Post on 04-Jan-2016
Design of Energy Efficient Computations and Protocols for Wireless Sensor Networks
Ashfaq A. KhokharMultimedia Systems Lab(http://multimedia.ece.uic.edu/)
University of Illinois at Chicago
Wireless Sensor Networks
• A large number of sensor nodes with limited capability of computation, communication and sensing.
• Nodes collaborate with each other through a wireless channel to accomplish an assigned task.
Sample Sensor Nodes
Modern Sensor Nodes
UC Berkeley: COTS Dust
UC Berkeley: COTS DustUC Berkeley: Smart Dust
UCLA: WINS Rockwell: WINS JPL: Sensor Webs
Courtesy UC Berkley
Features of WSN
Traffic rate is generally low– typical communication frequency is in seconds or minutes.
Sensor nodes are battery powered– recharging is usually unavailable – energy is an extremely expensive resource
Sensor nodes in the network coordinate with each other to implement a certain function,
– traffic is not random as in mobile ad hoc networks.
Motivation
Wireless Sensor Networks is one of the top 10 Technologies that will change the World in 21st Century
According to MIT Technology Review
Pervasive computing environments are increasing
Abundance of defense, scientific, and commercial applications
Wireless Medium Popularity
Phenomenal growth in – Mobile Communications– Internet/Intranet, E-Commerce– Use of Laptops, Palmtops, and PDAs
New High Bit Rate Wireless Services– Intranet/Internet– Multimedia: Integrated Voice, Data, Video– High quality voice and Videoconferencing
New Technology means new products/services– Revenue opportunities
Market Estimation
WSN: $150 Million in 2003, $7 Billion estimated in 2010 (ON World)
Mobility infrastructure market expected to grow from $25.7 Billion in 2004 to $34.8 Billion in 2008 (Dell’Oro Group)
Today more wireless connections than wired lines
Typical WSN Applications
Environment monitoring Habitat monitoring
Office securityTransportation
Industrial monitoring
Fire detection
Challenges in Wireless Sensor Networks
Software Systems– Computing– Control– Databases– Fusion– Knowledge Extraction
Networking and Communication– Routing, Data Gathering, Data Dissemination
Hardware:– VLSI integration– Architectures– Deployment
Signal and Systems– Signal processing– Classification
Devices– Sensor technologies
User Interfaces and Development Environment
Research at Multimedia Systems Lab
Software Systems– Power-Time Efficient Algorithms
Networking
– MAC layer – Routing Layer Protocols
Signals and Systems– Field Estimation– Localization– Classification
Collaborative Computing over Sensor Networks
Sensors are smart:– processing, storage and communication capabilities
Exploit these resources and communicate with sink only when necessary
– Similar arguments hold for computing among sinks Develop distributed algorithms which are power-time
efficient:– Power-time product is comparable to sequential counter-
part Contradicting goals:
– Exploit distributed computing resources– Avoid redundant computations
Example: Conventional Distributed FFT
0 1 2 3 4 5 6 7
0 1 2 3 4 5 6 7
x2 x3
x2 + w x3 x2 - w x3
0 1 2 3 4 5 6 7
0 1 2 3 4 5 6 7
Unbalanced Power-Aware FFT: (Ramesh et al -- Milcom 2003)
0 1 2 3 4 5 6 7
0 1 2 3 4 5 6 7
x2 w*x3
x2 + w x3 x2 - w x3
0 1 2 3 4 5 6 7
0 1 2 3 4 5 6 7
31 5 7
2 3 6 7
4 5 6 7
0 1 2 3 4 5 6 7
0 1 2 3 4 5 6 7
x2 w*x3
x2 + w x3 x2 - w x3
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x3
31 5 7
2 3 6 7
8 110
312
514
7
8 9 2 312
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6 7
x2 w*x3
x2 + w x3 x2 - w x3
8 910
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x3
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610 2
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5 9 1
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4 8 015 7
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5 9 1
713
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8
Inverse-Shuffle Complement Permutation
Power–Time Efficient Distributed 1-d FFT Algorithm
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Sensors
Prop
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/Con
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l Normalized Radio Cost
The ratio of total number of send/receives in the proposed and conventional 64-Sensored FFT algorithm
RESULTS RESULTS [SenSys04, AICSS 2006][SenSys04, AICSS 2006]
•The Actual communication cost improvement based on our experiments result is 36%.
•Theoretical cost improvement is 42%.
•The discrepancy arises from packet dropping due to collisions.
0
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8 16 32 64Number of Sensors
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erg
y(m
J)
Proposed
Original
RESULTSRESULTS
Energy consumption of the FFT computation as the number of sensors increased
•Approximately 20% energy cost reduction for N=32 and 64.
•The energy improvement is due to reduction in the FFT computation time, as sensors are all on during the computation.
•Except for the network of 8, the overhead of id shuffle phase is justified from the overall savings.
RESULTSRESULTS
The number of packets transmitted at each time-interval during the conventional and proposed FFT computation over a 64-sensor network sensor networks
Conventional FFT Proposed FFT
•The Congested intervals are reduced significantly.
•Packet collision probability is reduced.
•MAC protocols can shut down the radio circuitry aggressively.
Example: Classification
Apriori determination of categories
Train for each category– Expose to sample
measurements– Compute Mean (i,j) &
Covariance (i,j) Test
– Classifier Algorithms– Develop false positives,
beliefs, etc Deploy
– Identify detected object w.r.t. categories
?
Classifier Algorithms
Traditional Signal Processing (SP) algorithms Maximum Likelihood (ML) [Li & et. el. 2002; Duarte & Hu, 2003] k Nearest Neighbor (kNN) [Li & et. el. 2002; Duarte & Hu, 2003]
– Pro: used extensively for its known accuracy– Con: computationally intensive
Novel WSN classifiers Sub-optimal Classifiers [Kotecha & et. el. 2005] Influence fields [Arora & et. el. 2004] Differentiated Surveillance [Yan & et. el. 2003]
– Pro: simpler computationally– Con: novice, accuracy?
Our Goal: adapt existing SP algorithms for efficient classification in sensornets
What’s the Problem?
Events are f × d matricesf – modalities/features sensedd – temporal processing dimensions
Typically f 50 & d 512 Mean is f × d and is f × f matrices
-1 is also f × f Large matrix computations for every detected event
– Matrix multiplication Inverse computation !
– Unstable– Expensive
Power Consumption Comparison [HiPC 2006]
Power consumption comparison of MAP with Jacobi (MAP-J) and MAP with LU (MAP-L)
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10 20 30 40 50 60 70 80 90 100
features
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of
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MAP-L
d = 512
Communication Protocols
A fresher look was needed Energy efficiency and network life time as the
main goal– MAC Layer– Network Layer: Geographical Routing– Application Layer: Data Collection
Geographic Routing in WSN
Geographic routing:– Greedy forwarding: At each hop, data packet is relayed by a neighbor which
is geographically closer to the destination than the packet holder in terms of Euclidean distance
– Communication void(hole): a network area where greedy forwarding fails to locate the next hop node in a packet holder’s neighborhood
– Some strategies must be used to handle the void to guarantee the packet delivery if a real path exist
Existing Geographic Routing Protocols
Beacon-based(state-based): PGSR…. Neighbor information collection: periodical beacon messages exchanged
among neighbor nodes Next hop node is chosen from the neighbor table by the packet holder Right/left hand rule based on planar graph is used to handle void High communication overhead due to the beacon messages
Beacon-free(state-free): IGF, PSGP, SIF… No neighbor information maintained in each node Next hop node is chosen based on a competition, which is triggered by a packet
holder, among the packet holder’s neighbors Right/left hand rule or increasing transmission power methods are used to
handle the void:
Motivation for Void Handling
Right/left-hand rule is impractical since it’s hard to build planar graph in beacon-free geographic routing protocols
Increasing transmission power is not energy efficient
History of void handling should be maintained and used for future data delivery
Proposed Contention-based Geographic Routing Protocol (CGR-D)
Beacon-free A depth first search (DFS) method is used to locate the next hop node
at each hop An integrated cost function which combines forwarding area
determination, void handling and load balancing is defined as the metric used in DFS method
Each node maintains a local variable (void cost) which is an indication whether a node is in or close to a void area
The void cost is updated per sink basis and periodically reset to 0
Cost Function
If a node C is closer to the sink D than the current packet holder F:f (d, E, r, H) = [* (1-d/R) + * (1-E/E0) + *r + K*(1+H)]*T0 (1)
Otherwise:f (d, E, r, H) = [* (1-d/R) + * (2-E/E0) + *(1+r) + K*(1+H)]*T0 (2)
Whered = dist(F, D) – dist(C, D) dist(x,y)means distance between x and yH = voidCost(C) - voidCost(F) voidCost(x) is the void cost value of
node xR is radio rangeE is the remaining energy of node CE0 is the initial energy
r is a random number between [0,1]+ + = 1 and , , > 0K, T0 are system parameters
Data Delivery Example for CGR-D
Performance Evaluation
General Data Gathering
Data gathering problem:
Sensing data and transmitting data to a sink. Clustering based solution:
LEACH is a typical one.
Nodes organized into clusters.
Head collects data and transmits aggregated data to the sink.
Sink
Mobility of Sensor Nodes
The nearest cluster head may change especially in a high-mobility scenario.
Keeping transmitting to the old cluster head consumes more energy.
Sink
Our Solution: Low-energy Dynamic Cluster Selection (LEDCS) Protocol [VTC 06]
As in LEACH, time is divided into rounds and at the beginning of each round, each node i determines whether it is a cluster head in the current round with a predefined probability.
Introduce a contention period at the beginning of each time frame.
Nodes may join the new cluster head during this contention period.
If a node is a cluster head in the current round, it broadcasts this info across the network which also includes its own moving direction and velocity in the current round.
Our Solution: Low-energy Dynamic Cluster Selection (LEDCS) Protocol
Simulation Results
Percentage of Percentage of contention contention period period considering considering different different velocity of velocity of sensor nodes.sensor nodes.
1000 sensors in a 400m x 400m area1000 sensors in a 400m x 400m area
Simulation Results
Total number Total number of data packets of data packets received by the received by the sink sink considering considering different different velocity of velocity of sensor nodes.sensor nodes.
Up to 80% Up to 80% higherhigher
Simulation Results
Total number of data rounds
Data Gathering in Event Driven Applications [INSS 07]
Bursty traffic in event driven applications.
Sink
Estimation of Traffic Load
Estimate the current traffic load at the beginning of each round.
Based on the estimated value, set up mathematical model and determine optimal number of cluster heads to minimize the total energy consumption.
the total energy consumption vs.
optimal number of cluster heads
Comparison of Total Energy Consumption
Test case 1 Test case 2
Data Collection via Cross-Layer Optimization
Sink
Sensor nodes
Data Collection:●A major class of sensor network application●Generally a spanning tree is used for data collection over a sensor network
Problems:● Congestion is major performance bottleneck.
Goals:●To increase data delivery ratio with simpler MAC protocols●To mitigate congestion in the network especially near the sink node●To increase bandwidth utilization
●SMAC like contention based MAC protocols are simpler and do not require tight synchronization.
BA
C
Question: In data collection applications is it absolutely necessary for a node to communicate every one of its hop neighbors?
-Not if we can find route to sink!
AB
C
Sink
-Node B does not have to communicate with C, so it can stop following C's schedule
active
active
activeactive
sleep
sleep
sleep sleep
MOTIVATION
Single Data Collection Tree
•Single Data Collection Tree
•Multihop communication
•Single spanning tree is constructed and nodes forward their readings to their parents
•Major performance bottlenecks:
• Heavy congestion near the sink node.
• High competition for the wireless medium.
• High delays due to medium access
Phase 1: Construct 2 different trees Phase 2 : Activate each tree at different times
Multiple Data Collection Trees
Multiple Data Collection Trees
•Construct more than 1 collection tree•Active trees at different times than the others•Data collection is possible without communicating with all the neighbors•Routing protocol should find the set of neighbors necessary to communicate with sink. •Decreasing the number of active nodes will mitigate congestion and increase delivery ratio.
sleep active
active sleep
sleep
1 Duty Cycle
activeSingle data collection tree:
Advantage: Simple tree construction
Disadvantages:●Network is highly congested near sink●Bandwidth is not fully utilized●High collision probability
Single vs. Multiple Data Collection Trees
Multiple Data Collection Trees
Advantages:
•Increased bandwidth utilization
•Mitigated Congestion
•Energy consumption not increased
•Less nodes are active
Disadvantage: More complex tree construction
Delivery Ratio- 10% SMAC
0.15
0.25
0.35
0.45
0.55
0.65
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0.85
1 2 3 4 5 6 7 8 9
Num ber Of Trees
SMAC 50
SMAC 100
SMAC 200
Simulation Results
Delivery Ratio Regular Grid
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1 2 3 4 5 6
Number Of Trees
50
100150
200
Simulation Results
Energy Consum ption
650
670
690
710
730
750
770
790
810
1 2 3 4 5 6 7 8 9
Num ber of Trees
Jou
les
SMAC 50
SMAC 100
Simulation Results
Data Gathering in Vehicular Networks
Stationary Internet Gateways (IGWs) along the highway which provide interfaces connecting vehicles to the Internet, a gateway communicate with vehicles out of its transmission range via multi-hop communications
Traditional 802.11 MAC solution: Multi-hopping scheme suffers from low throughput
and starvation of packets originating from vehicles far away from gateways due to high collision
Example: CVIA Protocol
1. Divide the line between gateways into segments , length of segments is equal to the transmission range of a vehicle
2. Assign TDMA slots for each segments such that only segments out of
transmission range will transmit in the same time slot– collision free
3. In the figure S1,3,5,7 transmit in a time slot and s2,4,6,8 transmit in the next
time slot
CVIA Protocol
Four phases in each time slot:1) Temporary Router Selection phase: inbound and outbound
temporary routers elected for each segments2) Inbound router transmits collected data to outbound router3) Outbound router collects data within its segment4) Outbound router transmits data to the neighboring inbound
router
Problems
Inter- and Intra-segment contention leads to higher packet losses
The time length of a slot for local data gathering phase is uniform, leading to transmission delays
Solutions:– ???
Distortion Analysis in Sensing Field Measuring Spatial-temporal Correlated Data
Example of field measuring Gaussian correlated data.
Consider the real-time data gathering problem in a field that data is both spatial and temporal correlated.
Sink will do real-time data reconstruction for the whole field.
How many nodes should be put into the field to minimize the total distortion?
Number of nodes increases:
Spatial distortion decreases while temporal distortion increases.
An optimal number of nodes exists to minimize the total distortion.
Randomly deployed single-hop sensor networks
Nodes randomly deployed in the field following Poisson distribution.
1. One-dimensional case
2. Two-dimensional caseUse Voroni Cell partitions to achieve minimal distortion within each cell
TDMA protocol used for data collection
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Randomly deployed one-hop sensor networks
1. An optimal number of nodes always exist to minimize the total distortion D.
2. when correlation intensity α is fixed and time scaling constant βT is increased, the optimal value of n that minimizes the total distortion D decreases.
Fixed topology for multi-hop sensor networks
We assume that each node has a limited transmission range R
A TDMA-based transmission algorithm is designed for collision-free data transmission from nodes to the sink.
An Example 1D Transmission Schedule
Total distortion varies with different coefficients (one-dimensional case)
Total distortion varies with different coefficients (two-dimensional case)
a) α=0.1,βT=0.001 b) α=0.1,βT=0.002 c)α=0.1,βT=0.005
d) α=0.02,βT=0.002 e) α=0.05,βT=0.001 f) α=0.05,βT=0.002
Analysis for both cases
The total distortion experiences a sudden drop for every increase of five in number of nodes (one-dimensional case) or number of rings (two-dimensional case).
With the increase of correlation coefficients α and βT, for a given number of nodes, the total distortion will increase due to the weaker correlation of the field.
Minimum number of nodes required given a certain distortion constraint
Correlation Coefficients Distortion constraint
Minimum number of nodes
α=1, βT=0.2 12 35
α=1, βT=0.002 4 6
α=1, βT=0.002 1.5 14
α=0.5, βT=0.02 4 5
α=0.5, βT=0.002 1.5 7
α=0.5, βT=0.002 1 14
α=0.2, βT=0.002 1.5 5
α=0.2, βT=0.002 1 6
Correlation Coefficients Distortion constraint
Minimum number of nodes n*k(n, k)
α=0.1, βT=0.005 120 50(10,5)
α=0.1, βT=0.002 60 90(18,5)
α=0.1, βT=0.001 40 120(24,5)
α=0.1, βT=0.001 120 45(9,5)
α=0.05, βT=0.005 120 35(7,5)
α=0.05, βT=0.002 40 70(14,5)
α=0.02, βT=0.002 60 30(6,5)
α=0.02, βT=0.002 40 35(7,5)
One-dimensional grid network case Two-dimensional wheel-based network case
Other Problems Under Investigation
Localization Synchronization Upper layer communication protocols Data fusion Knowledge extraction