摘要： 为提高环境监测应用中节点部署的准确性，提出一种基于空间权重与模糊感知的粒子群优化算法。引入空间权重量化区域重要性，建立模糊感知模型描述节点感知性能，设计加权覆盖率作为算法评价函数。在此基础上，挖掘感知模型中的粒子飞行特性，并利用权重引力优化粒子进化方程，提高算法的寻优能力。仿真结果表明，与粒子群优化算法、虚拟力算法和外推人工蜂群算法相比，该算法最高可使目标覆盖率提升13%，节点数减少15%。

关键词: 环境监测, 无线传感器网络, 节点部署, 空间权重, 模糊感知, 粒子群优化

Abstract: In order to improve the accuracy of node deployment in environmental monitoring application,this paper proposes a Particle Swarm Optimization(PSO) algorithm based on spatial weight and fuzzy perception.The spatial weight is introduced to quantify the importance of the region,the fuzzy perception model is established to describe the perceived performance of the nodes,and the weighted coverage rate is designed as the algorithm evaluation function.On this basis,the particle flight characteristics in the perception model are excavated,and the particle evolution equation is optimized by using the weighted gravity to improve the optimization ability of the algorithm.Simulation results show that,compared with PSO algorithm,Virtual Force(VF) algorithm and Extrapolation Artificial Bee Colony(EABC) algorithm,the proposed algorithm can increase the target coverage rate by up to 13% and reduce the number of nodes by up to 15%.

Key words: environmental monitoring, Wireless Sensor Network(WSN), node deployment, spatial weight, fuzzy perception, Particle Swarm Optimization(PSO)

中图分类号: 

• TP393