optimal self-placement of heterogeneous mobile sensors in sensor networks lidan miao aicip research...
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Optimal Self-placement of Heterogeneous Mobile Sensorsin Sensor Networks
Lidan MiaoAICIP ResearchOct. 19, 2004
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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.
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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.
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Objective
Minimize energy consumption convergence time the number of sensor nodes
Maximize network coverage
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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.
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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.
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Self-deployment algorithm
Random movementMovement based on centralized controlSwarm intelligence-based movement
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Stopping criterion
The optimal placement confines the sensor site of different sensor platforms.
3 sensor types
7 sensor types
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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.
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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.
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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?
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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
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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
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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
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Simulation and Evaluation…
Convergence time
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Simulation and Evaluation…
Network coverage
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Simulation and Evaluation…
Energy consumption: moveleft eE vs.
random SI-based
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Simulation and Evaluation…
Energy consumption: vs.leftE
002.0movee 008.0movee
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Simulation and Evaluation…
Energy consumption: BEleft vs.
Ratio =0.1 Ratio =0.446
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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.