Optimal Self-placement of Heterogeneous Mobile Sensorsin Sensor Networks

Lidan MiaoAICIP ResearchOct. 19, 2004

2

Motivation

Collaborative efforts of many sensors.The placement affect the effectiveness of sensor network:

cost, coverage, network lifetime, etc.

Self-deployable sensor network is required in hazardous environment or some military applications.

3

Literature review

The sensor deployment has been an active research topic in sensor network.

The environment is sufficiently known and under control.

The deployment of mobile sensors based on centralized control.

Self-deployable sensor networks.

Assumption: Homogenous sensor platforms.

4

Objective

Minimize energy consumption convergence time the number of sensor nodes

Maximize network coverage

5

Proposed idea

The sensor deployment contains two interrelated issues:

Optimal placement design Convergence from initial state to optimal state by

sensor movement

The sensor locations are known before actual deployment.

6

Optimal placement algorithm

Stimulated from the MSFA design.The sensing field is represented by a 2D array, each grid point denotes a sensor site.Different sensor platforms form a mosaic pattern.

7

Self-deployment algorithm

Random movementMovement based on centralized controlSwarm intelligence-based movement

8

Stopping criterion

The optimal placement confines the sensor site of different sensor platforms.

3 sensor types

7 sensor types

9

Random Movement

At each position, the sensor randomly choose one direction to go.

It is very likely for one sensor to visit the same location many times.

It is not energy efficient. Low convergence rate.

10

Sensor Movement based on Centralized Control

The sensor needs to communicate with the base station.

Report sensor ID, location, sensor type

The deployment algorithm is carried out by the base station.

Location mapping (assignment problem)

The sensor movement is guided by the base station.Drawbacks:

It is not suitable for large scale problem. The failure of base station leads to the failure of the whole

system.

11

Swarm intelligence-based Movement

What is SI-based algorithm? Stimulated from social insects society, like ants,

bees, etc. simple rule carried by individual can lead to complex

behavior of the whole system. TSP, network routing, optimization, etc.

SI-based movement Each sensor carries a rule which guides the

movement and leads to an optimal configuration of the sensor network.

What’s the rule?

12

Rules carried by each sensor

The movement direction is based on the current location and sensor type (where I am and where to go?)

Rules carried by type 1 sensor:While x mod 2 !=0 and y mod 2 != 0 do

if (x+y) mod 2 !=0

if x mod 2 == 1 then

random walk N,S;

else

random walk W,E;

end

else

random walk NW,NE,SW,SE;

end

end

13

Performance metric

Network Coverage Probabilistic sensing model: Probabilistic coverage:

Convergence time The number of epochs of the last positioned sensor.

Energy consumption Mechanical movement: Communication:

TeerT

t

gsdn

ntt /cov'

1

),(

otherwise 0

if Rdec

d

p

movemove deE

BeE elecrcv

BdeBeE ampelectran2

g

14

Simulation and Evaluation…

Simulation environment Sensing field: 50m×50m -> 8×8 grid points 64 sensors of 5 different types: r=1/4:1/4:1/4:1/8:1/8 Sensing distance: 10m Initial energy: Unit consumption in movement: Electric circuitry: Transmitter amplifier: Location of base station: (0,0)

Jemove 01.0~0JEinit 1

2~0 , moveelec ee

500/elecamp ee

15

Simulation and Evaluation…

Convergence time

16

Simulation and Evaluation…

Network coverage

17

Simulation and Evaluation…

Energy consumption: moveleft eE vs.

random SI-based

18

Simulation and Evaluation…

Energy consumption: vs.leftE

002.0movee 008.0movee

19

Simulation and Evaluation…

Energy consumption: BEleft vs.

Ratio =0.1 Ratio =0.446

20

Conclusion

Optimal placement provides reliable coverage.Comparison of movement strategies.

Random movement consumes more energy, no communication is needed.

Centralized control converges the fast at the cost of full communication, where the energy consumption in communication is dominant.

SI-based method provides a good tradeoff in terms of convergence time and energy consumption.