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Coverage Optimization Strategy of Wireless Sensor Networks Based on Particle Swarm Optimization

Author: LinZhuLiang
Tutor: YuLiï¼›FengYuanJing
School: Zhejiang University of Technology
Course: Control Theory and Control Engineering
Keywords: Wireless sensor networks Particle swarm optimization Proposed and resources -oriented Particle evolution Coverage optimization
CLC: TN929.5
Type: Master's thesis
Year: 2009
Downloads: 393
Quote: 6
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Abstract


Wireless sensor network (Wireless Sensor Networks, WSNs) coverage control is a fundamental problem in wireless sensor network applications, it is a study in how to achieve the maximization of network coverage, to ensure a certain quality of service conditions to provide reliable monitoring and target tracking service. Effective coverage of the application of the control strategies and algorithms can be optimized distribution of the various resources of WSNs, contribute to the efficient use of energy of network nodes, the perception of the improvement of the quality of service and overall survival time prolonged. The combination of different environmental needs to design practical coverage strategy, is a hot research topic in the field. The wireless sensor network is a typical the groups network system, its coverage control problem with a group of self-organizing feature. In this paper, a wireless sensor network coverage optimization strategy based on swarm intelligence. First multi-particle swarm optimization algorithm and particle evolution of the particle swarm establish specific coverage optimization algorithm, and the experimental simulation, point out their inadequacies; thus the lack of these two algorithms based Particle Swarm and resources-oriented and intended The coverage optimization algorithm, the experimental results show the effectiveness of this algorithm. The specific research work and results are as follows:. Evolution of the particle swarm algorithm and particle particle swarm algorithm design coverage optimization simulation to analyze the impact of various parameters on the coverage performance through simulation results, the two algorithms coverage optimize the effectiveness analysis, pointing out their inadequacies and propose improvement ideas; 2. lack of these two algorithms, Particle Swarm Optimization and material resources algorithm with the proposed integration of design intends to material resources-oriented wireless sensor network The coverage optimization strategies. Same simulation experiment conducted under the same conditions as with the above-mentioned two algorithms coverage optimization of the three algorithms compare the effect of the combination of the improved algorithm having a better coverage optimization effect; 3 To further demonstrate that the algorithm the effectiveness of the two algorithms of the three algorithms with traditional genetic algorithm (CGA) and the new quantum genetic algorithm (NQGA) coverage optimization results in the same environment for comparative analysis, algorithm Experimental results show that the combination of the particle swarm algorithm particle evolution of multi-particle swarm algorithm, traditional genetic algorithm (CGA) and the new quantum genetic algorithm (NQGA) has better optimization results. 4 This thesis is based on the evolution of the particle swarm algorithm and particle coverage of multi-particle swarm algorithm optimization simulation experiments, design put forward a proposed material resources-oriented the particle swarm wireless sensor network coverage optimization strategies by different optimization algorithms in the same conditions under the simulation results are compared and analyzed, improved optimization algorithm to better improve the performance of wireless sensor networks.

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