基于改进粒子群算法和特征点集的无线传感器网络覆盖问题研究  

Area Coverage Problem Based on Improved PSO Algorithm and Feature Point Set in Wireless Sensor Networks

作　　者：丁旭[1] 吴晓蓓[1] 黄成[1] 

机构地区：[1]南京理工大学自动化学院,江苏南京210094

出　　处：《电子学报》2016年第4期967-973,共7页Acta Electronica Sinica

基　　金：教育部博士点专项基金(No.20113219110028);江苏省自然科学基金(No.BK2012803)

摘　　要：本文针对基于网格点的区域覆盖算法未考虑网络的固有特征,导致算法存在近似及复杂度偏高等问题,通过研究区域覆盖的特征,结合概率感知模型,对区域内两点的覆盖率关系进行分析,定义了特征点集的概念;对特征点集进行建模,将区域覆盖转化为基于特征点集的优化问题.利用改进粒子群算法解算此优化问题,通过惯性权重及局部增强因子扰动项,避免其陷入早熟状态;同时,针对集中式PSO算法不适用于无线传感网的问题,本文提出了一种并行分区式策略.仿真分析验证了所提算法的优越性和特征点距上界的存在性,该方法为区域覆盖问题的研究提供了新的思路.Traditional grid point-based area coverage methods are committed to algorithm optimization, causing coarse approximation and high complexity problems. In order to solve these problems, based on the probabilistic sensing model, we first study the sensing probabilities of two adjacent points and obtain the fundamental mathematical relationship between them. According to this relationship, we define the concept of feature point set （FPS） to character the area. Then, we trans- form the probabilistic area coverage into optimization problem of FPS. Further, we design an improved particle swarm opti- mization （1WPSO） algorithm to solve this optimization problem, which can effectively avoid the premature problems in the convergence of PSO algorithm. Finally, through extensive simulations, we demonstrate that our algorithm outperforms the proposed solutions significantly, and provides a new train of thought for area coverage problem.

关 键 词：无线传感器网络 覆盖约束优化 概率感知模型 特征点集 惯性权重 并行分区式粒子群算法 

分 类 号：TP393.02[自动化与计算机技术—计算机应用技术]