Biased Sink Mobility with Adaptive Stop Times for Low Latency Data Collection in Sensor Networks

Athanasios Kinalis , Sotiris Nikoletseas , ∗ ∗Dimitra Patroumpa , Jose Rolim†∗

∗University of Patras and Computer Technology Institute, Patras, Greece

†Centre Universitaire d’ Informatique, Geneva, Switzerland

IEEE Globecom2009

Biased Sink Mobility with Adaptive Stop Times forLow Latency Data Collection in Sensor Networks

Outline

Introduction Network modelThe proposed schemeSimulation Conclusion

Introduction

As the sensed data are forwarded to the sink node in the WSNSettings have increased implementation complexitySensor devices consume significant amounts of energy

Sensor node

Sink node

Introduction

A approach has been introduced that shifts the burden of acquiring the data, from the sensor nodes to the sink.Help conserve energy since data is transmitted over

fewer hops.

Many apparent difficulties arise as well since traversing the network in a timely and efficient way is critical.high density of sensors in an areasome sensors have recorded a significant amount of data

Introduction

High delivery delaysEven data loss

A

B

Goal

They propose biased sink mobility with adaptive stop times, as a method for efficient data collection in wireless sensor networks.reduces latencydelivery success rate

Network model

The sink can accurately calculate its positionThe sink can aware of the dimensions and boundaries of the network

areaThe sensor of sensing range R

D

D

j

j

2

Dj

R

Sensor node

Sink node

Scheme

Network TraversalDeterministic WalkBiased Random Walk

Calculation of Stop Time

Deterministic Walk

j

j

ASensor node

Sink node

Biased Random Walk The probability p(f)v of visiting a neighboring vertex v

2 2 1

1 42 2

2 3 3 0

2 32 3

1

A B

C

D

E

1 1 / 8 7( )

3 24bp f

1 2 / 8 6( )

3 24cp f

1 2 / 8 6( )

3 24dp f

1 3 / 8 5( )

3 24dp f

2

Sensor node

Sink node

Etotal is the total initial energy of all the sensors in the network

Ttotal_stop represents the maximum total amount of time the sink can remain stationary.

n is the number of sensors of the network


εi the initial energy of each sensor i.

Tsim is the total time that the experiment is performedEtotal

the maximum amount of energy consumed when sending a messagethe average event generation rate

the energy spent when the sensors remain idle

Ttotal_stop_round is the maximum amount of time that the sink will remain static in each round.

__ _

Ttotal stopTtotal stop round

r

represents the maximum total amount of time the sink can remain stationary


the algorithm evolves in r rounds

Constant stop time.

Adaptive stop time.

the maximum amount of time that the sink will remain static in each round


the density in cell i


Example

A B

CD

10iTcell

d = 1

dA = 1.5dB = 1.2dC = 0.5dD = 0.8

1.5 1.2

0.50.81.5

10 151adapAT

1.210 12

1adapBT

0.510 5

1adapCT

0.810 8

1adapDT

Simulation

Simulator ns − 2

the size of the network area 200m × 200m

sensor nodes 300

the speed of the mobile sink s {4, 8, 10, 20}m/s∈

The initial energy reserves of the nodes 5.5 Joules

The data is generated at an average rate 1 message/10 sec

Simulation SCD (Stop to Collect Data), one of the algorithms proposed in [6]. In SCD, the

mobile sink stops when receiving new data.

A. Kansal, A. Somasundara, D. Jea, M. Srivastava, and D. Estrin, “Intelligent fluid infrastructure for embedded networks,” in 2nd ACM/USENIX International Conference on Mobile Systems, Applications, and Services (MobiSys04), 2004.

Conclusion They propose both randomized mobility and

deterministic traversals.They adaptive stop times lead to significantly reduced

latency and keeping the delivery success rate.

Thank you ~

Biased Sink Mobility with Adaptive Stop Times for Low Latency Data Collection in Sensor Networks

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