashed version of incoming data). Even simply concatenating individual data messages

into a single, larger message can result in energy savings, because the startup cost of a message transmission can be

significant.

A persistent sensor network query is initiated by asinknode, which then collects all of the data from other

sensors. Sensor nodes that have data to report are calledsourcenodes, and data messages are relayed to the sink using

the network’s routing protocol. Nodes that transmit the data on behalf of source nodes, but which do not generate

data themselves, areforwardingnodes. The “persistent” nature of the query means that source nodes are expected to

periodically send event messages until the query expires oris cancelled.

Without aggregation, every source sends a data message to the sink, and forwarding nodes may relay mes-

sages from several sources. If aggregation is allowed, a forwarding node mayopportunisticallynotice that it is on

the routing path between multiple source nodes and the sink.It can then start combining messages from those nodes.

Fig. 3.1 shows a simple routing tree example.

The advantage of this opportunistic style of aggregation isits simplicity. Each node uses its regular routing

path to the sink, and aggregation occurs whenever a link is shared among two or more of those paths. The disadvantage

is that the resulting aggregation tree is usually not optimal. If the nodes had knowledge of the global network topology,

19

they could choose a routing tree that maximizes aggregationand minimizes the total number of messages.

We discuss such trees in Section 3.1, but for now we will just say that selecting an optimal tree would be very

expensive, since it requires the exchange of topology information among nodes. Furthermore, the number of source

nodes and their locations are usually unpredictable and highly dynamic. The expense of re-calculating an optimal tree

for each new set of source nodes would quickly swamp the benefit gained by more efficient aggregation.

The goal is to adaptively and dynamically transform a simplerouting tree, using easily-obtained local infor-

mation, to improve the efficiency of aggregation. The main idea is that a node can acquire neighborhood information

by hearing beacon packets or overhearing messages transmitted in the same locality. By observing these messages,

the node can determine whether it can change its route to increase shared links with other active paths. The details are

described in Section 3.2, along with implementation issuesin Section 3.3.

Our results in Section 4.4 show that this local adaptivity achieves significant energy reduction, compared

to the opportunistic approach discussed above. This approach is especially beneficial to systems that monitor event-

driven phenomena, such as traffic monitoring or wildlife tracking systems, where the source nodes change over time,

favoring a simple adaptive scheme.

3.1 Background

3.1.1 Query Processing in Sensor Networks

In a sensor network, the sensors typically collect and transmit information periodically, and have to carefully

manage limited power to ensure that essential information is collected and reported in a timely fashion. Several

sensor query processing architectures, such as TinyDB [45]and Cougar [93], have been proposed to facilitate fast

development of such data collection applications. In thesearchitectures, users compose queries specifying the data

they wish to collect and how they wish to combine, transform,and summarize it. In such queries, extracting all data

over all time from all sensors will consume large amounts of energy as each individual sensor’s data is independently

routed through the network. There are several approaches, such aspacket merging, grouping, anddata caching, to

solve the problem [92], anddata aggregationhas been proposed as a particularly useful paradigm to reduce network

traffic.

In a sensor query processing system, a communication architecture is required for query dissemination and

result collection. A routing tree is a communication primitive rooted at either the base station or a storage point. The

Figure 3.1: A simple routing tree, from source nodes (grey) to sink (black). Messages are received and retransmittedby forwarding nodes (white). Dotted lines are links that areavailable, but are not in the routing tree. Aggregationreduces message count from 15 to 10.

20

root is the point from which the routing tree will be built, and upon which aggregated data will converge. The root

first broadcasts a query message, asking sensors to organizeinto a routing tree; in that message it specifies its own ID

and itslevel(distance from the root), which is zero. Any sensor that hears this message assigns its own level to be the

level in the message plus one, if its current level is not already less than or equal to the level in the message. It also

chooses the sender of the message as itsparent, through which it will route messages to the root. These sensors then

rebroadcast the routing message, inserting their own IDs and levels. The routing message floods the whole network

in this fashion, until all nodes have been assigned a level and a parent. Nodes that hear multiple parents choose one

arbitrarily or based on certain rules. When a sensor wishes to send a message to the root, it sends the message to its

parent, which in turn forwards the message to its parent, andso on, eventually reaching the root.

A node can generally choose a parent from several possible candidate nodes; a simple approach is to choose

the ancestor node at the lowest level or with the shortest delay. In practice, however, choosing a proper parent is quite

important in terms of communication and data collection efficiency.

3.1.2 Data aggregation

Data aggregation is a way to combine data from different sources. The simplest data aggregation function

is duplicate suppression- if multiple sources all send the same data and only one is required, these data items can be

aggregated together. Other aggregation functions could bemin, count, average, or even a user-defined function with

multiple inputs, as long as the function isdecomposable. A function is decomposable if it can be computed by another

function as follows:

f(v1, v2, ..., vn) = g(f(v1, ..., vk), f(vk+1, ...vn))

Using decomposable functions, the value of the aggregate function can be computed for disjoint subsets, and

these values can be used to compute the aggregate of the wholeset, using the merging functiong. In this approach,

after the queries are distributed across the network, aggregate results are sent back to the querier over a spanning tree,

with each sensor combining its own data with results received from its children.

If there are no failures, this in-network aggregation technique is both effective and energy-efficient. How-

ever, a high loss rate is inevitable on wireless links, and this effect accumulates quickly as the number of hops increases.

For example, when loss rate is 5% per hop, the loss rate after 10 hops becomes 40%. The effect becomes more severe

when aggregation is used because a single packet loss results in the loss of all aggregated data in that packet. Thus a

reliable transmission scheme is critical for efficient dataaggregation.

3.1.3 Aggregation Trees

In this section, we introduce several aggregation trees andtheir characteristics.

Shortest-Path Tree

Shortest-path tree (SPT) can be easily constructed during the query dissemination process. As in our earlier

discussion, every node maintains a list of its neighbors andtheir levels after the query dissemination process, and the

21

nodes on the list with lower level can be the candidate nodes for parent selection. If delay is used as the criterion of

parent selection (i.e. choose a candidate node which has thelowest level), the resulting tree will be a shortest-path

tree. There is nearly no additional computation or communication required for the construction of SPT, and hence it

is the easiest way to form an aggregation tree.

In this data aggregation scheme, each source node sends its information to the sink node along the shortest

path. Where these paths overlap for different source nodes,they are combined to form the aggregation tree. Since

the primary goal of this structure is to minimize delay, SPT does not necessarily maximize the degree of aggregation

possible in the network. More opportunities for aggregation may occur when non-shortest paths are chosen to increase

overlap.

Minimal Steiner Tree

If the primary goal is to minimize the number of messages sent, then we can try to construct a tree that

minimizes the total number of edges. Finding such a tree in a fully-connected graph is a well-known graph theory

problem.

Given a graph with non-negative edge lengths and a selected subset of vertices, theSteiner problem in

networks (SPN)[69] is to find a tree of minimum length that spans the selectedvertices. LetG = (V, E, cost) be a

graph with a non-negative cost function on its edges. Any tree inG spanning a given set ofterminalsS ⊆ V is called

a Steiner tree, and the cost of the tree is defined to be the sum of its edge costs. Note that a Steiner tree may contain

non-terminal vertices, referred to asSteiner points. Fig. 3.2 shows a minimal Steiner tree example.

Most versions of SPN are NP-complete, and finding an explicitsolution in a large network is prohibitively

expensive. A number of heuristics have been reviewed extensively. KMB [35] is an approximate algorithm which

constructs a solution for the Steiner tree problem within a factor of 2 of the optimum. The KMB heuristic provides a

good scheme for heuristic development for the Steiner tree problem, since it gives an initial guarantee of approximate

optimality. IKMB (Iterated KMB) [3] is a heuristic that uses the KMB algorithmiteratively to find the minimal Steiner

tree. Starting with an initially empty set of Steiner points, S = ∅, IKMB repeatedly finds Steiner point candidates that

reduce the overall cost, and includes them into the growing set of Steiner pointsS. Since IKMB provides a technique

to effectively navigate through the very large solution space of possible Steiner tree candidates, it is one of the best

known heuristics to find the minimal Steiner tree.

non-terminalterminal

Figure 3.2: A minimal Steiner tree can use non-terminal (white) nodes to connect all terminal (black) nodes using theminimal number of edges. The minimal Steiner tree is indicated by heavy line segments.

22

Greedy Incremental Tree

The Greedy Incremental Tree (GIT) [82] is an algorithm originally proposed to solve the Steiner tree prob-

lem. It is also adapted indirected diffusion[30] to increase the amount of path sharing in sensor networks [36], in

order to increase the opportunity for aggregation. Directed diffusion establishes paths usingpath reinforcement. Ex-

ploratory messages are initially and repeatedly flooded throughout the network, such that the nodes in the network can

have some empirical information about their neighbors. Thus a node may locally decide (based on perceived traffic

characteristics) to draw data from one neighbor in preference to other neighbors.

In the GIT heuristic, each exploratory sample contains an energy cost for delivering this sample from the

source to the current node, and the source node closest to theexisting tree can be found through reinforcement by a

greedy algorithm. Therefore the aggregation tree can be built sequentially. At the first step the tree consists of only

the shortest path between the sink and the nearest source. Ateach step after that, the next source closest to the current

tree is connected to the tree. If all the source nodes are connected, the optimal data aggregation tree can be formed in

polynomial time with respect to the number of nodes.

Discussion

While the Steiner Tree has the minimum number of edges, and therefore the minimum number of message

transmissions, it is difficult to compute. Heuristics such as IKMB reduce the computation time, but they still require

global knowledge of the routing graph, which is impracticalin a sensor network.

In addition, the Steiner/IKMB Tree tends to dramatically increase the depth of the spanning tree, relative to

the Shortest-Path Tree. In other words, to minimize the number of edges, the path between some source nodes and the

sink can become much longer than necessary. While this does maximize the opportunities for aggregation, a long path

means a long delay between the detection of an event and its arrival at the sink node.

GIT has been shown to be able to provide some energy gains withdata aggregation. However, it has some

features that may limit its use in sensor networks. First, the number of steps required to construct the tree is propor-

tional to the number of source nodes, which can be quite large. Moreover, GIT has to be reconstructed whenever the

set of source nodes changes, which prohibits its usage in environments where the number and location of source nodes

are highly dynamic.

In the next section, we propose a method to adaptively optimize the aggregation tree to improve aggregation

without significantly increasing depth. The adaptation algorithm is completely decentralized—each node observes

traffic in its local neighborhood, and may change its parent when it believes that the change will reduce the number

of edges in the tree. The additional cost is small because it only relies on overhearing messages in the neighborhood.

Our results show that this approach yields much of the benefitof the Steiner/IKMB Tree with little added overhead.

23

Figure 3.3: Snooping on neighborhood traffic. Node A overhears the message from X to Y, and can change its parentfrom B to Y, to improve aggregation.

Algorithm 1 Procedure of parent selectionParent = Self.parent

foreach Node in NeighborTable of Self

ifNode.type==SOURCE and Parent.type==(FORWARDER or IDLE)then

ifNode.level<Self.level or (Node.level==Self.level and Node.id<Self.id)then

SET Parent to Node

end if

end if

ifNode.type==Parent.typethen

ifNode.level<Parent.levelthen

SET Parent to Node

end if

end if

end loop

3.2 Adaptive Aggregation Tree

3.2.1 Overview

The adaptive aggregation tree (AAT) starts with the easily-constructed shortest-path tree, and then allows

each node to choose a new parent node if it appears to provide better opportunities for aggregation.

Because of the broadcast nature of the wireless medium, a node can easily observe its neighborhood by

receiving periodic beacon packets or snooping on the channel and recording information about nodes from which it

receives packets. For example, Fig. 3.3 shows the communication range for Node A. The current routing tree has node

A sending to B, which is a forwarding node. Node X sends its events to Y, which is also a source node. Since Y is a

source node, it must send a message anyway, so it would be better for A to send its message to Y, where its data can

be aggregated with X’s and Y’s.

Based on the gathered information, every node maintains a list of parent candidates and their associated

state information. When a node has a packet to send, it chooses the most appropriate candidate in the list as its parent,

which becomes the next hop of the packet towards the sink. Thesame process repeats until the packet reaches the sink

node. Since this parent selection process is performed dynamically whenever there is a packet to send, this approach

can adaptively change the topology of aggregation according to different situations.

Algorithm 1 describes how a node selects its parent. To perform the algorithm, two pieces of information

are required for each neighbor node:level(depth in the shortest path tree) andtype(source or forwarding node). This

24

(a) SPT (b) AAT

Figure 3.4: An example SPT and its corresponding AAT. Sourcenodes are represented by red circles. In AAT, nodescan only select parents in the same level or one level up, which is loop-free if there are no loops in each level. Thelevel in AAT is actually the level in SPT, which is not changedduring the AAT parent switching process.

information can be easily acquired through packet overhearing or periodic beacon packets. The implementation details

will be discussed in Section 3.3.

There are two basic heuristics in the algorithm:

1. Choose source nodes before forwarding nodes: If the neighbor under consideration is a source and the current

parent is a forwarding node, then this neighbor is a good candidate.

2. Choose nodes closer to the sink: If the candidate node and the parent are of the same type (i.e. both forwarding

nodes or source nodes), the one closer to the sink is preferred.

Based on the heuristics, a node may choose a new parent node which is originally in its descendant tree, and hence

form a loop in the routing path. To prevent the formation of possible loops in our algorithm, the parent selection of a

node is restricted to neighbors which arenot farther away than itself. If a node and its parent candidate are in the same

level, we use node id as a tiebreaker – only candidates with smaller node id can be selected as its parent. Without the

tiebreaker, two nodes in the same level may choose each otheras their parents and form a routing loop.

Fig. 3.4 is an example of SPT and the corresponding AAT. SPT isloop-free because all nodes have parents

one level up. In AAT, nodes can select parents from nodes in the same level and nodes one level up. Since no downward

selection is allowed, the only possible loop formation is within one level. In our AAT implementation, we use node id

as a tie breaker. That means nodes in the same level have a total ordering in the priority of being parents (i.e. {source

with small id} > { source with large id} > { forwarder with small id} > { forwarder with large id}). Therefore, no loop

can be formed within each level, and that proves the loop-free property of AAT.

Since the loop prevention approach restricts the membership of parent candidates, it may also limit the

possible cost reduction of the aggregation tree. In following sections, we simulate the loop-free AAT first, then the

non-loop-free AAT by loosening the restrictions to allow some possible loops. Although most of the possible loops in

the non-loop-free AAT can be easily avoided through some simple rules, there is only marginal improvement in the

tree cost reduction compared to the loop-free AAT.

25

SPT IKMB AAT

SPT

cost=262 depth=12

IKMB

cost=132 depth=29

AAT

cost=159 depth=16

Figure 3.5: Three different aggregation trees for a sensor network. The network includes 1024 nodes with randomdisplacement from a320×320 grid. The green dotted lines represent the possible communication links. The dark linesrepresent paths used in the aggregation tree. The sink node is in the middle of the network, and 100 randomly-selectedsource nodes are shown as circles.

3.2.2 Preliminary Analysis

To evaluate the ultimate potential of AAT, we use an “ideal” network to exclude the environmental uncer-

tainties. In each network, 1024 nodes are evenly spread overa320× 320 grid with a spacing of 10 feet. Nodes are not

placed directly on the grid points, but are placed close to the grid points, with a random displacement within 5 feet.

One hundred nodes are chosen at random to be source nodes, anda node near the center of the grid is chosen to be

the sink node. The effective communication range of each node is set to 22 feet, which guarantees that every node is

able to communicate with at least one neighbor. Here we assume a perfect radio model, which means every node can

perfectly communicate with other nodes within the circularradio range without any interference.

We consider the cost of the tree to be the total number of linksused to transmit data from the sources to the

sink. This is optimistic, because it assumes that an aggregated data message is the same size as a message containing

a single event. This may or may not be true, depending on the aggregation function being used.

Fig. 3.5 shows the results of three different mechanisms forcomputing an aggregation tree, giving the same

randomly-chosen source nodes. The left diagram shows the shortest-path tree (SPT), in which each source simply

follows the shortest path to the sink. Aggregation occurs opportunistically, whenever two or more data messages are

sent to the same forwarding node. In this example, the cost ofSPT is 262, and its depth is 12. (In other words, the

longest path from any source to the sink is 12 hops.)

The middle diagram is the IKMB tree, which approximates the minimal Steiner Tree. The number of edges

drops significantly to 132, which is only 55% of the original cost. The depth of the tree, however, is 29, which is more

than twice the depth of SPT. To minimize the number of edges, IKMB prefers to route message through other source

nodes, even if they do not lie on the shortest path.

The right diagram shows the adaptive aggregation tree. To generate this tree, we started with SPT, and

then ran Algorithm 1 for nodes which have packets to send (i.e. the source and forwarding nodes). This was done

iteratively, until the topology remained stable. (In all ofthe networks that we modeled, this only required two or three

iterations of the algorithm.) The resulting cost is 159, which is 61% of the original SPT. As expected, this reduction is

not as much as the IKMB tree, but the depth of AAT is 16, only 33%higher than SPT. Therefore, AAT achieves 87%

of the cost reduction of IKMB, but with a much smaller increase in depth.

26

10

20

30

40

50

AAT

SPT

IKMB

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

10987654321

Normalized tree cost

Topology

Treedepth

AATIKMB

Figure 3.6: The tree depth of ten random networks, and the corresponding tree cost (normalized to the cost of SPT).

Fig. 3.6 shows the results for 10 randomly-generated networks, using the same parameters (1024 nodes, 100

sources). In each case, AAT achieves a substantial portion of the savings of IKMB, with only a small increase in depth.

The average reduction in cost for AAT is 34%, while the average depth increased by 43%. In contrast, IKMB reduces

cost by 48%, but the increase in depth varies considerably among different topologies, from 67% to 317%, with an

average of 149%.

3.2.3 Non-Loop-Free AAT

In the AAT parent switching algorithm, we applied some restrictions to guarantee loop-free while nodes

switching their parents. The restrictions, however, may sacrifice some potential reduction of the tree cost. Therefore,

we try to loosen the restrictions to see if the tree cost can befurther reduced.

While the loop-free AAT restricts nodes from choosing parents farther away than themselves, we simulate

the non-loop-free AAT by allowing a node to aggressively choose a neighbor node farther away as its parent. That

means a node may choose a node in its descendant tree that results in a cycle in the routing path. Since the loop

detection mechanism in such situation usually requires heavy weight protocols with inter-nodal coordination, we use

local loop trackingto avoid the formation of possible loops. In local loop tracking, each node identifies its neighboring

descendant nodes by tracing down the parent-child relationship in its neighbor table, and these neighboring descendant

nodes are ruled out being the parent candidates.

Although this loop avoidance approach can’t guarantee loop-freedom, it uses only local information and is

capable of avoiding all the possible loops within a node’s radio range. Fig. 3.7 shows the average tree cost and depth

of 10 simulation runs, with various number of source nodes. Here the non-loop-free AAT is referred to as AAT-nlf.

We can observe from the results that when the number of sourcenodes increases, the cost of AAT becomes

closer to the cost of IKMB, because each node has higher opportunity to hear from a source node and choose it as its

27

10

20

30

40

AAT

SPT

IKMB

AAT-nlf

0

100

200

300

400

500

600

40035030025020015010050

Averagetree cost

Number of sources

Averagetree depth

SPTIKMBAAT

AAT-nlf

Figure 3.7: The average tree cost and depth with different number source nodes (10 simulation runs each). Thedifference between AAT and AAT-nlf is not obvious, in terms of both cost and depth. When the number of sourcesincreases, the cost of AAT becomes very close to IKMB. The tree cost reduction of AAT against SPT is around 34%,independent of the number of source nodes. The depth increase of AAT over SPT increases gradually with the numberof sources, from 25% to 83%.

parent. When the source density keeps increasing, the cost of AAT becomes very close to the cost of IKMB. With

400 source nodes, the different between AAT and IKMB is less than 2%. The results also show that although AAT-nlf

has lower cost over AAT in all the simulated network settings, there is only marginal difference (always less than

3%). Since the loop detection/breaking mechanisms are necessary for AAT-nlf due to the possible loops (which can’t

be avoided by a single node), the introduced energy consumption and protocol complexity can easily outweigh the

advantages. Therefore, we consider only loop-free AAT in the following sections.

After demonstrating the significant benefit that AAT can approach in an ideal environment, the practical

network uncertainties and implementation details still need to be considered. In the next section, we first introduce

some related work and discuss the implementation issues of AAT, then more realistic simulation results will be shown

in Section 4.4.

3.2.4 Related Work

Zhuet al.[100] published an extensive comparison of SPT and DB-SMT, which is a depth-bounded minimal

Steiner tree generated by BSMA (bounded shortest multicastalgorithm). Their performance experiments show a

cost reduction of 20–40%, which is consistent with our analytical results. The reduction is non-trivial, especially in

systems whose lifetimes are measured in weeks or months. However, one of their major conclusions is that the energy

improvement in using correlation aware aggregation (e.g.,DB-SMT) is not significant under many network scenarios.

The main reason is the cost of constructing the improved aggregation tree, and the cost of changing it when the set of

source nodes changes.

28

In AAT, however, our adaptive scheme tries to minimize such overhead by using only local information. The

adaptive aggregation tree can approach the cost savings of the Steiner tree with very little overhead. In addition, it

automatically adapts to changes in the set of source nodes, since source messages are observed by other nodes in the

tree. This makes AAT a simple yet powerful mechanism for aggregation tree optimization.

An aggregation tree can be considered as the reverse of a multicast tree. Multicasting is a technique for

data routing in networks that allows multiple destinationsto be addressed simultaneously, and it is well known that

the determination of a minimum-cost multicast tree is a difficult problem, which can be modeled as the NP-complete

Steiner tree problem. Several heuristics have been proposed to minimize the cost of a multicast tree [53, 88], but there

are some difficulties in applying these heuristics to the construction of aggregation trees. These heuristics usually

require a comprehensive knowledge of all the destination nodes in advance, and take a lot of extra communication and

computation to build up the multicast tree. These drawbacksmake the multicast tree heuristics undesirable in dynamic

sensor network environments where the set of source nodes changes over time.

Zhanget al. [98] proposed an adaptive spanning tree based on MCBR (Message-initiated Constraint-Based

Routing). MCBR is a framework composed of the explicit specification of constraints for messages and a set of

QoS-aware meta-strategies. With the separation of routingspecifications from routing strategies, MCBR takes QoS

specifications into account by applying general-purpose meta-routing strategies. The adaptive spanning tree is con-

structed and maintained through a reinforcement learning core in each node, which estimates and updates the cost

from the current node to the destination. This makes the spanning tree adapt automatically to different routes when

network conditions change. With different routing metrics, the adaptive spanning tree can perform energy-aware load

balancing to increase lifetime, or congestion-aware routing to reduce latency and increase reliability.

Approaches to dynamically change the the aggregation tree structure have also been proposed in the MAC

layer. DAA (Data-Aware Anycast) [23] and DB-MAC (Delay-Bounded MAC) [4] modify the basic RTS/CTS mech-

anism to increase the aggregation opportunity. In these approaches, instead of assigning a specific destination (i.e.

next hop) while transmitting a data packet, a node broadcasts an RTS without specifying the destination node. All

the neighboring nodes which hear the RTS can contend for the medium access by responding with CTS packets, and

the winner becomes the next hop of the data packet. By assigning higher priorities (by having shorter delay before

responding with a CTS packet) to the nodes which also have data to send, the high priority nodes have higher chances

to win the contention and the data packets have more opportunity to be aggregated. While significantly reducing of

the total amount of transmissions, these MAC layer adaptation approaches still have some limitations. First, these ap-

proaches are tightly coupled with RTS/CTS mechanisms, which limit their applicability to different MAC protocols.

Second, the routing decision is made dynamically in the MAC layer, which makes it difficult to implement different

routing protocols or accommodate other optimization schemes. For example, some energy-aware routing protocols

require each node to switch among multiple parents for load-balancing purposes [74, 13]. Taking the load-balancing

optimization into consideration in the MAC layer will significantly complicate the design and implementation of MAC

protocols. Therefore, AAT is designed to perform the dynamic adaptation in the network layer. In the following sec-

tions, we will show that AAT can be easily implemented on various MAC protocols (e.g., B-MAC and S-MAC) and

work with different upper layer routing protocols (e.g., GPSR).

29

3.3 Implementation Issues

In the Section 3.2, we have demonstrated the significant potential benefit of AAT in an ideal environment.

However, the assumptions are unrealistic in real sensor networks. In this section, we take the practical environment

into consideration and discuss the implementation issues of AAT.

3.3.1 Acquisition of Neighbor Information

In the adaptive aggregation tree, each node maintains a neighbor table, and chooses its parent from the

candidate nodes in the table. To perform the parent selection, some neighbor information is required, includingid,

type, level, link quality, etc. A simple way to obtain the neighbor information is through broadcasting packets. Nodes

can periodically broadcast their own status so that every node in the network can have the necessary information of its

neighboring nodes. These periodic broadcasting packets, which are usually referred to asbeaconpackets, are indeed

necessary for some sensor network routing protocols, such as Directed Diffusion [30], GPSR [33], and MintRoute [89].

For such routing protocols, the extra information requiredfor AAT can be added into the original beacon packets, so

that nodes can have the necessary neighbor information to alter the routing path.

For protocols without beacon packets, the neighbor information can be acquired by exploiting the overhear-

ing effect. Overhearing is one of the most important components of energy waste in sensor networks [6]. It occurs

when a node receives packets that are destined to other nodes. Overhearing unnecessary traffic is a dominant factor

of energy waste when the traffic load is heavy and the node density is high, and therefore overhearing avoidance has

been an important issue in the design of MAC protocols. In next few paragraphs, we describe the relationship between

overhearing and MAC protocols, and show the applicability of our approach on different MAC protocols.

The MAC protocols for sensor networks can generally be divided into two categories:schedule-basedand

contention-based. In schedule-based protocols, such as TDMA and LEACH [25], there is nearly no overhearing

because the channel is divided into time slots, and only one node is allowed to transmit in each slot. However, this

approach has some disadvantages that may limit its use in sensor networks. Schedule-based protocols normally require

nodes to form clusters. One of the nodes within the cluster isselected as the cluster head, and nodes are normally

restricted to communicate with the cluster head within a cluster. Moreover, these protocols depend on distributed,

fine-grained time synchronization to align slot boundaries, and have limited scalability and adaptivity to changes in

the number of nodes.

Contention-based protocols, such as CSMA/CA, S-MAC [94], B-MAC [59], and WiseMAC [22], do not

divide and pre-allocate the channel into sub-channels for each node to use. Instead, a common channel is shared

by all nodes, and it is allocated on-demand. A contention mechanism is employed to decide which node has the

right to access the channel. These protocols are not as energy-efficient as schedule-based protocols, but they have

several advantages. First, they scale more easily across changes in node density or traffic load. Second, they are more

flexible as topologies change, because there is no requirement to form communication clusters. Finally, they do not

require fine-grained time synchronization. Due to the highly dynamic and untethered nature of sensor networks, these

advantages make contention-based protocols the preferredchoice, and they have been widely adopted. We use two

common MAC protocols, S-MAC and B-MAC, as examples, and discuss the applicability of our approach on these

protocols.

30

S-MAC is an energy-efficient MAC protocol specifically designed for sensor networks. It is a hybrid of

CSMA and TDMA: it maintains synchronized time slots, but, unlike TDMA, its slots can be much bigger and syn-

chronization failures do not necessarily lead to communication failure. Nodes periodically listen and sleep, and form

virtual clusters based on common sleep schedules. It also adopts similar contention schemes as the IEEE 802.11ad

hocmode [19] to handle hidden terminals and avoid overhearing unnecessary traffic.

Based on the overhearing avoidance mechanism of S-MAC, a sender and a receiver will establish a brief

handshake before the sender transmits data. The handshake starts from the sender by sending a short Request-to-Send

(RTS) packet to the intended receiver. The receiver then replies with a Clear-to-Send (CTS) packet. The sender starts

sending data after it receives the CTS packet. The purpose ofRTS-CTS handshake is to make an announcement to the

neighbors of both the sender and the receiver. Therefore, when a node sends out a data packet, the node’s neighbors

other than its own parent will mostly be sleeping. RTS and CTSwill have much higher opportunity to be overheard

by the neighbors. In this situation, we can put the useful information for parent selection (i.e. node type and depth) in

either RTS or CTS packets (or both) to facilitate the information sharing through overhearing among neighbors.

B-MAC is a lightweight CSMA-based MAC protocol. It is totally asynchronous and doesn’t rely on any rel-

ative time-synchronization information among nodes. Instead, each packet is transmitted with a long enough preamble

so that the receiver is guaranteed to wakeup during the preamble transmission time. It also employs an adaptive pream-

ble sampling scheme to reduce duty cycle and minimize idle listening. Compared to S-MAC, there is no overhearing

avoidance in B-MAC. Before a sender sends out a packet to a receiver, it will first send a preamble long enough for

all its neighbors to wake up, detect activity on the channel,receive the preamble, and then receive the packet. There-

fore, in addition to the receiver, all the other neighbors ofthe sender will receive the packet, even the packet is not

addressed to them. In this situation, we can put the information in the packet header. When a node receives a packet

not addressed to itself, it can retrieve the helpful information from the header before dropping the packet.

3.3.2 Neighbor Table Management

To perform the parent selection in AAT, every node has to record information about its neighboring nodes

from which it receives or overhears packets. All the information is kept in the neighbor table. However, in many cases

the set of candidate neighbors (e.g., recently heard nodes)is usually much larger than the set of useful neighbors (e.g.,

neighbors which can provide a reasonably reliable link) andis too large to retain in the memory of most microcon-

trollers. Thus, neighbor table management is critical. A good management algorithm must keep a small but sufficient

number of good neighbors regardless of the number of nodes ithears from.

When a node receives or overhears a packet, an analysis is performed to decide whether to update the

neighbor table or not, based on the neighbor table management algorithm. Since in a dense deployment of sensors,

a node will hear from many more weakly connected, distant nodes, the management algorithm should prevent the

table from being polluted by these low utility neighbors. Several algorithms have been proposed for neighbor table

management [38, 46], and Frequency [20] has been shown to perform well in maintaining a subset of good neighbors

in a constrained neighbor table regardless of node density.

In the AAT parent selection algorithm, a node tends to chooseother source or forwarding nodes as its parent

in order to reduce the tree cost and increase aggregation opportunity. Without considering link quality, a node may end

up with a distant parent which it can barely communicate with. The use of a neighbor table management algorithm

31

ensures that AAT has a list of “good” neighbors to use for parent selection.

3.3.3 Implicit Acknowledgment

Due to environmental interference and packet collisions, link failures and packet losses are inevitable in

sensor networks. Although sensor networks may indeed tolerate a certain amount of data loss without significantly

affecting the aggregate, for the system to scale, the fraction of delivered packets should remain finite and representative

of the original pool. That means a certain degree of reliability is needed, especially when hop count increases, because

a single lost packet may result in the loss of a large amount ofaggregated data.

A simple solution to improve packet delivery is to use explicit acknowledgment packets or signals. If the

sender does not receive an ack from the receiver within a timeout period, it assumes that either the data packet or

the ack is lost, and sends the data packet again. This solution is intuitive, and has proved to be robust in practice.

However, the extra energy required to send and receive the acks makes this scheme unattractive to sensor networks.

Therefore, we useimplicit acknowledgment[9] to improve the packet delivery rate. When a node receivespackets

from its upstream nodes, instead of sending back explicit acknowledgment packets, the acks are piggybacked on the

aggregated data packet destined to the next hop. Based on thebroadcasting nature of wireless links, the upstream

nodes can usually overhear this packet and get the acks. Our simulation result shows that this scheme can achieve

nearly 100% packet delivery, even with a high link failure rate.

3.4 Simulation Results

To evaluate AAT in a practical sensor network environment, we first simulate the detailed communication

process of a single sensor node to model the additional computation required by AAT. Then we use large-scale net-

works to compare the total energy consumption of SPT and AAT.Since IKMB is not practical for sensor networks, it

is not considered in this section.

3.4.1 Computation Overhead

To model the computation overhead of AAT, we use Atemu [61], acycle-accurate sensor network simulator,

to simulate the communication process of a Mica2 mote with eight neighbors. A Mica2 mote has a 7.3 MHz Atmel

ATmega 128L processor, as well as a CC1000 radio chip, LEDs, ADC, EEPROM, SPI, Timers, and external sensor

boards [18]. Table 3.1 presents the current consumption model for the communication of Mica2 [60]. Our simulation

result is shown in Fig. 3.8. The time period for parent selection, which is the only additional computation required by

AAT, is very short compared to the actual packet transmission. Although the time period for updating neighbor table

is a little longer in AAT (AAT requires more neighbor information), the difference is negligible.

Table 3.2 presents the estimation of energy consumption foreach time period in Fig. 3.8. The result shows

that for each packet sent, AAT only introduces6.211472.48 ≈ 0.4% more energy consumption compared to SPT. We will

use this estimation to calculate the total energy consumption for communication.

32

Table 3.1: Mica2 current consumption model

Mica2 operationCurrent consumption

(with a 3V power supply)

MCU Active 8.0 mAMCU + Radio RX 15.1 mAMCU + Radio TX(0 dBm) 25.4 mA

Figure 3.8: The packet sending/receiving process of AAT. The dark area shows the additional computation overhead.

3.4.2 Network Simulation Settings

We use Prowler [78], a probabilistic wireless network simulator written in Matlab, as the simulation tool.

The settings of the simulation are shown in Table 5.3. The network topology and radio range are the same as in the

preliminary analysis in Section 3.2.2.

We simulate 10 randomly-generated networks as in Section 3.2.2, and calculate the average as the final

result. The beginning of the simulation is the query propagation phase, in which the sink node floods a query message

to the network, and uses it to construct SPT as the aggregation tree. In the result collection phase, 100 randomly

chosen event source nodes generate event packets periodically for 10 epochs. The event packets (with implicit acks)

are relayed to the sink node along the aggregation tree. We simulate simple aggregation functions (e.g.,sum, max,

min), hence the aggregated data packet is the same size as a packet containing a single event. The event deadline is

set to 40 epochs, which is the time period the event data remain valid after they are generated in the source nodes. A

packet is dropped if all of its carried events are outdated.

3.4.3 Packet Delivery Ratio

Link failures and packet losses are common across wireless channels. When a spanning tree approach is

used for aggregation, a single failure would result in an entire subtree of values being lost. If the failure is close to the

Table 3.2: Energy consumption estimation for Fig. 3.8

PeriodEnergy consumption(]cycle × current × cycle time × voltage)

Choose parent 1890 × 8 ×1

7.3×106× 3 ≈ 6.21 µJ

Transmit packet 141064 × 25.4 ×1

7.3×106× 3 ≈ 1472.48 µJ

Receive packet 83483 × 15.1 ×1

7.3×106 × 3 ≈ 518.05 µJ

Update neighbor table 491 × 8 × 17.3×106

× 3 ≈ 1.61 µJ

33

Table 3.3: Simulation settingsTOPOLOGY

field range 320×320

number of nodes 1024

node distribution grid-based + random displacement

RADIO MODEL

max radio range 22

link failure rate 0 – 20%

packet transmission time 288 bit-time

MAC waiting time 40 + random(0–1240) bit-time

MAC backoff time 40 + random(0–600) bit-time

EVENT MODEL

# source nodes 100 (randomly chosen)

# events per source 10

event interval (epoch) 200000 bit-time

event deadline 40 epochs

0

10

20

30

40

50

60

70

80

90

100

0 5 10 15 20

Eve

nt D

eliv

ery

Rat

io (

%)

Link Failure Rate (%)

AAT - no ack

SPT - no ack

AAT - ackSPT - ack

Figure 3.9: Event delivery ratio versus link failure rate for AAT and SPT, with and without implicit acknowledgements.

sink, the influence on the resulting aggregate can be significant. Fig. 3.9 shows the impact of link failures on event

delivery ratio (the ratio of events relayed to the sink node)for SPT and AAT. Without implicit ack, even when link

failure rate is zero, more than 10% of events are lost due to packet collisions. Here AAT performs better than SPT

because of its lighter traffic, which leads to fewer collisions. But when link failure rate increases, the delivery ratioof

AAT decreases faster than SPT due to the higher degree of aggregation. That means each packet in AAT on average

contains more aggregated data, and hence a packet loss results in more event losses.

With implicit ack, on the other hand, almost all the events are successfully delivered. Only a few packets

are dropped because of outdated event data. As before, the delivery ratio of AAT becomes lower than SPT due to the

higher degree of aggregation.

Fig. 3.10 presents the timing of the event delivery with5% link failure rate for one of the networks. Whether

the ack is used or not, AAT has longer latency than SPT, but thedifference never exceeds two epochs. For example, to

deliver 90% of the events with ack enabled, AAT requires 21 epochs and SPT requires 19 epochs. Although there is a

31% increase in the tree depth (17−1313 ≈ 31%), it only results in limited increase in delay.

34

0

100

200

300

400

500

600

700

800

900

1000

5 10 15 20 25 30

Num

ber

of D

eliv

ered

Eve

nts

Time (epoch)

SPT: depth=13 cost=327AAT: depth=17 cost=205

source nodesgenerate events

SPT - ackAAT - ack

SPT - no ackAAT - no ack

Figure 3.10: The timing of event delivery for AAT and SPT, with and without acknowledgements.

3.4.4 Total Energy Consumption

While the cost of the aggregation tree provides an easy way toestimate the communication cost, it doesn’t

precisely reflect the actual energy consumption. To model the communication cost more accurately, we use the single-

node power estimation in Table 3.2 to calculate the network-wide energy consumption, which includes the following

components:

• Parent selection: AAT requires extra computation for parent selection, and hence introduces more energy con-

sumption.

• Packet sending: The energy consumption to transmit data through radio.

• Packet receiving/overhearing: The energy consumption to receive data from radio. Here we assume that over-

hearing a packet consumes the same energy as receiving a packet. The assumption is true for MAC protocols

(e.g., B-MAC) which require nodes to receive the whole packet before discarding useless ones.

• Failed packet receiving/overhearing: The energy consumption for failed packet receiving/overhearing, which

results from packet collision or link failure.

• Neighbor table update: The energy required to update the neighbor table.

Fig. 3.11 shows the total energy consumption for communication in the result collection phase with implicit acknowl-

edgement enabled. Because of the fewer edges of AAT, the number of packets sent is less than SPT. Even though

AAT requires0.4% more energy consumption per packet sent for parent selection, the total energy consumption for

packet sending is still much lower. Furthermore, the fewer packets sent also results in less energy consumption for

packet receiving, overhearing, and failed packet receiving. On average, AAT saves20% on energy consumption for

communication. Fig. 3.12 shows the total communication energy consumption with different number of source nodes.

When the number of sources increases, the reduction in energy consumption becomes more obvious because each

node has higher opportunity to observe other source nodes inits neighborhood. This feature is especially beneficial

35

0

5

10

15

20

25

30

35

SPT AAT SPT AAT SPT AAT SPT AAT SPT AAT

Ene

rgy

Con

sum

ptio

n fo

r C

omm

unic

atio

n (J

)

Link Failure Rate (%) 0 5 10 15 20

SendingReceivingOverhearingFailed_Rx

Figure 3.11: The components of energy consumption for communication for different link failure rates. The error barrepresents the variance on the total energy consumption forthe 10 simulated networks.

for event-based query processing systems, where a group of nearby sensor nodes tend to generate event packets and

become source nodes simultaneously when events occur in thevicinity.

3.5 AAT on GPSR

While shortest path routing as described in Section 3.1 is commonly used for query processing, there are

other different routing protocols used in sensor networks.In this section, we use GPSR (Greedy Perimeter Stateless

Routing) [33] as an example to demonstrate how AAT works withGPSR and evaluate its performance.

3.5.1 GPSR

GPSR is a geographic routing protocol, which routes packetsbased on the coordinates of nodes. Every node

in the sensor network is assumed to know its own position, either from a GPS device or through other localization

mechanisms. Two different algorithms are used within GPSR:greedy forwarding and perimeter forwarding. Greedy

forwarding is conceptually simple. Assume each node in a network knows its own location and that of its neighbors.

When a node receives a message, it sends the message to the neighbor closest to the destination. GPSR forwards

packets greedily whenever possible; however, a neighbor closer to the destination might not always exist. When a

packet is at a node which has no neighbors closer to the destination than itself, we say that the packet has encountered

a void. GPSR invokes perimeter forwarding, which uses the right-hand rule to walk around the perimeter of the void.

When this traversal reaches a node that is closer to the destination, greedy forwarding resumes. If a packet traverses

the entire perimeter and returns to its entry point, then thenode must be the closest node to the destination. Thus,

given a destination location, GPSR uses a combination of greedy and, if required, perimeter forwarding to reach the

node closest to the destination.

36

0

10

20

30

40

50

60

SPTAAT SPTAAT SPTAAT SPTAAT SPTAAT SPTAAT SPTAAT SPTAAT

Ene

rgy

Con

sum

ptio

n fo

r C

omm

unic

atio

n (J

)

Number of sources 50 100 150 200 250 300 350 400

SendingReceivingOverhearingFailed_Rx

Figure 3.12: The total communication energy consumption with different number of source nodes. The link failurerate is 5%. The error bar represents the variance among the 10simulated networks. The energy saving of AAT isaround 23%, independent of the number of source nodes.

3.5.2 AAT on GPSR

The implementation of AAT on GPSR follows the same heuristics as we discussed in Section 3.2. While

the routing decisions are made based on the node level (number of hops away from the sink) in SPT, in the greedy

forwarding of GPSR, the decisions are made with respect to the Euclidean distancebetween a node and the sink.

Therefore, the only slight modification required for AAT to work with GPSR is using the Euclidean distance to rep-

resent the “level” in Algorithm 1. Similarly, the loop prevention mechanism is used to restrict nodes from choosing

parents geographically farther away, with node id as a tiebreaker when necessary. Furthermore, since GPSR uses

periodic beacon packets for neighbor table maintenance, the necessary neighbor information for the AAT algorithm

can be easily acquired through the beacon packets.

3.5.3 Simulation Results

The simulations use the same settings as in Table 5.3, and implicit acknowledgement is accommodated for

better event delivery ratio. Every node sends out one beaconpacket per epoch. The beacon frequency affects the

transformation speed of AAT. High beacon frequency allows sensor nodes to notice the changes of the network (e.g.,

topology or the set of source nodes) in a shorter period of time, and hence the AAT can adapt to the changes faster.

However, high beacon frequency also implies more energy consumption. Dynamic beacon frequency [15] dependent

on network mobility and traffic pattern has been proposed to optimize the utilization of wireless medium and node

energy. Here we assume fixed beacon frequency for simplicity.

The comparison of GPSR and GPSR with AAT is shown in Fig. 3.13.With different number of source

nodes, the tree cost reduction is between 37% and 47%, with the increase of tree depth ranging from 16% to 32%.

Fig. 3.14 shows the comparison of communication energy consumption for data packets. The energy consumption of

beacon packets is not included for simplicity, since it is only related to the size and frequency of the beacon packets.

37

0

5

10

15

20

GPSR

GPSR_AAT

0

100

200

300

400

500

600

700

40035030025020015010050

Averagetree cost

Number of sources

Averagetree depth

GPSRGPSR_AAT

Figure 3.13: The comparison of the original GPSR and GPSR with AAT. The result is the average of 10 randomnetworks. With different number of source nodes, the tree cost reduction is between 37% and 47%.

We assume identical energy consumption of beacon packets byignoring the increase of packet size resulted from the

extra field (i.e. node type) necessary for AAT, which is smallcompared to the size of the whole beacon packet. Similar

to the results in Section 3.4.4, the increase of source nodesleads to more energy savings.

3.6 Summary

In this section, we propose the adaptive aggregation tree for constructing and managing data aggregation

in wireless sensor networks. Data aggregation reduces the energy consumption of a sensor network by reducing the

number of message transmissions, and effective aggregation requires that event messages be routed along common

paths.

Based on the simple shortest-path tree, AAT allows each nodeto choose a new parent if it appears to pro-

vide better opportunities for aggregation. Rather than accommodating complex algorithms with heavy inter-nodal

communication, the parent selection is based on simple heuristics, which not only require just local information but

also guarantee loop-free. The required information for theAAT transformation can be easily obtained either through

periodic beacon packets or packet overhearing. The relatedimplementation issues are described in detail in the paper.

To evaluate the performance of AAT, we first use a cycle-accurate simulator to model the detailed commu-

nication process of a sensor node, then use a wireless network simulator to further estimate the total communication

energy consumption for large-scale networks. It shows thatAAT has 23% energy saving on average compared to SPT,

with number of source nodes ranging from 50 to 400 in a 1000-node network. The saving even approaches the minimal

Steiner tree when the source density is high. The obvious saving is beneficial for long-running and energy-constrained

sensor networks.

Besides SPT, AAT can also be easily applied on different routing protocols. We use GPSR as an example to

38

0

10

20

30

40

GPSR

GPSR_AAT

GPSR

GPSR_AAT

GPSR

GPSR_AAT

GPSR

GPSR_AAT

GPSR

GPSR_AAT

GPSR

GPSR_AAT

GPSR

GPSR_AAT

GPSR

GPSR_AAT

Ene

rgy

Con

sum

ptio

n fo

r C

omm

unic

atio

n (J

)

Number of sources 50 100 150 200 250 300 350 400

SendingReceivingOverhearingFailed_Rx

Figure 3.14: The energy consumption of GPSR and GPSR with AATin networks with 5% link failure rate. Theenergy consumption for beacon packets are not included. Theerror bar represents the variance among the 10 simulatednetworks. The energy saving is around 31%, independent of the number of source nodes.

demonstrate that AAT can be carried out with only slight modification, and the average energy saving of GPSR with

AAT achieves 31% without considering the cost of beacon packets.

While the aggregation tree optimization can decrease the amount of traffic and reduce the total energy

consumption in sensor networks, it may not necessarily benefit the whole system lifetime. In the next two chapters,

we will show that the lifetime of sensor networks is mostly dominated by a small group of sensor nodes. Due to

the multihop routing nature of sensor networks, these nodestend to have heavier workload and deplete their energy

sources faster than other nodes. Such workload imbalance leads to a premature loss of connectivity in the network and

negatively impacts the system lifetime. Therefore, we propose load-balancing mechanisms to alleviate such workload

imbalance problems for longer system lifetime.

39

Chapter 4

Overhearing Reduction along Routing

Paths

Various energy-efficient paradigms and strategies have been devised to collect and route the data packets

towards the base station, trying to maximize the lifetime ofsensor nodes while maintaining system performance and

operational fidelity. For example, based on the observationthat the communication among sensor nodes consumes a

large portion of the battery energy of the sensor nodes [27],some approaches focus on reducing communication power

consumption, such as clustering algorithms [5], data-centric paradigms [30], dynamic transmission power adjustment

[13, 49], etc. However, regardless of the routing strategy,the sensor nodes closer to the base station have to forward

more packets than the ones at the periphery of the network. The heavier workload results in more energy consumption,

and the nodes close to the base station will deplete their energy first, leading to a premature loss of connectivity in the

sensor network. Thisenergy holeproblem occurs regardless of the routing strategies and MACprotocols, and may

severely reduce the effective network lifetime.

To alleviate this undesirable effect, a mechanism to balance the energy usage among sensor nodes is required.

As shown Chapter 3, in-network data aggregation [37] has been proved to be an efficient approach to decrease the

traffic in sensor networks, and can successfully reduce the forwarding workload of sensor nodes close to the base

station. However, the energy consumption on overhearing still results in an obvious imbalance on energy consumption.

In this paper, we propose a mechanism to alleviate the energyhole problem by reducing the overhearing energy

consumption along the routing paths. Our approach extends the concept of density control mechanisms. Instead of

achieving a uniform density all over the sensor field, we further exploit the local information to reduce the density

along routing paths, in order to reduce the overhearing energy consumption.

In Section 4.1, we present a sensor network scenario to stateour assumptions and analyze the main cause

of the unbalanced energy usage. Some background knowledge and related works are reviewed in Section 4.2. Section

4.3 describes our approach and implementation issues in more detail. We present the simulation results in Section 4.4,

40

Figure 4.1: The network model for analysis.Hmax is the maximum number of hops required to forward packets fromthe outermost nodes to the base station.

and summarize in Section 4.5.

4.1 Unbalanced Energy Usage

To motivate our research, we consider a habitat monitoring sensor network deployed in a remote field with

the base station in the center. The sensors in the network arehomogeneous; that is, all sensors have the same com-

putation capability and transmission range, and are powered by batteries with the same available energy. The sensors

work in an event-driven mode, which means a node only generates event packets when phenomena are detected. With

this scenario, we can analyze the components of energy consumption to determine the main cause of the energy hole

problem.

4.1.1 Preliminary analysis

We consider a network consisting of sensor nodes deployed in2-D Poisson distribution, and analyze the

energy consumption corresponding to packet sending, receiving, and overhearing. For simplicity, we assume that the

level of every node (i.e., the number of hops away from the base station) is simply proportional to the distance from

the base station, and every node has the same number of neighbors. Fig. 4.1 illustrates the network model for analysis.

If the sensor nodes are deployed in 2-D homogeneous Poisson distribution with densityλ, the expected

number of sensor nodes in thekth level isN(k) = λ(2k − 1)πr 2c for k ≥ 1. Ps(k) is the probability of a node in thekth

level to become a source node. In a network without data aggregation, the average number of messages sent by a node

in thekth level is

MS(k) =

PHmaxk′=k

(2k′− 1)Ps(k′)

2k − 1

Assuming that every node has the same number of children, we can then calculate the average number of

messages received by a node in thekth level.

MR(k) = MS(k + 1) ×N(k + 1)

N(k)

41

0 5 10 150

1

2

3

4

5

6

7

Level

Ave

rage

num

ber

of p

acke

ts

(a) No aggregation, Hmax

=15

sendrecvoverheartotal

0 2 4 6 8 100

1

2

3

4

5

6

7

Level

Ave

rage

num

ber

of p

acke

ts

(b) No aggregation, Hmax

=10

sendrecvoverheartotal

0 5 10 150

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

Level

Ave

rage

num

ber

of p

acke

ts

(c) Aggregation, Hmax

=15

sendrecvoverheartotal

0 2 4 6 8 100

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

Level

Ave

rage

num

ber

of p

acke

ts

(d) Aggregation, Hmax

=10

sendrecvoverheartotal

Figure 4.2: The average number of messages sent, received, and overheard by nodes in different levels, with andwithout aggregation, with fixedBt = 8 andPS(k) = 0.01.

The average number of messages overheard by a node inkth level is

MO(k) = Bu · MS(k − 1) + Bs · MS(k) + Bl · MS(k + 1) − MR(k)

whereBu, Bl, andBs represent the numbers of neighbors in upper, lower, and the same level, respectively. If every

sensor node hasBt neighboring nodes, we can calculateBu, Bl, andBs asBu = Bt ×N(k−1)

N(k−1)+N(k)−1+N(k+1), Bl =

Bt ×N(k+1)

N(k−1)+N(k)−1+N(k+1), andBs = Bt ×

N(k)−1N(k−1)+N(k)−1+N(k+1)

.

With in-network data aggregation, since a node sends out at most one message in each epoch, the average

number of messages sent can be reduced to

MS(k) = Mfwd(k) + Msrc(k) − Mfwd(k) · Msrc(k)

andMS(Hmax) = PS(Hmax). In the equation,Mfwd(k) = min(1, MS(k +1)× N(k+1)N(k)

) is the average number of messages

a node has to forward, andMsrc(k) = PS(k) is the average number of event messages a node generates.

By settingPS(k), Hmax, andBt appropriately, we can analyze the communication workload of nodes in

different levels. Fig. 4.2 shows the average workload with respect to varying routing tree depth (Hmax). When in-

network aggregation is not performed, the workload of nodesin different levels is extremely unbalanced, which results

in the unfavored energy hole problem. The unbalance becomeseven more severe when the network size increases

(i.e. largerHmax). Although the nodes’ workload closer to the base station can be greatly reduced with in-network

aggregation, the unbalanced workload among nodes in different levels is still obvious.

The workload in different levels with respect to varying number of neighbors (Bt) is shown in Fig. 4.3. It

can be seen that overhearing is the dominant component of theworkload in all cases, and the cost of overhearing is

proportional to the number of neighbors each node has. However, a large number of neighbors is common in sensor

networks. For example, it is usually easier to deploy a largenumber of nodes initially than to deploy additional

nodes or to recharge depleted batteries at a later date. Also, to meet the sensing coverage requirements of some

applications, dense deployments of sensors are necessary because of the small sensing range compared to radio range

in typical sensor mote architectures [97]. Such dense deployment of sensors would aggravate the overhearing waste

and severely reduce the system lifetime.

42

0 5 10 150

2

4

6

8

10

12

Level

Ave

rage

num

ber

of p

acke

ts

(a) No aggregation, Bt=16

sendrecvoverheartotal

0 5 10 150

2

4

6

8

10

12

Level

Ave

rage

num

ber

of p

acke

ts

(b) No aggregation, Bt=8

sendrecvoverheartotal

0 5 10 150

0.5

1

1.5

2

2.5

3

3.5

Level

Ave

rage

num

ber

of p

acke

ts

(c) Aggregation, Bt=16

sendrecvoverheartotal

0 5 10 150

0.5

1

1.5

2

2.5

3

3.5

Level

Ave

rage

num

ber

of p

acke

ts

(a) Aggregation, Bt=8

sendrecvoverheartotal

Figure 4.3: The average number of messages sent, received, and overheard by nodes in different levels, with andwithout aggregation, with fixedHmax = 15 andPS(k) = 0.01.

4.1.2 Reduce Overhearing on Routing Paths

To alleviate the unbalanced energy usage, a mechanism to reduce the energy consumption of sensor nodes

close to the base station is required. Although in-network data aggregation can successfully reduce the forwarding

traffic of sensor nodes close to the base station, the energy consumption on overhearing still results in obvious unbal-

anced energy usage. Therefore, we proposeneighborhood-aware density control (NADC), an approach to alleviate the

unbalanced energy consumption by reducing the overhearingenergy consumption along the routing paths.

In NADC, nodes observe their neighborhoods and dynamicallyadapt their participation in the multihop

network topology. Since the neighborhood information of each node can be easily obtained through the overheard

information, the density in different regions can be adaptively adjusted in a totally distributed manner. If a node ob-

serves that it is near a routing path, the node can reduce its probability to join the network in order to avoid unnecessary

overhearing. The details of NADC will be further discussed in Section 4.3.

4.2 Related Work

4.2.1 Density Control

Overhearing is one of the most important components of energy waste in sensor networks [6]. It occurs

when a node receives packets that are destined to other nodes. Overhearing unnecessary traffic is a dominant factor of

energy waste when the traffic load is heavy or the node densityis high. Furthermore, radios consume energy not only

when sending and receiving, but also when listening or idle.Table 4.1 shows the power consumption characteristics

of MEDUSA-II and Rockwell’s WINS node [63]. The power consumed by the node with the radio in Idle mode is

approximately the same with the radio in Receive mode. Thus,operating the radio in Idle mode does not provide any

advantage in terms of power. The great energy consumption associated with idle time and overhearing suggests that

energy optimizations must turn off the radio, not simply reduce packet transmission and reception.

Sleep-based topology control, also referred to asdensity control, is an approach that controls the density of

working sensors at a desirable level. Specifically, densitycontrol ensures only a subset of sensor nodes operate in

the active mode, while meeting the sensing coverage or connectivity requirements. The basic approach used in many

density control techniques is as follows. A set of sleep-related states is defined for each node, and the nodes then

43

Table 4.1: Power consumption (inmW) of Medusa II and Rockwell’s Wins nodes (with processor and sensor in activemode) [63]

Radio Mode Tx Rx Idle Sleep

Medusa II 24.58 (Tx power: 0.7368mW) 22.2 22.06 9.72

WINS 1080.5 (Tx power: 36.3mW) 751.6 727.5 416.3

transition between these states depending on explicit messages with their neighbors, overhead messages, or implicit

timers.

Consider the following simple example as an illustration. Each node can be in one of three states:sleep,

test, andactive. By default a node switches periodically between sleep and test states. During the test state, a local

eligibility rule is applied (e.g., “are there enough awake neighbors nearby?”, or “is there data intended for me?”) to

determine whether the node should wakeup. If the rule is satisfied, then the node transitions to the active state. When

the node is in the active state, it may remain in the active state or return back to sleep according to different conditions.

Several methods have been proposed to minimize the energy consumption and prolong the network lifetime

while maintaining the connectivity. The GAF [91] scheme divides the network into virtual grid cells. Assuming every

node knows its location through GPS or other localization service, every node knows which grid it is in. Within each

grid only one node stays up and the rest go to sleep, and nodes alternately have their radios on in order to accomplish

load balancing.

In AFECA [90], each node maintains a count of the number of nodes within its radio range, obtained by

listening to transmissions on the channel. A node switches between sleeping and listening, with randomized sleep

times proportional to the number of neighboring nodes. The net effect is that the number of listening nodes is roughly

constant, regardless of node density; as the density increases, more energy can be saved. ASCENT [12] further takes

the per-link data loss rate into consideration, and make thestate transitioning decisions based on the number of active

neighbors and link quality measurements.

SPAN [14] adaptively elects coordinators from all nodes to form a forwarding backbone in the network. Each

node bases its decision on an estimate of how many of its neighbors will benefit from it being awake, and the amount

of energy available to it. Span coordinators stay awake continuously and perform multihop packet routing, while other

nodes remain in power-saving mode and periodically check ifthey should wakeup and become a coordinator.

In STEM [73], the link between nodes is constructed by havingeach node periodically turn on its radio for a

short time to listen if someone else wants to communicate with it. The node that wants to communicate wakes up the

target node by sending out a beacon with the id of the node it istrying to wake up. As soon as the target node receives

the beacon, it responds back and both keep their radio on at this point. Once both nodes have their radio on, the link

is constructed and can be used for subsequent packets. STEM also proposes using different frequency bands for the

wakeup protocol and the data transfer to avoid interference.

4.2.2 Transmission Power Adjustment

If the deployment density is already sufficient to guaranteethe required level of coverage, the connectivity

properties of the network can be adjusted by tuning the transmission power of the constituent nodes. Power control is

quite a complex and challenging cross-layer issue. Increasing radio transmission power has a number of interrelated

44

consequences – some of these are positive, others negative:

• It can extend the communication range, increasing the number of communicating neighboring nodes and im-

proving connectivity in the form of availability of end-to-end paths.

• For existing neighbors, it can improve link quality (in the absence of other interfering traffic).

• It can induce additional interference that reduces capacity and introduces congestion.

• It can cause an increase in the energy expended, due to the higher transmitting power and increased overhearing

waste.

Many algorithms have been developed for variable power-based topology control [50, 64, 87, 41, 55]. Power control

techniques must provide connectivity, while taking into account diverse factors, including interference minimization

and energy reduction.

4.2.3 Balanced Energy Usage

While designing protocols to achieve energy efficiency to extend the nodes’ lifetime has become a major

concern, it is also important to maintain a balance of power consumption in the network so that certain nodes do not

die much earlier than others, leading to network partitionsor unmonitored areas in the network. Many papers have

shown that the nodes nearest the base station die earlier than the nodes far from the base station due to their heavier

workload. The phenomenon is also referred to asenergy holes. Several approaches have been proposed trying to

balance the energy usage among sensors to extend the networklifetime.

Zhanget al. have found the upper bounds of the lifetime of a large multihop wireless sensor networks

[97]. Optimal sensor management [58] is formulated as a generalized maximum flow graph problem with addi-

tional constraints to maximize the lifetime of an application. Maximum lifetime data gathering problem [32] presents

polynomial-time algorithms to solve the data gathering problem, given the locations of the sensors and the sink and

the available energy at each sensor. Olariuet al. also propose an iterative process to avoid the creation of energy holes

around the base station [52].

4.2.4 Location-Varying Deployment

Some mechanisms have been proposed to alleviate the unbalanced energy usage problem while deploying

the sensor nodes. Location-varying node density [43] is a method that deploys more sensor nodes around the base

station, so that the load among sensor nodes at different distances from the base station can be balanced. Deploying

assisting gateways or special relaying nodes in the networkis also proposed to provide better connectivity and facilitate

scalability [1, 95].

Sichitiu et al. [77] propose to use multiple levels of batteries. Nodes closer to the base station are equipped

with batteries of large capacity, and other nodes have the battery inversely proportional to the distance from the

base station. They have shown with simulation that under a total battery budget, the lifetime of the network can be

significantly improved, even if a small number of battery levels is used.

45

4.3 Neighborhood-Aware Density Control

While density control mechanisms show the capability to reduce the unnecessary energy consumption in

WSNs, the approach doesn’t help much on the unbalanced energy usage, which results in the energy hole problem and

negatively impacts the system lifetime. As described in Section 4.2, some algorithms have been proposed to solve the

energy hole problem by pre-determining the topology based on the anticipated traffic within the network. However,

these algorithms typically need the topology or traffic information of the whole network, and the sensor nodes have

to perform complicated computation to decide their work schedule or transmission range. These requirements are

usually impractical in a highly dynamic sensor network environment.

Therefore, we propose neighborhood-aware density control(NADC), which requires only local information

and simple computation, to balance the energy usage among sensor nodes in WSNs. In this section, we introduce

the system assumptions used throughout the work, describe the uniform density control mechanism, and then present

the algorithm of NADC and some relevant implementation issues. For illustration purposes, we use a simple density

control mechanism based on the number of active neighboringnodes. The implementation of NADC with other density

control mechanisms will be addressed in future work.

4.3.1 System Assumptions

NADC is based on the assumption that most of the time the sensor network is only sensing the environment,

waiting for an event to happen. One example of such application is a sensor network designed to track animals in

a forest. These networks have to remain operational for months or years, but sensing only on the occurrence of an

animal passing by. Clearly, although it is desirable that the transfer state be energy-efficient, it may be more important

that the monitoring state be ultra-low-power as the networkresides in this state for most of the time. This observation

holds true for many other applications as well.

We assume that the sensors in the network are homogeneous; that is, all sensors have the same computation

capability and storage memory. In addition, they transmit at the same power level and hence have the same radio

range, and are powered by batteries with the same available energy. We also assume that the power consumption of

sensor nodes is dominated by communication costs, as opposed to sensing and processing costs. This assumption is

reasonable for many types of sensor nodes that require very little energy, such as pressure and temperature sensors.

4.3.2 Uniform Density Control

Similar to AFECA [90] discussed in Section 4.2, the uniform density control (UDC) mechanism uses the

number of active neighbors as the criteria of node state transitioning. In UDC, nodes are in one of three states:sleeping,

discovery, andactive. A state diagram is shown in Fig. 4.4.

Initially nodes start out in thesleepingstate. Whensleepingthe radio is off, not consuming power. In this

state they keep their radio turned off for timeTsleep, then transition todiscovery. If a node has data to send while

sleeping, it transitions toactiveand starts sending the data. (Although the radio is off, sensors or other parts of the

node may be on.)

46

Figure 4.4: State transitioning diagram of uniform densitycontrol. NB represents the number of neighbors, andThrepresents the threshold value.

When in the statediscovery, a node turns on its radio and listens for messages for an epoch. It also performs

neighbor discovery by maintaining the number of active neighbors, which is the number of neighboring nodes it hears

in this epoch. At the end of the epoch, the node transitions tostateactiveif it gets a routing message and participates

in the route, or if it decides to send data. Otherwise it uses the number of active neighbors,Nb, and a predefined

threshold,Th, as the transitioning criteria: transition toactiveif Nb < Th, otherwise returns tosleeping.

When in the stateactive, a node does the data sending/receiving as well as neighbor discovery. If the node

has no data to send in this epoch, it sends out a beacon packet as a notification of its existence. At the end of the epoch,

it makes the state transitioning decision by following the same rules as in thediscoverystate.

In this simple UDC mechanism, all nodes except source and forwarding nodes perform density control based

on a predefined threshold value. The threshold value is chosen so that there is a high probability that the active nodes

form a connected graph, so that multihop forwarding works. If the density of active nodes can not provide good

connectivity in the network, the delay of event delivery (i.e. the time required to relay an event packet to the base

station) will increase dramatically. Since a node does not know whether it is required to be active in order to maintain

good connectivity in the network, to be conservative the threshold value tends to be high to keep a large number

of active nodes. That means nodes are active even when they could be asleep, and results in unnecessary energy

consumption.

4.3.3 Neighborhood Type

In many event-based sensor network applications, such as habitat monitoring or intrusion detection systems,

source nodes tend to locate in the same area where events occur. As shown in Fig. 4.5, we can categorize the sensor

field into three different types:hot area, midstream area, andsilent area. A node’s neighborhood type is identified

according to the following rules:

• A node is in ahot areaif it is a source node or at least one of its neighbors is a source node. A node is referred

to as a source node if it detects events and generates data packets.

• A node is in amidstream areaif none of its neighbors is a source node and at least one of itsneighbors is a

forwarding node. A forwarding node doesn’t generate data packets but forwards packets along the routing path.

47

Figure 4.5: Different neighborhood types in event-based sensor networks. The outermost rectangular area representsthe whole sensor field.

• A node is in thesilent areaif all its neighbors are idle nodes (i.e. none of its neighbors is a source or forwarding

node).

By embedding thenode typeinformation in the transmitted packets, each node can keep track of the node type of its

neighbors, and easily identify its neighborhood type. Algorithm 2 shows the process of neighborhood observation.

Algorithm 2 Neighborhood observation of NADCfunction PacketReceived()

switch ReceivedPacket.NodeTypecase Src:

Node.NeighborhoodType=HOTAREAcase Fwd:

if Node.NeighborhoodType!=HOTAREA thenNode.NeighborhoodType=MIDSTREAMAREA

end ifcase Idle:

if Node.NeighborhoodType!=HOTAREA and Node.NeighborhoodType!=MIDSTREAMAREA thenNode.NeighborhoodType=SILENTAREA

end ifend switch

end function

4.3.4 NADC

The objective of NADC is to alleviate the unbalanced energy usage by avoiding unnecessary overhearing

along routing paths (i.e. hot areas and midstream areas) while keeping the delay of event delivery in a reasonable

range. In UDC, since all nodes other than source and forwarding nodes perform density control based on the threshold

value, if different threshold values are used in different areas, the node density can be controlled in different levels.

The mechanism of NADC is very similar to UDC. However, instead of achieving a uniform node density

throughout the network, NADC uses neighborhood type to determine the threshold value. Since there are packets

being transmitted in hot areas and midstream areas, the nodedensity in these areas can be reduced by using a smaller

threshold value in order to avoid unnecessary overhearing.The node density in silent areas is maintained in the normal

level by using the original threshold value to provide good connectivity in the network. Algorithm 3 shows the state

transitioning procedure of NADC.

Initially nodes start out in thesleepingstate, where a random timer is used to set the length of sleep in a

predefined range to avoid synchronization. When a node wakesup, it transitions to thediscoverystate, and performs

neighbor discovery and neighborhood observation for an epoch. At the end of the epoch, the node obtains the number

48

Algorithm 3 State transitioning procedure of NADCswitch NeighborhoodType

case hot area:Th=THRESHOLD1

case midstream area:Th=THRESHOLD2

case silent area:Th=THRESHOLD3

end switchif Node.type==SRC or Node.type==FWD then

Node.state=ACTIVEelse

switch Node.statecase SLEEP:

if sleep_time>=Tsleep thenNode.state=DISCOVERY

end ifcase DISCOVERY:

if NumberOfActiveNeighbors>=Th thenNode.state=SLEEP

elseNode.state=ACTIVE

end ifcase ACTIVE:

if NumberOfActiveNeighbors>=Th thenNode.state=SLEEP

end ifend switch

end if

Table 4.2: NADC statesstate radio sensor communication task

Sleeping off on –

Discovery on on

neighbor discovery

neighborhood observation

data receiving

Active on on

neighbor discovery

neighborhood observation

data sending/receiving

of active neighboring nodes and its neighborhood type from the overheard information, and then transits to either the

activestate or thesleepingstate according to the NADC state transitioning procedure.If a node has data to send or

receives packets that need to be forwarded, it transitions to activestate in the next epoch to do the communication tasks,

regardless of its current state. When a node is in theactivestate, it performs neighbor discovery and neighborhood

observation as in thediscoverystate, and makes the state transitioning decision at the endof each epoch. Table 4.2

lists the three different states of NADC.

4.3.5 Tuning NADC

NADC leaves choices of some parameters to the application, includingthresholdsandsleep time. In our

design, we have three threshold values,Thh, Thm, andThs, corresponding to the threshold in hot, midstream, and

silent areas, respectively. When there is no event occurring, the node density of the network is determined byThs;

therefore,Ths should be set to an appropriate value to maintain the required connectivity and sensing coverage. When

events occur,Thh andThm determine the node density in hot areas and midstream areas.In midstream areas, a small

Thm value keeps a small number of active nodes and reduces the overhearing energy consumption. In hot areas, the

Thh value can be further adjusted according to application needs. For example, it can be set to a larger value to retain

49

more active nodes for higher observation fidelity, or to a smaller value to reduce the overhearing energy consumption.

When a node transitions to the sleep state, it sets the sleep time T sleep to determine how many epochs it

will sleep. Thus the range ofT sleep affects the speed of NADC to adapt to topology changes (due tounstable links,

new source nodes, or dead nodes). A short sleep time allows nodes to wake up frequently and rapidly adapt to the

environment changes, while longer sleep time saves more energy and suits relatively stable networks. A node can

dynamically adjust its range of sleep time according to the stability of the network.

4.3.6 Implementation Issues

In our proposed algorithm, we exploit the overhearing effect as an approach for neighbor discovery and

neighborhood observation. Similar to the AAT implementation discussed in Section 3.3, the required information for

NADC (i.e. node type) can be embedded in either regular data packets, RTS-CTS packets, or even beacon packets

based on different underlying MAC protocols.

The issue of neighbor management must also be considered. Neighbor table management has been discussed

in detail in Section 3.3.2.

4.4 Evaluation Results

4.4.1 Simulation Settings

In our simulation, we assume that every node turns on only itssensors and some preprocessing circuitry

during monitoring states. Whenever a possible event is detected, the main processor is woken up to analyze the sensed

data in detail and forward it to the base station. Data aggregation is performed in each node to combine multiple

received packets into a single packet on their way to the basestation. To reduce the overhearing energy consumption

along the routing paths, NADC tries to reduce the density of active nodes in hot areas and midstream areas, while

keeping the density in the normal level in silent areas in order to maintain good connectivity in the network. Therefore,

we define two threshold values,T H andT L, to control the density in silent areas and hot/midstream areas, respectively.

For simplicity, we also use a fixed range of sleep time (3–6 epochs) whenever a nodes transitions into the sleep state,

while leaving adaptive sleep range for future work. We have the following four different density control settings in our

simulation:

• UDC-high: Uniform density control with high active node density, whereT H = 12 andT L = 12.

• UDC-medium: Uniform density control with medium active node density, whereT H = 6 andT L = 6.

• UDC-low: Uniform density control with low active node density, whereT H = 2 andT L = 2.

• NADC: Neighborhood-aware density control, whereT H = 12 andT L = 2.

We use Prowler [78], a probabilistic wireless network simulator written in Matlab, as the simulation tool. The network

setting of the simulation is shown in Table 4.3. In the network, a 320 × 320 field is evenly divided into 1024 grids,

50

Table 4.3: Network settingTOPOLOGY

field range 320×320

number of grids 1024

nodes per grid 2

node distribution grid-based + random displacement

RADIO MODEL

max radio range 22

link failure rate 0.05

packet transmission time 288 bit-time

MAC waiting time 40 + random(0–1240) bit-time

MAC backoff time 40 + random(0–600) bit-time

EVENT MODEL

mobility model Random Waypoint

number of moving objects 20

sensing range 5

ENERGY MODEL

radio Tx cost 1.2 per packet

radio Rx cost 1 per packet

energy budget 50 per node

and two nodes are placed at random positions within each grid. A base station is placed at the center of the field. The

communication range is set to 22 units, which guarantees that each node will be able to communicate with at least

one node in its neighboring grids. Implicit acknowledgement (see Section 3.3.3) is accommodated when packets are

relayed to the base station.

The Random Waypoint (RWP) model [31] is used to simulate the movement of sensing objects in the sensor

field. In RWP, each object is initially placed at a random position. As the simulation progresses, each object pauses at

its current location for a period, which we call the pause time, and then randomly chooses a new location to move to

and a velocity at which to move there. Each object continues this behavior, alternatively pausing and moving to a new

location, for the duration of the simulation.

In our simulation, we have 20 moving objects in the sensor field. The sensor nodes close to the objects

will detect the event and become source nodes. Source nodes generate event packets in each epoch. Event packets

are routed on the aggregation tree towards the base station,and in-network aggregation is performed by the nodes

on the routing paths. We use the number of packets sent/received/overheard to compare the workload of nodes,

and calculate the energy usage using the Mica2 power model measured by Shnayderet al. [76], where the ratio of

energy consumption for packet sending and receiving is1.2:1with 0 dBm transmission power. We also assume that

overhearing a packet consumes the same energy as receiving apacket, which is true for MAC protocols (e.g., B-MAC)

that require nodes to receive the whole packet before discarding useless ones.

4.4.2 Unbalanced Energy Consumption

Density control mechanisms reduce the number of active nodes to avoid the unnecessary overhearing waste.

However, the heavier workload of nodes close to the base station still results in an extremely unbalanced energy

consumption. This unfavored effect becomes worse when the density of active node increases. Fig. 4.6 shows the

energy hole problem due to unbalanced energy consumption bydividing the nodes into four classes based on the

nodes’ levels.

Among the four settings, UDC-high suffers from the worst unbalanced energy consumption among nodes

in different levels, which results in a high percentage of dead nodes near the base station. Since there are more active

nodes along the routing paths, the large amount of overhearing waste soon depletes the limited energy budget of nodes

close to the base station. We can see that while there are lessthan 5% of dead nodes in the peripheral of the network

51

0 25 50 75 100 125 150 1750

0.2

0.4

0.6

0.8

1

% o

f dea

d no

des

Epoch

(a) UDC−high (TH=12 T

L=12)

0 25 50 75 100 125 150 1750

0.2

0.4

0.6

0.8

1

% o

f dea

d no

des

Epoch

(b) UDC−medium (TH=6 T

L=6)

0 25 50 75 100 125 150 1750

0.2

0.4

0.6

0.8

1

% o

f dea

d no

des

Epoch

(c) UDC−low (TH=2 T

L=2)

0 25 50 75 100 125 150 1750

0.2

0.4

0.6

0.8

1

% o

f dea

d no

des

Epoch

(d) NADC (TH=12 T

L=2)

level 1−2 (105) level 3−4 (286) level 5−6 (440) level >7 (1217)

Figure 4.6: Percentage of dead nodes in areas in different levels of the network. The parenthesized number representsthe total number of sensor nodes in each area.

(i.e. levels higher than 7), there have been more than 80% of nodes dying in the central area (i.e. within the first two

levels close to the base station). When there are not enough alive nodes in the inner levels of the network to maintain

the routing paths to the base station, the curves representing the inner levels stop rising because few event packets can

be propagated to the central area.

The unbalanced energy consumption can be alleviated if the overhearing waste can be reduced in the areas

with high communication activity. This can be achieved by either uniformly reducing the density in the network (as

UDC-low does) or selectively reducing the density along therouting paths (as NADC does). We can see in Fig. 4.6

that NADC and UDC-low successfully reduce the unnecessary overhearing energy consumption in the central area of

the network, and hence extend the lifetime of the nodes closeto the base station.

4.4.3 System Performance and Lifetime

As nodes’ limited energy budget are drained and they stop functioning, the network will eventually cease

to be usable. We refer to the length of the time that the network operates prior to becoming unusable as thenetwork

lifetime. A formal definition of network lifetime is not straightforward and may depend on the application scenario

in which the network is used. In the literature, network lifetime has often been defined as the time for the first node

or a certain percentage of nodes to die [25], or as the time forthe first network partitioning to occur [13]. However,

it is usually difficult to know the status of all the sensor nodes in a practical sensor network environment. According

to the simulation results in the the previous section, nodesclose to the base station tend to deplete their energy faster

than other nodes, and result in an energy hole in the central area of the network. Once the number of alive nodes in the

central area can not maintain good connectivity from outer nodes to the base station, few events can be successfully

delivered. Therefore, an easy way for the base station to define the lifetime of the network is to use the event delivery

rate or the number of alive neighbors of the base station.

Fig. 4.7 shows four different lifetime indications of the simulation:a) number of alive base station neigh-

bors, b) number of delivered events to the base station, c) total number of dead nodes, andd) total number of detected

events in the network. While the number of alive base station neighbors and the number of delivered events can be

directly obtained in the base station, the total number of dead nodes and detected events require the knowledge of the

whole network.

As expected, the dead nodes soon form a bottleneck of communication to the base station. Although the total

52

0 25 50 75 100 125 150 1750

5

10

15

20

25

30(a) # BS neighbors

Epoch0 25 50 75 100 125 150 175

0

200

400

600

800

1000

1200(b) Total # events received by BS

Epoch0 25 50 75 100 125 150 175

0

200

400

600

800

1000

1200

1400(c) Total # dead nodes

Epoch0 25 50 75 100 125 150 175

0

500

1000

1500

2000

2500(d) Total # detected events

Epoch

UDC−high UDC−medium UDC−low NADC

Figure 4.7: Four different lifetime indications of the simulation.

Table 4.4: Statistics of the nodes’ energy usage in Fig. 4.8Energy usage UDC-high UDC-medium UDC-low NADC

Total 3531 2696 2984 2456

Average 1.72 1.31 1.46 1.2

Standard deviation 3.04 1.89 2.06 1.73

Maximum 33 15 15 13

number of detected events keeps increasing, the delivery rate decreases dramatically due to the energy hole problems.

However, the energy hole problem may not necessarily first occur on the nodes closest to the base station. Some nodes

farther away may deplete their energy sooner and affect the event delivery, and the base station may stop receiving

events packets even when it still has alive neighbors.

Compare NADC with the other three UDC settings, NADC has longer system lifetime than UDC-high

and UDC-medium, and achieves similar system lifetime as UDC-low. However, UDC-low trades off delay of event

delivery for lower energy consumption. The tradeoff will beexplored in the following sections.

4.4.4 Unbalanced Energy Usage

To illustrate the unbalanced energy usage among sensor nodes, we randomly choose 20 source nodes, which

generate event packets simultaneously for one epoch, record the timing of event delivery, and measure the energy

consumption of individual sensor nodes. Fig. 4.8 shows the number of nodes with respect to different amount of

energy consumption. The corresponding statistics of the nodes’ energy usage is shown in Table 4.4. The result shows

that UDC-high has the most unbalanced energy usage among sensor nodes, where a few nodes consume much more

energy than others. NADC not only reduces the total energy consumption by avoiding unnecessary overhearing, but

also alleviates the unbalanced energy usage. We can see in the result that NADC has the smallest standard deviation

of the nodes’ energy usage; that means NADC has the most balanced energy usage among sensor nodes compared to

the other three schemes.

Although UDC-low maintains the lowest active node density compared to other schemes, which implies less

overhearing waste, it still has higher total energy consumption compared to NADC and UDC-medium. The cause is the

bad connectivity between source nodes and the base station that results in more re-routed packets or re-transmissions.

The phenomena can also be observed in Fig. 4.9, which shows the timing of the event delivery to the base station. Due

to the bad connectivity among sensor nodes, UDC-low takes much longer time for the base station to receive all the

events. However, the delay of event delivery relates not only to the active node density, but also the distance from the

53

Figure 4.8: Distribution of sensor nodes with different energy consumption.

0 10 20 30 40 50 60 700

2

4

6

8

10

12

14

16

18

20

Epoch

Tot

al #

eve

nts

rece

ived

by

BS

UDC−highUDC−mediumUDC−lowNADC

Figure 4.9: The timing of event delivery from 20 randomly chosen source nodes in the network. Each source sendsone event at time 0.

source nodes to the base station and the mobility model of sensing targets. The issue will be further discussed in the

next section.

4.4.5 Delay of Event Delivery

The delay of event delivery is dominated by the connectivityfrom the source nodes to the base station. If

the density of active nodes is not high enough to maintain theconnectivity, the forwarding nodes have to re-route the

packets or even temporarily hold the event data until a new route is available.

To further explore the impact of different density control settings on the delay of event delivery, we explicitly

control the source nodes to be a fixed number of hops away from the base station, so that no extra source nodes will

show up along the routing paths and increase the active node density. That means the active node density along routing

paths will be determined only by the NADC threshold values, without the interference from the mobility of sensing

targets. In this set of simulations, 10 source nodes are randomly chosen in level 4, level 8, and level 12 respectively,

and the source nodes generate event packets simultaneouslyfor 6 consecutive epochs. We measure the time from the

generation of the first event to the successful delivery of all 60 events as an indication of delivery delay. We run each

54

0

10

20

30

40

50

60

70

80

Del

ay o

f Eve

nt D

eliv

ery

(epo

ch)

UDC-high UDC-medium UDC-low NADC

level4level8

level12

Figure 4.10: The difference in the timing of event delivery from source nodes in different hops away from the basestation.

setting five times and take the average as the result.

Fig. 4.10 shows the measured results with different densitycontrol settings. When the source nodes are in

level 4, which is relatively close to the base station, the difference in delay is not obvious among the UDC and NADC

settings. However, when the source nodes are farther away from the base station, the connectivity from the source

nodes to the base station has decisive impact on the delay of event delivery. When the source nodes are in level 12, the

average time for UDC-low to deliver all the events is more than twice compared to UDC-high. NADC, on the other

hand, has far less increase in delay compared to UDC-low due to its capability of maintaining better connectivity from

the source nodes to the base station.

4.4.6 NADC with AAT

In the above simulations, we use an SPT-based approach to construct the routing tree. That means each

node chooses its parent based on the number of hops away from the base station, without considering the aggregation

opportunity. This simple approach can effectively reduce the length of routing paths by trying to forward the packets

through the minimum number of hops. In Section 3.2, however,we have shown that Adaptive Aggregation Tree (AAT)

can significantly reduce the number of transmissions by dynamically transforming the routing tree structure. Since

both AAT and NADC require only local information obtained through overhearing, these two optimization schemes

can be easily applied together without increasing the complexity of the system.

Using the same simulation settings as in Section 4.4.1, Fig.4.11 shows the four lifetime indications when

both NADC and AAT are applied on the network. Similar to the results in Fig. 4.7, NADC achieves similar lifetime

compared to UDC-low. Fig. 4.12 explicitly compares NADC andNADC with AAT (NADC-AAT). While the total

number of delivered events is about the same due to the energyhole problem, NADC-AAT has a smaller number of

dead nodes and hence more detected events due to a higher opportunity of in-network aggregation, which results in

a smaller amount of packet transmissions. However, the difference is not large, because the number of source nodes

in each epoch is relatively small (about 10 source nodes in each epoch). That means the routing paths are usually not

close enough for AAT to merge.

Similar to the previous section, we explicitly control the source nodes to be a fixed number of hops away

55

0 25 50 75 100 125 150 1750

5

10

15

20

25

30(a) # alive BS neighbors

Epoch0 25 50 75 100 125 150 175

0

200

400

600

800

1000

1200(b) Total # events received by BS

Epoch0 25 50 75 100 125 150 175

0

200

400

600

800

1000

1200(c) Total # dead nodes

Epoch0 25 50 75 100 125 150 175

0

500

1000

1500

2000

2500(d) Total # detected events

Epoch

UDC−high−AAT UDC−medium−AAT UDC−low−AAT NADC−AAT

Figure 4.11: Four lifetime indications of sensor networks with Adaptive Aggregation Tree accommodated.

0 25 50 75 100 125 150 1755

10

15

20

25

30(a) # alive BS neighbors

Epoch0 25 50 75 100 125 150 175

0

200

400

600

800

1000

1200(b) Total # events received by BS

Epoch0 25 50 75 100 125 150 175

0

100

200

300

400

500

600

700

800(c) Total # dead nodes

Epoch0 25 50 75 100 125 150 175

0

500

1000

1500

2000

2500(d) Total # detected events

Epoch

NADC−AAT NADC

Figure 4.12: The comparison of NADC and NADC with AAT.

from the base station to analyze the delay of event delivery.Fig. 4.13 shows the average of five simulation runs, where

NADC-AAT has lower delivery delay on average, regardless ofthe location of the source nodes. The result is contrary

to what we observed in Fig. 3.10, where AAT has longer delay due to longer routing paths. The reason is that in Fig.

3.10, all the nodes are well connected and event packets are rarely re-routed, so the delay is directly related to the

length of the original routing paths. With NADC, however, the nodes may not be well connected, especially in the area

near routing paths where nodes aggresively turn off the radio to avoid overhearing waste. In this scenario, the packets

may have to be re-routed (or temporarily held by intermediate nodes) several times before arriving the base station.

Thus the delivery delay is dominated by the number of times packets are re-routed. Since the number of re-routed

packets is related to the total amount of in-network traffic,the capability of AAT to reduce in-network traffic reduces

the number of re-routed packets, resulting in shorter delivery delay.

0

10

20

30

40

50

60

Del

ay o

f Eve

nt D

eliv

ery

(epo

ch)

NADC NADC-AAT

level4level8

level12

Figure 4.13: Comparison of delivery delay from source nodesin different hops away from the base station.

56

4.5 Summary

In this chapter, we propose neighborhood-aware density control to reduce the unnecessary overhearing en-

ergy consumption along routing paths. Through analysis andsimulations, we show that overhearing is the main cause

of the energy hole problem which severely reduces the effective system lifetime.

In NADC, nodes observe their neighborhoods and dynamicallyadapt their participation in the multihop

network topology. Since the neighborhood information can be easily observed through the overheard information, the

density in different regions can be adaptively adjusted in atotally distributed manner. By reducing the node density

near the routing paths while keeping the nodes involved in packet generation or forwarding in the active state, the

overhearing waste can be reduced without dramatic increasein delay.

We run the simulation with four different density control settings: UDC-high, UDC-medium, UDC-low, and

NADC. Although UDC-high has the lowest delay in event delivery, the high density of active nodes results in large

amount of unnecessary overhearing along the routing paths and quickly forms an energy hole near the base station.

UDC-low, on the other hand, alleviates the extremely unbalanced energy usage problem, but incurs high increase in

the delay of event delivery, especially when source nodes are far away from the base station. The ability of NADC

to adjust the node density in different regions successfully alleviates the unbalanced energy usage while keeping the

increase of delay in a reasonable range. Our simulation results show that NADC achieves similar system lifetime as

UDC-low with much less increase in the delay of event delivery.

57

Chapter 5

Hot-spot Avoidance through

Load-balancing

In previous chapters, we have seen that MAC layer and networklayer optimizations can significantly reduce

unnecessary energy consumption and alleviate workload imbalance in sensor networks. However, even with “perfect”

MAC and network protocols which have no unnecessary energy consumption at all, the intrinsic characteristic of

WSN applications may still result in unbalanced energy consumption among sensor nodes and have negative impact

on the system lifetime. In this chapter, we use data-centricstorage as an example to illustrate how application level

optimization can help alleviate the unbalanced energy consumption and prolong the system lifetime.

Researchers have recognized that data-centric storage is an efficient scheme to store and retrieve data in

sensor networks [67]. When events occur, the sensed data is sent to and stored at some home nodes determined by the

name of the data. Such an approach enables efficient data access: given the name of a data item, one can retrieve the

data without flooding the query. However, with the multihop routing nature of sensor networks, the traffic of the home

nodes and their neighboring nodes tends to be much higher than other nodes [33]. These hot-spots will drain off their

limited energy rapidly and adversely impact the lifetime ofthe overall system.

Therefore, we propose Zone Repartitioning (Z-R) to alleviate the hot-spot problems. When the event fre-

quency in certain areas is higher than a threshold, we split the corresponding zone into several sub-zones, each with

its own temporary home node, and events that occur afterwardare stored in one of the temporary home nodes. After

the event frequency becomes low, the sub-zones merge into the original zone, and the stored events in the temporary

home nodes are sent to the original home node. In this way, theload of the original hot-spots during the split-merge

time period is distributed to those temporary home nodes andtheir neighbors. Another benefit of this approach is that

it trades the cost of event access for the cost of event storage. Because transmitting events to the temporary home

node requires fewer hops on average than to the original homenode, the cost of storing events is reduced during the

split-merge time period. Although the cost of accessing events becomes higher because the queries have to be sent to

58

more nodes, this approach can still be beneficial because thecost of storing events is more important while the event

frequency is high.

5.1 Background and Related Work

In most communication networks, naming of nodes for low-level communication leverages topological in-

formation, and the communication mechanisms rely on addressing individual nodes. This is appropriate when each

end system is usually associated with a distinct user and most traffic is point-to-point. In a sensor network, however,

the nature of the data is more important than the identity of the node that gathers it. Researchers have found it useful

to move away from the traditional point-to-point communication abstraction and instead adopt abstractions that are

more data-centric [67, 37, 30]. In this section we give a brief overview of the data dissemination approaches in sensor

networks, describe the concept of data-centric storage, and introduce some extensions to data-centric storage systems.

5.1.1 Data-Centric Storage

Since the nature of the data is more important than the identity of the nodes in sensor networks, data-centric

models have been proposed for sensor networks, in which the sensor data elements (as contrasted to sensor nodes)

are named, based on attributes such as event-type or geographic location. In particular, data-centric routing [37] and

data-centric storage [67] have been shown to be energy-efficient in a sensor network scenario.

In data-centric routing, routing decisions are based on thename(s) associated with data. For example,

in Directed Diffusion [30], each node matches a data item with forwarding state created by interest messages before

deciding which neighbor to send the data packet to. In data-centric storage [67], on the other hand, data items are stored

and retrieved by name. Generally speaking, a data-centric storage system provides primitives of the formput(key, data)

andget(key), where key is derived from the name (or type) of the data. There are many ways to implement data-centric

storage. Efficient implementations usually involve an algorithmic mapping between a data name and a “location”,

which can be a network node, a precise geographic location, or an identifier in a key space. Through the algorithmic

mapping, nodes can retrieve data efficiently knowing only the name of the data. More precisely, unlike data-centric

routing systems, retrieving a data item does not require flooding of queries. This property can be used to design a class

of systems that are complementary to data-centric routing systems.

To further precisely specify what data gets stored in a data-centric storage system, it helps to introduce some

terminology [83]. Individual sensor nodes generate low-level observations. Examples of such observations include a

sequence of temperature readings, or the readings from an accelerometer. Observations can be combined or aggregated

to produce eithereventsor features. An event is a semantically-rich description of a physical phenomenon. Examples

of events include “a tank went through this intersection,” or “a bird alighted upon this nest.” By contrast, a feature

is a geometric or statistical pattern in the observations generated by one or more sensor nodes, perhaps across time.

Examples of features include “the average temperature reading in the last 10 minutes,” or “the total number of cars in

the parking lot.” (In what follows, we use the termeventto encompass both events and features.)

Once an event has been identified, the event data can be handled in a number of different ways. For example,

a node could be instructed to send event information to external storage, to store the event information locally, or to

59

use data-centric storage. We consider these three canonical approaches [67]:External Storage (ES)in which all event

data is stored at an external storage point for processing,Local Storage (LS)in which all event information is stored

locally at the detecting node, andData-Centric Storage (DCS)in which all event data is stored by event-type within

the sensor network at designated nodes. We use the number of messages as the metric of communication cost, and

show the comparison of these approaches in Table 5.1, whereN denotes the number of sensor nodes,Dtotal the total

number of events detected,Q the number of queries issued, andDq the number of events detected for the queries. The

asymptotic cost of a flood isO(N) and that of direct routing from one random node to another isO(√

N).

Table 5.1: Communication cost of External Storage (ES), Local Storage (LS), and Data-Centric Storage (DCS)

Total Hot-spot

ES Dtotal

√N Dtotal

LS QN + Dq

√N Q + Dq

DCSQ√

N + Dtotal

√N + Dq

√N (list)

2Q√

N + Dtotal

√N (summary)

Q + Dq (list)2Q (summary)

It is easy to see the performance trade-offs exhibited by these different alternatives. For external storage,

the cost of accessing events is zero, since all events are available at one node. However, the cost of conveying data to

this external node is non-trivial; there is an energy cost incommunicating events to this node, and significant energy

is expended at nodes near the external node in receiving all these events (these nodes become hot-spots). If events are

accessed far more frequently than they are generated, external storage might be an acceptable alternative.

At the other end of spectrum, local storage incurs zero communication cost in storing the data, but incurs a

large communication cost — a network flood — in accessing the data. Local storage may therefore be feasible when

events are accessed less frequently than they are generated.

Data-centric storage lies somewhere in between, and it becomes more preferable as the size of the network

increases, or when many more events are detected than can be usefully queried. Two different kinds of queries can be

used in DCS:list indicates a full listing of events is returned (requiring a packet for each event) andsummaryindicates

only a summary of events is returned (requiring only one packet). The performance advantage further increases if

summaries are used. Thus, data centric storage becomes an attractive alternative as sensor networks scale.

5.1.2 Geographic Hash Table

Our discussion has introduced data-centric storage as a concept, without describing the specifics of a partic-

ular instance of a data-centric storage system. In this section, we describe a system called a Geographic Hash Table

(GHT) [67], which represents one implementation of data-centric storage. GHT’s mechanisms are powerful in that

they can be re-used to design a variety of other distributed data structures in sensor networks.

5.1.3 GHT Scaling: Structured Replication

GHT supports the interface provided by data-centric storage in two steps: it first uses the GPSR (Greedy

Perimeter Stateless Routing) [33] routing algorithm for low-level routing, and then uses an approach borrowed from

60

the peer-to-peer lookup algorithms [80, 66, 99, 70] to builda distributed hash-table (DHT) on top of GPSR. On top

of GPSR, DHT provides the lookup service by hashing the name of an event to a key, which is a location somewhere

in the sensor network. Therefore, based on the functionality provided by GPSR and DHT, GHT can store or retrieve

event data at the node closest to a specified location, which we call thehome nodefor that event type.

The basic version of GHT has all events of the same type storedin a single home node. If an event type

has too many events detected, the home node and its neighboring nodes could become hot-spots. To overcome this

problem, structured replication hierarchically decomposes the geographical region enclosing the sensor network in a

manner shown in Figure 5.1. In structured replication, the rectangular or square boundary encompassing the sensor

network is split into4d equally sized sub-regions, whered is the depth of the replication. Consider an event whose

home node isr; we call that the root of an event. For a given rootr and a given hierarchy depthd, every node can

compute several mirror images in the sub-regions, and a nodethat detects an event now stores the event at the mirror

closest to its location. In this way, the root is no longer theonly hot-spot and the event storage cost (i.e. the cost of

conveying event data to the storage node) is reduced becauseof the shorter distance between the detecting node and

the storage node.

Figure 5.1: Structured Replication

Contrary to what the name might suggest, structured replication does not replicate data at multiple nodes;

rather, the home node for an event is now replicated in several sub-regions to alleviate hot-spots. However, queries

for a particular event now have to be directed to all the mirrors. When the number of queries increases, the query cost

may be a big problem.

5.1.4 Resilient Data-Centric Storage

Resilient Data-Centric Storage (R-DCS) [24] is another extension of GHT to achieve scalability and re-

silience by replicating data at strategic locations in the sensor network. In R-DCS, each sensor node operates in one

of three possible modes: monitor mode, replica mode, or normal mode. The whole sensor network is partitioned into

zones, each with exactly one monitor node. The monitor node storesand exchanges information in the form of a

monitoring map, which includes the control and summary information for each event type. A monitor node can also

be a replica node, which actually stores event data for the given event type. When events occur, the event data are sent

to the monitor node in its zone and then forwarded to the closest replica node if necessary. The monitor nodes also

generate and exchange monitoring maps (meta-data of the events) periodically to form a global image of all events

occurred in the network. Through the periodic information exchange between monitor nodes, queries only need to

be routed to the closest monitor node, and overall query traffic is reduced. Simulation results [24] show that R-DCS

61

presents an intermediate solution between LS (free storage, expensive queries) and DCS (both at moderate cost), and

has good performance whenDq � Q.

Since the zone partitioning of R-DCS is fixed, although it reduces the storage cost without increasing the

query cost, the monitor nodes of certain zones may still become hot-spots when the event frequency in these zones is

much higher than the others. If the partitioning of zones candynamically change according to the event frequency, the

load of these hot-spots can be further distributed to other nodes.

5.2 Mechanisms for Zone Repartitioning

In a data-centric storage system, the communication energyconsumption has two main components: query

cost and storage cost. Query cost represents the energy consumption of sending query messages to the home nodes

and sending the query responses back to the querier. Storagecost represents the energy consumption of conveying

the event data to the home nodes. When the event frequency is high, the storage cost would be the dominant term

of the total energy consumption because more and more event data are travelling to home nodes. In such conditions,

increasing the number of home nodes not only reduces the storage cost but also alleviates the load of the original home

nodes. However, as in the case of structured replication, higher number of home nodes also represents higher query

cost. When the event frequency decreases, the query cost mayexceed the storage cost and become the dominant term

of the total energy consumption. At these moments, decreasing the number of home nodes reduces the query cost.

Therefore, Z-R dynamically changes the number of home nodes(by changing the number of zones) to reduce the total

communication cost and alleviate the hot-spot energy consumption.

There are two basic operations in Z-R:split andmerge.

Split When the event frequency of a zone is higher than a threshold (Split_Threshold), we divide the zone into several

smaller sub-zones; each has its own home node (temp home node). Since these sub-zones have a smaller size,

which implies a shorter distance from the detecting node andthe temp home node, it usually takes fewer hops

for an event to reach the storage node. The storage cost is therefore reduced, and the load of the original home

node is now distributed to the temp home nodes.

Merge Although using smaller sub-zones reduces the storage cost and distributes the load, the query cost is increased

because the queries now have to be sent to all the home nodes and temp home nodes. Therefore, when the event

frequency of the sub-zones is lower than a threshold (Merge_Threshold), we merge the sub-zones back into the

original zone, and the cost of subsequent queries is reduced.

The whole split-merge process is illustrated in Figure 5.2 and is detailed as follows:

1. The home node checks the event frequency periodically. When the event frequency is higher than Split_Threshold,

it sends a broadcast message to all the nodes within its zone,indicating a split process. The broadcast message

contains the locations of temp home nodes. The original homenode also notifies the temp home nodes by

sending additional unicast messages.

62

Figure 5.2: Split-merge process

2. The nodes which receive the broadcast message choose the closest temp home node as its new home node,

where subsequent events are sent.

3. Events are sent to the temp home nodes, and each temp home node periodically sends a status message to the

original home node, indicating the event frequency in its sub-zone. However, queries are still sent to the original

home node. For summary queries, the original home node will retrieve the required information from temp

home nodes and then reply with the summary. For list queries,the queries will be forwarded to the temp home

nodes, and temp home nodes will reply directly.

4. The original home node keeps monitoring the status of the sub-zones (through status messages). If the event fre-

quency of all the sub-zones is lower than Merge_Threshold, the original home node sends a broadcast message

to all the nodes within its zone, indicating a merge process.It also notifies the temp home nodes by additional

unicast messages.

5. The nodes which receive the broadcast message change their home node, from temp home nodes back to the

original home node. The temp home nodes then send the stored events back to the original home node. The

stored events can be sent in a condensed form, through compression, packet merging, or data aggregation[17],

to reduce the corresponding energy consumption.

Although the load is distributed and the cost is reduced in the time period between split and merge, the whole process

requires two additional broadcasts and some unicasts. Since broadcasts are expensive operations in sensor networks,

we did a qualitative analysis and detailed simulations to further examine the feasibility of our scheme.

5.3 Analytical Results

In this section, we derive analytical expressions for energy costs and savings obtainable with Z-R. We

consider a zone withN nodes.Dtotal denotes the total number of events detected,Q the number of queries issued,Dq

the number of events detected for these queries,S the number of sub-zones after a zone splits, andK the compression

ratio of event data.

The communication costs can be estimated using the fact thatthe asymptotic cost of a flood isO(N) and

that of direct routing from one random node to another (usingGPSR) isO(√

N). Table 5.2 shows the comparison of

Z-R against GHT. The presented formulae give the expected values of these quantities.

We can see that the storage cost is reduced at the expense of a higher query cost and an additional cost for

split and merge processes. SinceDtotal � Dq � Q in typical sensor networks [24], we neglect the query cost to

63

Table 5.2: Estimated total energy consumed by GHT and Z-R

GHT Zone Repartitioning

split N

event storage Dtotal

√N Dtotal

q

NS

list query Q√

N+Dq

√N Q

√N + SQ

√N + Dq

√N

summary query 2Q√

N 2Q√

N + 2SQ√

N

merge N +Dtotal

K

√N

simplify our analysis, and find that the total cost depends onS andK. The relationship between the total cost and the

value ofS andK is as follows:

• If S = 4, K must be greater than2 to achieve lower cost.

• If S = 9, K must be greater than1.5 to achieve lower cost.

• If S = 16, K must be greater than1.33 to achieve lower cost.

Because modern compression techniques can easily compresssensor signal data to a factor of8 with a good SNR [83],

we expect that Z-R can not only distribute the load of hot-spots, but may also reduce the total cost of communication.

5.4 Performance Evaluation

In this section, we present our simulation results. We first describe our simulation methodology, explain the

calculation of energy consumption, and then compare the performance of Z-R against other schemes.

5.4.1 Methodology

In previous chapters, we evaluate the energy consumption assuming that overhearing a packet consumes the

same energy as receiving a packet. The assumption is true forMAC protocols, such as B-MAC, that require nodes

to receive the whole packet before discarding useless ones.However, various MAC protocols have been proposed to

avoid overhearing unintended packets. For example, S-MAC exploits RTS-CTS packets to avoid receiving the whole

unintended data packet, and PAMAS [79] uses a secondary signalling radio to avoid receiving unnecessary packets.

For such MAC protocols, the overhearing energy consumptioncan be significantly reduced, and may account for only

a small portion of the total energy consumption. To take suchMAC level optimization into consideration, we assume

adjustable transmission power, and use the energy consumption of sending/receiving packets as the indication of total

energy consumption of sensor nodes.

In Z-R, we distribute the communication cost in order to maximize the lifetime of the entire network. As

discussed on Section 2.1.3, the power consumed by a radio hastwo main components [25]:

1. An RF component that depends on the transmission distanceand modulation parameters.

64

2. An electronics component that accounts for the power consumed by the circuitry that performs frequency syn-

thesis, filtering, up-converting, etc.

Since the actual energy consumption is not linearly proportional to the transmission power, to accurately model the

power consumption in communication, we used Mica2 [18] motes as the sensor nodes in our simulation and used its

specification as a reference to calculate the actual consumed energy.

When a node tries to send out a packet for a certain distanced, we first use a large-scale path loss model

to get the minimum transmission power required,Pt. Then we usePt to estimate the actual consumed energy, as

described below.

5.4.2 Calculation of Energy Consumption

Since Mica2 has a constant current consumption (7.4mA) while receiving, it is easy to get the corresponding

energy consumption of receiving. As for the transmission part, since the radio on Mica2 can be adjusted for a range

of output power levels, we derive the minimum power requiredfor a certain transmission range by consulting the

datasheet of Chipcon CC1000 [16], the single-chip RF transceiver in Mica2.

In typical wireless communication, the transmission powerand the receiving power have the relationship

shown in equation 5.1, whereα is the path loss exponent.

Pr =K × Pt

dα(5.1)

Since the receive sensitivity of Mica2 is−100dBm and the maximum transmission power is10dBm, we can

get the minimum power required for reception and the maximumtransmission power:

{

−100dBm = 10 log10 Pr

10dBm = 10 log10 Pt

⇒{

Pr = 10−10mW (min power required for Rx)

Pt = 10mW (max Tx power)

Assuming that the maximum outdoor communication range of Mica2 (1000ft) is achieved by using the

maximum transmission power, from equation 5.1, we have

10−10 =K × 10

1000α

65

For α = 3 (α is between 2 and 4 in typical outdoor environments), we can get K = 10−2. So we have the

relationship betweenPr andPt :

Pr = 10−2 × Pt

d3(5.2)

Using equation 5.2 and the receive sensitivity of Mica2, we can get the minimum transmission power re-

quired for a distanced.

Pt = 100Pr × d3mW = 10 log10(10−8 × d3)dBm

After knowing the minimum transmission power required, we can get the corresponding current consump-

tion from the CC1000 datasheet [16], and therefore get the energy consumption of transmission.

5.4.3 Energy Consumption in Broadcast

In a broadcast task, a message originating from a source nodeneeds to be forwarded to all the other nodes in

the network. Because of the multihop routing nature in sensor networks, a broadcast usually results in a large amount

of packet forwarding and consumes a lot of energy. Several protocols for energy-efficient broadcast communications

have been proposed to solve the problem. However, most of these solutions are globalized, meaning that each node

needs global network information, and cannot be applied to Z-R, in which each node only knows the information of

its neighbors. Therefore, a localized protocol is requiredto reduce the energy consumption of broadcasts in Z-R.

RBOP (RNG broadcast oriented protocol) [11, 10] is an energy-efficient broadcast protocol where nodes

require only local information about their neighbors. It minimizes the total energy consumption of a broadcast process

by using a neighbor elimination scheme [57, 81] to reduce thetransmission range of each node while maintaining the

full coverage of the network. Previous research [11] shows that RBOP is competitive with globalized BIP (Broadcast

Incremental Power) protocol or minimum spanning tree basedprotocol. Therefore, instead of blind flooding, we use

RBOP as the broadcast mechanism of Z-R.

When a node tries to send or relay a broadcast packet, we use RBOP to figure out the minimum transmission

range, and use the same approach as in Section 5.4.2 to derivethe corresponding energy consumption.

5.4.4 Simulation Environment Setting

We ran the simulation on TinyOS Simulator (TOSSIM) [40]. Thesettings of the simulation environment are

shown in Table 5.3. To simplify the simulation, we assume that the sizes of all packets are the same. We also ignore

the energy consumption of all control packets, such as beacon packets and synchronization packets.

In our simulations, 400 sensor nodes are scattered over a 5000ft×5000ft field on a grid-based distribution

with some random displacement. Because the maximum radio range of Mica2 nodes is 1000ft, each node would have

66

Table 5.3: Simulation settings

Simulation time 10min, 30min

Number of nodes 400

Field range 5000 ft× 5000 ft

Node distribution Grid-based + random displacement

Max radio range 1000 ft

Event freq. per nodefH : 1

50– 1

240event/second

fL: 12000

event/second

Total # queries 0 – 19

# sub-zones in split 4

Split Threshold 20 events during sampling period (6 sec)

Merge Threshold 2 events during sampling period (6 sec)

Data compression ratio 7

at least one neighbor in every direction and a fully-connected network in established. The whole sensor network in

divided into four and sixteen equally sized zones (GHT-4 andGHT-16). Every sensor node triggers events randomly,

and one of the zones (hot zone) has a higher event frequency (fH), which is about 10 times higher than the others. We

also simulate the impact of queries by randomly selecting a number of querying nodes. The total number of queries

vary from 1 to 19, which is much less than the total number of events.

For the simulation of Z-R, we set the Split_Threshold and Merge_Threshold such that the hot zone splits and

merges only once. The home node of the hot zone would exceed the Split_Threshold quickly and split the zone into

four sub-zones. We issue an additional merge process at the end of the simulation to take the corresponding energy

consumption into account.

5.4.5 Load Distribution of Hot-Spots

To show the load balancing capability of Zone Repartitioning, we simulate a scenario that some phenomena

occur in the sensor field, and hence certain areas have much higher event frequency in the phenomena duration. We

run the simulation for 30 minutes, with event frequencyfL = 12000 event/second, and one of the zones has higher

event frequencyfH = 1150 during the middle ten-minute period. The total number of queries is10. Simulation results

are shown in Figure 5.3, in which each grid point represents the energy consumption of a node in the network.

0

500

1000

1500

2000

2500

3000

3500

0 500

1000 1500 2000 2500 3000 3500

GHT-4

0

500

1000

1500

2000

2500

3000

3500

0 500

1000 1500 2000 2500 3000 3500

GHT-16

0

500

1000

1500

2000

2500

3000

3500

0 500

1000 1500 2000 2500 3000 3500

Z-R

Figure 5.3: Energy consumption of each sensor node in GHT-4,GHT-16, and Z-R

In GHT-4, the home node of the hot zone and its neighbors are the hot-spots, which consume a lot more

67

energy than the other nodes because all the events occur in the hot area are routed to the only one home node. In

GHT-16, because there are four home nodes in the hot area, theevents are stored in the closest one of the four home

nodes and the load is distributed.

Although the load of hot-spots is distributed with more zones, the energy consumption of the other nodes

increases because of the higher query cost. Queries have to be routed to all the home nodes. Z-R overcame the problem

by splitting the hot zone into four sub-zones and keeping theother zones unchanged. Comparing the result of Z-R

against GHT-4 and GHT-16, we can see that the load of the hot-spots was distributed without increasing the energy

consumption of other nodes.

Table 5.4 lists the hot-spot and total energy consumption ofthese three schemes in our simulation, in which

Z-R has the lowest hot-spot energy consumption as well as total energy consumption. The result shows that by

repartitioning the zones dynamically, Z-R not only distributes the load of hot-spots, it also reduces the total energy

consumption through the trade-off between event storage cost and query cost.

Table 5.4: Hot-spot and total energy consumption

GHT-4 GHT-16 Z-R

Hot-spot 3678 1476 1446

Total 74841 86136 72767

5.4.6 Relationship between Event Frequency, Number of Queries, and Simulation Time

Since both event frequency and the number of queries influence the energy consumption of the schemes, it

is important to consider the ratio of query traffic to event-detection traffic while designing a sensor network. GHT-4

prefers scenarios with lower event frequency and more queries because of lower query cost, while GHT-16 prefers

higher event frequency and fewer queries because of lower event storage cost. We changed these parameters in our

simulations to show that by dynamically changing the numberof zones, Z-R adapts between GHT-4 and GHT-16.

Figure 5.4 shows the total energy consumption of two sets of simulations, 10 and 30 minutes respectively,

with different hot area event frequency,fH , and the number of queries,Q. As we predicted in Section 5.2, GHT-4

performs better whenQ is high andfH is low, while GHT-16 performs better whenQ is low andfH is high. Z-R lies

somewhere in between according to the value ofQ andfH . We observe the following:

1. WhenfH andQ are both low, Z-R has the highest total energy consumption inthe 10-minute simulation because

of the two additional broadcasts (i.e. split and merge), butin the 30-minute simulation, Z-R performs better than

GHT-4 because the additional broadcast cost is alleviated by the reduced event storage cost.

2. WhenfH is low andQ is high, the query cost is the dominant component in the totalenergy consumption. In

this situation, GHT-4 performs better than GHT-16, and the performance of Z-R is very close to GHT-4.

3. WhenfH is high andQ is low, the event storage cost becomes the dominant component. In this situation,

GHT-16 performs better because of the lower event storage cost, and the longer the simulation runs, the more

the difference between GHT-4 and GHT-16. The curve of Z-R rises slower than GHT-4 but faster than GHT-16.

68

sim time: 10min

GHT-4GHT-16

Z-R

024681012141618

Q

5080

110140

170200

230 1/fH

20000

40000

60000

80000

100000

120000

140000

160000

180000

sim time: 30min

GHT-4GHT-16

Z-R

024681012141618

Q

5080

110140

170200

230 1/fH

50000

100000

150000

200000

250000

300000

350000

Figure 5.4: Total energy consumption with varyingfH andQ (fL = 12000 )

4. WhenfH andQ are both high, the simulation time decides which scheme has better performance, and Z-R

always stays between GHT-4 and GHT-16.

Figure 5.5 and 5.6 show the comparison of these three schemesin different simulation settings. WhenfH

andQ are low, GHT-16 is apparently the best choice because of its low storage cost. But with the increase offH and

Q, it is not clear which scheme is the better one. In these situations, the adaptability of Z-R keeps the total energy

consumption close to the better one and makes it a competitive choice.

In our simulations, we set the parameters such that Z-R always splits at the beginning of the simulation and

merges at the end. But as the results indicate, always havinga split-merge process may not always be the best choice

— sometimes it results in the highest total energy consumption comparing to other schemes. Having the parameters

fine-tuned according to different applications would further increase the performance of Z-R.

69

20000

40000

60000

80000

100000

120000

6080100120140160180200220240

1/fH

10min #query = 0

GHT-4GHT-16

Z-R

20000

40000

60000

80000

100000

120000

6080100120140160180200220240

1/fH

10min #query = 5

GHT-4GHT-16

Z-R

20000

40000

60000

80000

100000

120000

6080100120140160180200220240

1/fH

10min #query = 10

GHT-4GHT-16

Z-R

Figure 5.5: Total energy consumption in 10-minute simulations

50000

100000

150000

200000

250000

300000

350000

6080100120140160180200220240

1/fH

30min #query = 0

GHT-4GHT-16

Z-R

50000

100000

150000

200000

250000

300000

350000

6080100120140160180200220240

1/fH

30min #query = 5

GHT-4GHT-16

Z-R

50000

100000

150000

200000

250000

300000

350000

6080100120140160180200220240

1/fH

30min #query = 10

GHT-4GHT-16

Z-R

Figure 5.6: Total energy consumption in 30-minute simulations

5.5 Summary

In this chapter, we described how Zone Repartitioning alleviates the hot-spots in data-centric storage sys-

tems. Z-R distributes the communication load of hot-spots to other nodes when the event frequency of certain areas

is much higher than the others. Its dynamic tradeoff betweenevent storage cost and query cost makes it a competitive

scheme in various scenarios.

We evaluated the performance of Z-R by comparing it to GHT with different number of zones. Through ana-

lytical results and simulations, we showed that Z-R adapts well to various kinds of situations. Its dynamic adaptability

not only achieves significant energy-savings for hot-spots, but also reduces the total energy consumption in scenarios

with intermittent hot areas.

70

Chapter 6

Conclusion

Efficient communication energy usage is a prerequisite for the operation of wireless sensor networks. De-

spite its importance, the optimization of energy usage remains a nontrivial problem because of many intrinsic WSN

characteristics, such as the constrained computing and communication power, limited storage, dynamic traffic pattern,

and harsh environments. Furthermore, various applications usually have different performance demands in terms of

delivery ratio, delay, or observation fidelity. The complexity of this design space, combined with the consideration of

application requirements, has made it difficult for energy optimization schemes to achieve a balance between network

lifetime and performance.

The requirements of energy optimization schemes are: (1)Low individual energy consumption: In a multi-

hop sensor network, nodes play a dual role as a data sender anda data router. Malfunctioning of some sensor nodes due

to power failure can cause significant topological changes and require reconfiguration of the network. (2)Balanced

energy usage: The energy status of the entire network should be of the sameorder. If certain nodes have much higher

workload than others, these nodes will drain off their energy rapidly and adversely impact the overall system lifetime.

(3) Low computation and communication overhead: The resource limitations imposed by typical sensor hardware call

for simple protocols that require minimal processing and have a small memory footprint. Otherwise, the extra energy

required to perform the optimization schemes may outweigh the benefits.

In this thesis, we studied the power consumption characteristics of typical sensor platforms, and proposed

energy optimization schemes in network and application level. We designed distributed algorithms that reduce the

amount of data traffic and unnecessary overhearing waste in sensor networks, and further proposed load balancing

mechanisms that alleviate the unbalanced energy usage and prolong the effective system lifetime. It contributes to the

advancement of energy optimization in the following two major thrusts:

Exploit overhearing effect to improve aggregation efficiency and network energy management In a sensor

network, data aggregation reduces energy consumption by reducing the number of message transmissions. Effective

aggregation requires that event messages be routed along common paths. While the shortest-path tree provides an

easy way to construct the aggregation tree, this opportunistic style of aggregation is usually not optimal. The minimal

71

Steiner tree maximizes the possible degree of aggregation,but finding such a tree requires global knowledge of the

topology, which is impractical in sensor networks. Therefore, we proposed Adaptive Aggregation Tree (AAT), which

dynamically transforms the routing tree, using easily-obtained overheard information, to improve the efficiency of data

aggregation. Based on the simple shortest-path tree, AAT allows each node to adaptively choose a new parent if it

appears to provide better opportunities for aggregation. We also showed that AAT can work on other routing protocols

such as GPSR. Our simulation results showed that the local adaptivity of AAT successfully reduces the number of

message transmissions and achieves 23% and 31% energy reduction, compared to the shortest-path tree and GPSR

respectively.

Based on the concept of density control mechanisms, Neighborhood-Aware Density Control (NADC) ex-

ploits the overheard information to reduce the unnecessaryoverhearing energy consumption along the routing paths.

In NADC, nodes observe their neighborhood and dynamically adapt their participation in the multihop network topol-

ogy. By reducing the node density near the routing paths while keeping the nodes involved in packet generation or

forwarding in the active state, the overhearing waste can bereduced without dramatically increasing the delay of event

delivery.

Extend system lifetime through load balancing We attacked the unbalanced energy usage problem from two per-

spectives. In the network level, NADC reduced the unnecessary overhearing along routing paths and alleviated the

unbalanced energy usage among sensor nodes. However, the inherent nature of sensor network applications may still

result in unbalanced energy usage among sensor nodes, even with perfect protocols in the link and network layer.

Therefore, load balance has to be considered in the application level. We proposed Zone-Repartitioning (Z-R) for load

balancing in data-centric storage systems. Z-R reduces theenergy consumption of certain hot-spots by distributing

their communication load to other nodes when the event frequency of certain areas is much higher than the others.

This scheme provides a good tradeoff between the query cost (the energy consumption for propagating queries and

results) and event storage cost (the energy consumption forpropagating event data to storage nodes).

Table 6.1: Proposed schemes for energy optimizationProposed scheme Applied layer Goal Target system

Adaptive Aggregation Tree network Communication energy reduction Tree-based routing protocols

Neighborhood-Aware Density Control network/application Unbalanced energy usage alleviation Density control mechanisms

Zone-Repartitioning application Hotspot avoidance Data-centric storage systems

Table 6.1 summarizes the energy optimization schemes presented in the thesis. These schemes have differ-

ent goals and can be applied individually or altogether according to different scenario or system requirements. For

example, we showed in the thesis that AAT can be applied with NADC to further reduce the number of transmissions

in the network. The research work has certainly brought us one step closer to realize the full potential of wireless

sensor networks. However, further research is needed in thefollowing areas:

1. Integrated energy optimization: Existing optimization schemes are usually tightly coupled with network proto-

cols and other system functionality. This monolithic approach usually leads to standalone solutions that cannot

easily be reused or extended to other applications or platforms. Therefore, integrated energy optimization is

needed to accommodate various approaches and their different assumptions about the rest of the system with

which they need to interact.

72

2. Smart adaptation mechanism: Since the workload of sensor networks are usually highly irregular, the system

should be able to operate in an energy-efficient manner undervarious conditions while fulfilling the performance

requirements. Thus it is important to dynamically tradeoffbetween performance and network lifetime. Such

adaptation mechanisms include the energy monitoring and management at the OS-level, as well as the strategies

used in the various layers of the protocol stack, providing fine-grained component level performance adjustment

that can be easily incorporated into the system design.

For the community, integrated energy optimization and smart adaptation mechanisms will aid in future designs of

energy-aware application and enable a class of energy-centric applications where lifetime and performance require-

ments can be specified and controlled under a uniform framework.

73

Bibliography

[1] M. Ahmed, S. Dao, and R. Katz. Positioning range extension gateways in mobile ad hoc wireless networks to

improve connectivity and throughput. InProceedings of IEEE Military Communications conference (MilCom),

2001.

[2] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci. Wireless sensor networks: A survey.Computer

Networks, 38(4):393–422, 2002.

[3] Michael J. Alexander and Gabriel Robins. New performance-driven FPGA routing algorithms. InProceedings

of the 32nd ACM/IEEE Conference on Design Automation (DAC), pages 562–567, 1995.

[4] Guido Di Bacco, Tommaso Melodia, and Francesca Cuomo. A MAC Protocol for Delay-Bounded Applications

in Wireless Sensor Networks. InProceedings of Mediterranean Ad Hoc Networking Workshop, June 2004.

[5] Seema Bandyopadhyay and Edward J. Coyle. Minimizing communication costs in hierarchically-clustered

networks of wireless sensors.Comput. Networks, 44(1):1–16, 2004.

[6] Prithwish Basu and Jason Redi. Effect of overhearing transmissions on energy efficiency in dense sensor

networks. InProceedings of the third International Symposium on Information processing in sensor networks

(IPSN), pages 196–204, 2004.

[7] Maxim Batalin and Gaurav S. Sukhatme. The analysis of an effiient algorithm for robot coverage and explo-

ration based on sensor network deployment. InIEEE International Conference on Robotics and Automation,

pages 3489–3496, April 2005.

[8] Luca Benini, Giuliano Castelli, Alberto Macii, and Riccardo Scarsi. Battery-driven dynamic power manage-

ment. IEEE Design and Test of Computers, 18(2):53–60, 2001.

[9] Qing Cao, Tian He, Lei Fang, Tarek Abdelzaher, John Stankovic, and Sang Son. Efficiency centric communi-

cation model for wireless sensor networks. InProceedings of IEEE INFOCOM, 2006.

[10] J. Carle and D. Simplot-Ryl. Energy efficient area monitoring for sensor networks.IEEE Computer Magazine,

37:40–46, 2004.

[11] J. Cartigny, D. Simplot, and I. Stojmenovic. Localized minimum-energy broadcasting in ad-hoc networks. In

Proceedings of IEEE INFOCOM, 2003.

74

[12] Alberto Cerpa and Deborah Estrin. Ascent: Adaptive self-configuring sensor networks topologies.IEEE

Transactions on Mobile Computing, 3(3):272–285, 2004.

[13] Jae-Hwan Chang and Leandros Tassiulas. Maximum lifetime routing in wireless sensor networks.IEEE/ACM

Transactions on Networking, 12(4):609–619, 2004.

[14] Benjie Chen, Kyle Jamieson, Hari Balakrishnan, and Robert Morris. Span: an energy-efficient coordination

algorithm for topology maintenance in ad hoc wireless networks. Wireless Networks, 8(5):481–494, 2002.

[15] Quan Jun Chen, Salil Kanhere, Mahbub Hassan, and Kun-Chan Lan. Adaptive position update in geographic

routing. InProceedings of International Conference Communication (ICC), 2006.

[16] Chipcon AS.Chipcon SmartRFR© CC1000 Datasheet (rev. 2.2), April 2004.

[17] Jeffrey Considine, Feifei Li, George Kollios, and JohnByers. Approximate aggregation techniques for sensor

databases. InProceedings of the 20th International Conference on Data Engineering (ICDE), page 449, 2004.

[18] Crossbow Technology, Inc. MICA2 Datasheet. http://www.xbow.com/Products/Product_pdf_files/-

Wireless_pdf/MICA2_Datasheet.pdf.

[19] B.P. Crow, I. Widjaja, J. Geun Kim, and P.T. Sakai. IEEE 802.11 wireless local area networks.IEEE Commu-

nications Magazine, 35:116–126, 1997.

[20] E. D. Demaine, A. Lopez-Ortiz, and J. I. Munro. Frequency estimation of Internet packet streams with limited

space. InEuropean Symposium on Algorithms (ESA), pages 348–360, 2002.

[21] Yanlei Diao, Deepak Ganesan, Gaurav Mathur, and Prashant J. Shenoy. Rethinking data management for

storage-centric sensor networks. InConference on Innovative Data Systems Research (CIDR), pages 22–31,

2007.

[22] Christian C. Enz, Amre El-Hoiydi, Jean-Dominique Decotignie, and Vincent Peiris. WiseNET: An ultralow-

power wireless sensor network solution.Computer, 37(8):62–70, 2004.

[23] Kai-Wei Fan, Sha Liu, and Prasun Sinha. Structure-freedata aggregation in sensor networks.IEEE Transactions

on Mobile Computing, 6(8):929–942, 2007.

[24] Abhishek Ghose, Jens Grossklags, and John Chuang. Resilient data-centric storage in wireless ad-hoc sensor

networks. InMobile Data Management, pages 45–62, 2003.

[25] Wendi Heinzelman, Anantha Chandrakasan, and Hari Balakrishnan. Energy-efficient communication protocols

for wireless microsensor networks. InInternational Conference on System Sciences, 2000.

[26] Mark Hempstead, Nikhil Tripathi, Patrick Mauro, Gu-Yeon Wei, and David Brooks. An ultra low power system

architecture for sensor network applications. In32nd Annual International Symposium on Computer Architec-

ture (ISCA), pages 208–219, 2005.

75

[27] Jason Hill, Robert Szewczyk, Alec Woo, Seth Hollar, David Culler, and Kristofer Pister. System architec-

ture directions for networked sensors. InInternational Conference on Architectural Support for Programming

Languages and Operating Systems (ASPLOS), 2000.

[28] Jason Hill, Robert Szewczyk, Alec Woo, Seth Hollar, David E. Culler, and Kristofer S. J. Pister. System archi-

tecture directions for networked sensors. InArchitectural Support for Programming Languages and Operating

Systems, pages 93–104, 2000.

[29] Ruimin Huang and Yiannos Manoli. Phased array and adaptive antenna transceivers in wireless sensor networks.

In Euromicro Symposium on Digital System Design (DSD), pages 587–592, 2004.

[30] Chalermek Intanagonwiwat, Ramesh Govindan, and Deborah Estrin. Directed diffusion: a scalable and robust

communication paradigm for sensor networks. InProceedings of the 6th Annual International Conference on

Mobile Computing and Networking (MobiCom), pages 56–67, 2000.

[31] David B. Johnson and David A. Maltz. Dynamic source routing in ad hoc wireless networks. InMobile

Computing, volume 353. 1996.

[32] Konstantinos Kalpakis, Koustuv Dasgupta, and Parag Namjoshi. Efficient algorithms for maximum lifetime

data gathering and aggregation in wireless sensor networks. Computer Networks, 42(6):697–716, 2003.

[33] Brad Karp and H. T. Kung. GPSR: greedy perimeter stateless routing for wireless networks. InProceedings of

the 6th Annual International Conference on Mobile Computing and Networking (MobiCom), pages 243–254,

2000.

[34] Kevin Klues, Guoliang Xing, and Chenyang Lu. Link layersupport for unified radio power management in

wireless sensor networks. InProceedings of the 6th International Conference on Information Processing in

Sensor Networks (IPSN), pages 460–469, 2007.

[35] L. Kou, G. Markowsky, and L. Berman. A fast algorithm forSteiner trees.Acta Informatica (Historical

Archive), 15(2):141–145, 1981.

[36] B. Krishnamachari, D. Estrin, and S. Wicker. Modellingdata-centric routing in wireless sensor networks.

Technical Report CENG 02-14, University of Southern California, 2002.

[37] Bhaskar Krishnamachari, Deborah Estrin, and Stephen B. Wicker. The impact of data aggregation in wireless

sensor networks. InProceedings of the 22nd International Conference on Distributed Computing Systems

(ICDCS), pages 575–578, 2002.

[38] L. K. Lee and H. F. Ting. Maintaining significant stream statistics over sliding windows. InACM-SIAM Symp.

on Discrete Algorithm (SODA), pages 724–732, 2006.

[39] P Lettieri, C. Fragouli, and M.B. Srivastava. Low powererror control for wireless links. InThe Third Annual

ACM/IEEE International Conference on Mobile Computing andNetworking (MobiCom), 1997.

76

[40] Philip Levis, Nelson Lee, Matt Welsh, and David Culler.TOSSIM: Accurate and scalable simulation of entire

TinyOS applications. InProceedings of the 1st International Conference on Embedded Networked Sensor

Systems (SenSys), pages 126–137, 2003.

[41] Li Li, Joseph Y. Halpern, Paramvir Bahl, Yi-Min Wang, and Roger Wattenhofer. Analysis of a cone-based

distributed topology control algorithm for wireless multi-hop networks. InProceedings of the Twentieth annual

ACM Symposium on Principles of Distributed Computing (PODC), pages 264–273, 2001.

[42] Zhenmin Li, Pin Zhou, and Jennifer C. Hou. Fragmentation based D-MAC protocol in wireless ad hoc network.

In Proceedings of the 23rd International Conference on Distributed Computing Systems (ICDCS), pages 468–

477, 2003.

[43] Sze-Chu Liu. A lifetime-extending deployment strategy for multi-hop wireless sensor networks. InProceedings

of the 4th Annual Communication Networks and Services Research Conference (CNSR), pages 53–60, 2006.

[44] Samuel Madden, Michael J. Franklin, Joseph M. Hellerstein, and Wei Hong. TAG: a Tiny AGgregation service

for ad-hoc sensor networks.ACM SIGOPS Operation Systems Review, 36(SI):131–146, 2002.

[45] Samuel R. Madden, Michael J. Franklin, Joseph M. Hellerstein, and Wei Hong. TinyDB: an acquisitional query

processing system for sensor networks.ACM Transactions on Database Systems, 30(1):122–173, 2005.

[46] Gurmeet Singh Manku and Rajeev Motwani. Approximate frequency counts over data streams. InInternational

Conference on Very Large Data Bases (VLDB), pages 346–357, 2002.

[47] Dustin McIntire, Kei Ho, Bernie Yip, Amarjeet Singh, Winston Wu, and William J. Kaiser. The low power

energy aware processing (LEAP) embedded networked sensor system. InProceedings of the 5th International

Conference on Information Processing in Sensor Networks (IPSN), pages 449–457, 2006.

[48] Moteiv Corporation.Tmote Sky Datasheet. http://www.moteiv.com/products/docs/tmoteskydatasheet.pdf.

[49] Alaa Muqattash and Marwan M. Krunz. A distributed transmission power control protocol for mobile ad hoc

networks.IEEE Transactions on Mobile Computing, 3(2):113–128, 2004.

[50] S. Narayanaswamy, V. Kawadia, R. Sreenivas, and P. Kumar. Power control in ad-hoc networks: Theory,

architecture, algorithm and implementation of the compow protocol. InEuropean Wireless Conference, pages

156–162, 2002.

[51] Leyla Nazhandali, Bo Zhai, Javin Olson, Anna Reeves, Michael Minuth, Ryan Helfand, Sanjay Pant, Todd

Austin, and David Blaauw. Energy optimization of subthreshold-voltage sensor network processors. InPro-

ceedings of the 32nd Annual International Symposium on Computer Architecture (ISCA), pages 197–207, 2005.

[52] Stephan Olariu and Ivan Stojmenovic. Design guidelines for maximizing lifetime and avoiding energy holes in

sensor networks with uniform distribution and uniform reporting. In Proceedings of IEEE INFOCOM, 2006.

[53] Carlos A. S. Oliveira and Panos M. Pardalos. A survey of combinatorial optimization problems in multicast

routing. Computers and Operations Research, 32(8):1953–1981, 2005.

77

[54] B. Otis, Y.H. Chee, and J. Rabaey. A 400uw rx, 1.6mw tx super-regenerative transceiver for wireless sensor

networks. InIEEE International Solid-State Circuits Conference (ISSCC), February 2005.

[55] Francisco Javier Ovalle-Martínez, Ivan Stojmenovic, Fabián García-Nocetti, and Julio Solano-González. Find-

ing minimum transmission radii for preserving connectivity and constructing minimal spanning trees in ad hoc

and sensor networks.Journal of Parallel and Distributed Computing, 65(2):132–141, 2005.

[56] Joseph A. Paradiso and Thad Starner. Energy scavengingfor mobile and wireless electronics.IEEE Pervasive

Computing, 4(1):18–27, 2005.

[57] Wei Peng and Xi-Cheng Lu. On the reduction of broadcast redundancy in mobile ad hoc networks. InProceed-

ings of the 1st ACM International Symposium on Mobile Ad Hoc Networking & Computing (MobiHoc), pages

129–130, 2000.

[58] M. Perillo and W. Heinzelman. Optimal sensor management under energy and reliability constraints. InPro-

ceedings of the IEEE Wireless Communications and Networking Conference (WCNC), 2003.

[59] Joseph Polastre, Jason Hill, and David Culler. Versatile low power media access for wireless sensor networks.

In Proceedings of the 2nd International Conference on Embedded Networked Sensor Systems (SenSys), pages

95–107, 2004.

[60] Joseph Polastre, Robert Szewczyk, and David E. Culler.Telos: enabling ultra-low power wireless research. In

Proc. of the 4th Intl. Symp. on Information Processing in Sensor Networks (IPSN), pages 364–369, 2005.

[61] Jonathan Polley, Dionysys Blazakis, Jonathan McGee, Dan Rusk, John S. Baras, and Manish Karir. Atemu:

A fine-grained sensor network simulator. InIEEE Conference on Sensor and Ad Hoc Communications and

Networks (SECON), 2004.

[62] Jan Rabaey. Ultra low-energy transceivers for wireless sensor networks. InProceedings of the 15th Symposium

on Integrated Circuits and Systems Design (SBCCI), page 386, 2002.

[63] V. Raghunathan, C. Schurgers, S. Park, and M. Srivastava. Energy-aware wireless microsensor networks.IEEE

Signal Processing Magazine, 19, March 2002.

[64] Ram Ramanathan and Regina Hain. Topology control of multihop wireless networks using transmit power

adjustment. InProceedings of IEEE INFOCOM, pages 404–413, 2000.

[65] S. Ramanathan and Errol L. Lloyd. Scheduling algorithms for multi-hop radio networks. InConference on

Communications Architectures & Protocols (SIGCOMM), pages 211–222, 1992.

[66] Sylvia Ratnasamy, Paul Francis, Mark Handley, RichardKarp, and Scott Schenker. A scalable content-

addressable network. InProceedings of the 2001 Conference on Applications, Technologies, Architectures,

and Protocols for Computer Communications (SIGCOMM), pages 161–172, 2001.

[67] Sylvia Ratnasamy, Brad Karp, Scott Shenker, Deborah Estrin, Ramesh Govindan, Li Yin, and Fang Yu. Data-

centric storage in sensornets with GHT, a geographic hash table. Mobile Networks and Applications, 8(4):427–

442, 2003.

78

[68] Injong Rhee, Ajit Warrier, Mahesh Aia, and Jeongki Min.Z-MAC: a hybrid MAC for wireless sensor networks.

In Proceedings of the 3rd International Conference on Embedded Networked Sensor Systems (SenSys), pages

90–101, 2005.

[69] Gabriel Robins and Alexander Zelikovsky. Improved Steiner tree approximation in graphs. InProceedings of

the 11th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pages 770–779, 2000.

[70] Antony Rowstron and Peter Druschel. Pastry: Scalable,decentralized object location and routing for large-scale

peer-to-peer systems. InIFIP/ACM International Conference on Distributed SystemsPlatforms (Middleware),

pages 329–350, November 2001.

[71] Mauro Sanctis, Simone Quaglieri, Ernestina Cianca, and Marina Ruggieri. Energy Efficiency of Error Control

for High Data Rate WPAN.Wireless Personal Communications, 34(1-2):189–209, 2005.

[72] Curt Schurgers, Olivier Aberthorne, and Mani Srivastava. Modulation scaling for energy aware communication

systems. InProceedings of the 2001 International Symposium on Low Power Electronics and Design (ISLPED),

pages 96–99, 2001.

[73] Curt Schurgers, Vlasios Tsiatsis, Saurabh Ganeriwal,and Mani Srivastava. Optimizing sensor networks in the

energy-latency-density design space.IEEE Transactions on Mobile Computing, 1(1):70–80, 2002.

[74] Rahul C. Shah and Jan M. Rabaey. Energy aware routing forlow energy ad hoc sensor networks. InProceedings

of the IEEE Wireless Communications and Networking Conference (WCNC), March 2002.

[75] Jang-Ping Sheu, Pei-Kai Hung, and Chih-Shun Hsu. Scheduling of broadcasts in multihop wireless networks.

The handbook of ad hoc wireless networks, pages 483–495, 2003.

[76] Victor Shnayder, Mark Hempstead, Bor rong Chen, Geoff Werner Allen, and Matt Welsh. Simulating the power

consumption of large-scale sensor network applications. In Proceedings of the 2nd International Conference

on Embedded Networked Sensor Systems (SenSys), pages 188–200, 2004.

[77] Mihail L. Sichitiu and Rudra Dutta. Benefits of multiplebattery levels for the lifetime of large wireless sensor

networks. InNetworking, pages 1440–1444, 2005.

[78] G. Simon, P. Volgyesi, M. Maroti, and A. Ledeczi. Simulation-based optimization of communication protocols

for large-scale wireless sensor networks. InIEEE Aerospace Conference, 2003.

[79] Suresh Singh and C. S. Raghavendra. PAMAS: Power aware multi-access protocol with signalling for ad hoc

networks.SIGCOMM Computer Communication Review, 28(3):5–26, 1998.

[80] Ion Stoica, Robert Morris, David Karger, M. Frans Kaashoek, and Hari Balakrishnan. Chord: A scalable

peer-to-peer lookup service for internet applications. InProceedings of the 2001 Conference on Applications,

Technologies, Architectures, and Protocols for Computer Communications (SIGCOMM), pages 149–160, 2001.

[81] Ivan Stojmenovic, Mahtab Seddigh, and Jovisa Zunic. Dominating sets and neighbor elimination-based broad-

casting algorithms in wireless networks.IEEE Transactions on Parallel and Distributed Systems, 13(1):14–25,

2002.

79

[82] H. Takahashi and A. Matsuyama. An approximate solutionfor the Steiner problem in graphs.Mathematica

Japonica, 24(6):573–577, 1980.

[83] Caimu Tang and Cauligi S. Raghavendra. Compression techniques for wireless sensor networks. InWireless

Sensor Networks, pages 207–231. 2004.

[84] Tijs van Dam and Koen Langendoen. An adaptive energy-efficient MAC protocol for wireless sensor networks.

In Proceedings of the 1st International Conference on Embedded Networked Sensor Systems (SenSys), pages

171–180, 2003.

[85] Alice Wang and Anantha Chandrakasan. A 180-mV subthreshold FFT processor using a minimum energy

design methodology.IEEE Journal of Solid-State Circuits, (1):310–319, January 2005.

[86] Andrew Wang, SeongHwan Cho, Charles Sodini, and Anantha Chandrakasan. Energy efficient modulation and

MAC for asymmetric RF microsensor systems. InProceedings of the 2001 International Symposium on Low

power Electronics and Design (ISLPED), pages 106–111, 2001.

[87] Roger Wattenhofer, Li Li, Paramvir Bahl, and Yi-Min Wang. Distributed topology control for wireless multihop

ad-hoc networks. InProceedings of IEEE INFOCOM, pages 1388–1397, 2001.

[88] Jeffrey E. Wieselthier, Gam D. Nguyen, and Anthony Ephremides. Energy-efficient broadcast and multicast

trees in wireless networks.Mobile Networks and Applications, 7(6):481–492, 2002.

[89] Alec Woo, Terence Tong, and David Culler. Taming the underlying challenges of reliable multihop routing in

sensor networks. InProceedings of the 1st International Conference on Embedded Networked Sensor Systems

(SenSys), pages 14–27, 2003.

[90] Ya Xu, John Heidemann, and Deborah Estrin. Adaptive energy-conserving routing for multihop ad hoc net-

works. Research Report 527, USC/Information Sciences Institute, October 2000.

[91] Ya Xu, John Heidemann, and Deborah Estrin. Geography-informed energy conservation for ad hoc routing.

In Proceedings of the 7th annual International Conference on Mobile Computing and Networking (MobiCom),

pages 70–84, 2001.

[92] Y. Yao and J. Gehrke. Query processing in sensor networks. InConference on Innovative Data Systems Research

(CIDR), 2003.

[93] Yong Yao and J. E. Gehrke. The Cougar approach to in-network query processing in sensor networks.ACM

SIGMOD Record, 31(2):9–18, 2002.

[94] Wei Ye, John Heidemann, and Deborah Estrin. An energy-efficient MAC protocol for wireless sensor networks.

In Proc. 21st Intl. Annual Joint Conf. of the IEEE Computer and Communications Societies, 2002.

[95] Zhenqiang Ye, Srikanth V. Krishnamurthy, and Satish K.Tripathi. A framework for reliable routing in mobile

ad hoc networks. InProceedings of IEEE INFOCOM, 2003.

[96] Bo Zhai, David Blaauw, Dennis Sylvester, and KrisztianFlautner. The limit of dynamic voltage scaling (DVS)

and extended DVS.IEEE Transactions on Very Large Scale Integration Systems, 2005.

80

[97] Honghai Zhang and Jennifer Hou. On deriving the upper bound ofα-lifetime for large sensor networks. InPro-

ceedings of the 5th ACM International Symposium on Mobile AdHoc Networking and Computing (MobiHoc),

pages 121–132, 2004.

[98] Ying Zhang and Qingfeng Huang. A learning-based adaptive routing tree for wireless sensor networks.Journal

of Communications, 1(2):12–21, 2006.

[99] B. Y. Zhao, J. D. Kubiatowicz, and A. D. Joseph. Tapestry: An infrastructure for fault-tolerant wide-area

location and routing. Technical Report UCB/CSD-01-1141, UC Berkeley, April 2001.

[100] Yujie Zhu, Karthikeyan Sundaresan, and Raghupathy Sivakumar. Practical limits on achievable energy im-

provements and useable delay tolerance in correlation aware data gathering in wireless sensor networks. In

IEEE Communications Society Conference on Sensor and Ad HocCommunications and Networks (SECON),

pages 328–339, 2005.

[101] Michele Zorzi and Ramesh R. Rao. Error control and energy consumption in communications for nomadic

computing.IEEE Transactions on Computers, 46(3):279–289, 1997.