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Sensors 2007, 7(5), 628-648; doi:10.3390/s7050628

Distributed Particle Swarm Optimization and Simulated Annealing for Energy-efficient Coverage in Wireless Sensor Networks

State Key Laboratory of Precision Measurement Technology and Instrument, Tsinghua University, Beijing 100084, P. R. China
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Received: 25 April 2007 / Accepted: 8 May 2007 / Published: 10 May 2007
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Abstract

The limited energy supply of wireless sensor networks poses a great challenge for the deployment of wireless sensor nodes. In this paper, we focus on energy-efficient coverage with distributed particle swarm optimization and simulated annealing. First, the energy-efficient coverage problem is formulated with sensing coverage and energy consumption models. We consider the network composed of stationary and mobile nodes. Second, coverage and energy metrics are presented to evaluate the coverage rate and energy consumption of a wireless sensor network, where a grid exclusion algorithm extracts the coverage state and Dijkstra’s algorithm calculates the lowest cost path for communication. Then, a hybrid algorithm optimizes the energy consumption, in which particle swarm optimization and simulated annealing are combined to find the optimal deployment solution in a distributed manner. Simulated annealing is performed on multiple wireless sensor nodes, results of which are employed to correct the local and global best solution of particle swarm optimization. Simulations of wireless sensor node deployment verify that coverage performance can be guaranteed, energy consumption of communication is conserved after deployment optimization and the optimization performance is boosted by the distributed algorithm. Moreover, it is demonstrated that energy efficiency of wireless sensor networks is enhanced by the proposed optimization algorithm in target tracking applications. View Full-Text
Keywords: Wireless sensor network; deployment optimization; energy efficiency; particle swarm optimization; simulated annealing. Wireless sensor network; deployment optimization; energy efficiency; particle swarm optimization; simulated annealing.
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Wang, X.; Ma, J.-J.; Wang, S.; Bi, D.-W. Distributed Particle Swarm Optimization and Simulated Annealing for Energy-efficient Coverage in Wireless Sensor Networks. Sensors 2007, 7, 628-648.

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