基于混沌粒子群的有向传感器网络覆盖优化

Coverage Optimization of Directional Sensor Networks Based on Chaos Particle Swarm

研究有向传感器网络覆盖控制问题,全向传感器不能直接应用于有向传感器网络。为改善有向传感器网络覆盖性能,在分析有向感知模型的基础上,提出了应用混沌粒子群的有向传感器网络覆盖优化算法,可随机部署有向传感器网络,以网络区域覆盖率为优化目标,利用粒子群算法较快的收敛速度和混沌搜索的遍历性、随机性,通过调整传感器节点的主感方向,减少网络感知重叠区和感知盲区。仿真结果表明,改进算法能有效提高网络覆盖率。与基本粒子群等覆盖优化算法相比,改进算法覆盖优化性能更好。

The research is on the control of directional sensor networks coverage. To improve the performances of directional sensor networks coverage, the optimization method based on chaos particle swam was proposed based on the analyses of the directional sensing model. Targeting at randomly deployed directional sensor networks and optimizing the network coverage rate, the algorithm utilizes swarm optimization＇s faster convergence speed and chaos search＇s ergodicity and stochastic property to reduce the sensing overlapping or senseless regions by adjusting the major sensing direction of sensing nodes. The simulation results indicate that the proposed algorithm can improve the networks coverage. Compared with the particle swarm coverage algorithm, the proposed algorithm has better performances.