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Coverage Optimization Strategy Of Wireless Sensor Networks Based On Particle Swarm Optimization

Posted on:2010-10-23Degree:MasterType:Thesis
Country:ChinaCandidate:Z L LinFull Text:PDF
GTID:2178360278451129Subject:Control theory and control engineering
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Wireless Sensor Networks (WSNs) coverage control is a basic problem in WSNs applications. It is a study on how to maximize the network coverage to provide reliable monitoring and tracking services in the guaranteed quality of service conditions. Effective strategies of the coverage control and algorithms can be used to optimize the allocation of resources of WSNs, increase the efficiency of the energy usage of network nodes, and improve the perceived quality of service and the overall survival time. How to combine different environmental demands and design a practical strategy for coverage is a significant research field.Wireless Sensor Networks is a typical group of network system, whose coverage control problem has the characteristics of self-organized groups. The present research constructs a wireless sensor network coverage optimization strategy based on swarm intelligence. Firstly, a specific coverage optimization algorithm making use of the elementary Particle Swarm Optimization (PSO) and the evolution of Multi-particle Particle Swarm Optimization (MPSO) is proposed. The shortages of the algorithms are shown by simulations, in order to cover the shortages of these two algorithms, the Virtual Material Force-directed Particle Swarm Optimization (VMFPSO) is proposed, the experiments show the effectiveness and superiority of the newly proposed algorithms. The main research work and results are as follows:1. Coverage optimization experimental simulation is designed by using elementary PSO and MPSO. The effect of various parameters on the coverage performance is analyzed according to the simulation results. Compared with the coverage optimization effect of the two algorithms, their inadequacies are pointed out and the ideas for improvement are proposed.2. The VMFPSO strategy is presented, which integrate the elementary PSO and virtual material force algorithm. The simulation experiment of VMFPSO is carried out by using the two algorithms under the same conditions. The simulations show that the combination algorithm has a better optimization performance than that of the elementary PSO or MPSO.3. To further prove the effectiveness of the algorithm, the coverage optimization effect of the above-mentioned three algorithms is analyzed. The experiments demonstrate that the combination algorithm outperforms that of the elementary PSO, MPSO, the Conventional Genetic Algorithm (CGA) or the New Quantum Genetic Algorithm (NQGA).4. The VMFPSO coverage optimization strategy of wireless sensor network is designed based on the experimental simulation of PSO and MPSO. It is proved that the improved algorithm can effectively improve the performance of WSNs through comparing and analyzing the different optimization algorithm results under the same conditions.
Keywords/Search Tags:wireless sensor networks, particle swarm optimization, virtual material force-directed, particle evolution, coverage optimization
PDF Full Text Request
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