计算机应用 ›› 2012, Vol. 32 ›› Issue (05): 1228-1231.

• 网络与通信 • 上一篇    下一篇

群混合算法应用于异构传感网络节点的优化部署

张斌1,毛剑琳2,李海平1,陈波1   

  1. 1. 昆明理工大学 信息工程与自动化学院,昆明 650500
    2. 昆明理工大学 信息工程与自动化学院,昆明 65000
  • 收稿日期:2011-10-19 修回日期:2011-12-08 发布日期:2012-05-01 出版日期:2012-05-01
  • 通讯作者: 张斌
  • 作者简介:张斌(1987-),男,江苏盐城人,硕士研究生,主要研究方向:无线传感网络覆盖控制;毛剑琳(1976-),女,广西桂林人,副教授,主要研究方向:无线传感网络、网络资源分配、网络优化控制系统;李海平(1985-),男,湖南郴州人,硕士研究生,主要研究方向:无线传感器网络时隙分配优化算法;陈波(1989-),女,湖南湘潭人,硕士研究生,主要研究方向:无线传感器网络控制系统。
  • 基金资助:

    国家自然科学基金资助项目(61074145);云南省应用基础研究基金资助项目(2009ZC050M);云南省教育厅科学研究基金资助项目(08Y0093)

Swarm hybrid algorithm for nodes optimal deployment in heterogeneous wireless sensor network

ZHANG Bin1,MAO Jian-lin2,LI Hai-ping1,CHEN Bo1   

  1. 1. School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming Yunnan 650500, China
    2. School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming Yunnan 65000, China
  • Received:2011-10-19 Revised:2011-12-08 Online:2012-05-01 Published:2012-05-01
  • Contact: ZHANG Bin

摘要: 针对异构传感网络节点初始随机部署时产生覆盖盲区和覆盖冗余的问题,以降低节点成本和提高网络覆盖率为目标,引入ε-目标约束法,提出一种基于粒子群算法和鱼群算法的群混合算法。该群混合算法首先建立个体中心的概念,将鱼群算法的聚群行为和追尾行为的思想引入到粒子群算法中以快速寻取个体的最优位置的解域,再利用粒子群算法对个体的速度和位置进行迭代寻优。仿真结果表明,该群混合算法与标准粒子群算法和标准鱼群算法相比,在网络覆盖率和成本目标之间能达到更好的平衡和优化。

关键词: 无线传感网络, 异构, 覆盖率, 覆盖育区, 覆盖冗余, 粒子群算法, 鱼群算法

Abstract: The coverage problem is a basic problem in the wireless sensor networks, which indicates the Quality of Service (QoS) of sensing by wireless sensor networks. A lot cover blind areas and cover redundancies will be produced, when the nodes are deployed initially in the networks. A hybrid algorithm was proposed to deploy the heterogeneous network nodes reasonably to improve the coverage ratio and reduce the cost of the nodes,which introduced the ε-target constraint method based on Particle Swarm Optimization (PSO) and Fish Swarm Algorithm (FSA). The swarm hybrid algorithm firstly set up the concept of individual center, to quickly search the best solution domain of the individuals' locations, introducing the idea of the cluster behavior and tracing cauda behavior into the PSO, and then used the PSO to find the optimized speed and optimized location of the individuals. The simulation results show that the swarm hybrid algorithm is better than the standard PSO and the standard FSA in pursuing the balance and optimization between the coverage ratio and the cost of the networks.

Key words: Wireless Sensor Network (WSN), heterogeneous, coverage ratio, cover blind areas, cover redundancies, particle swarm algorithm, fish swarm algorithm

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