计算机应用 ›› 2016, Vol. 36 ›› Issue (8): 2157-2162.DOI: 10.11772/j.issn.1001-9081.2016.08.2157

• 网络与通信 • 上一篇    下一篇

基于演化博弈论的无线传感网监测节点分群算法

刘保见, 张效义, 李青   

  1. 信息工程大学 信息系统工程学院, 郑州 450001
  • 收稿日期:2016-02-02 修回日期:2016-03-24 出版日期:2016-08-10 发布日期:2016-08-10
  • 通讯作者: 李青
  • 作者简介:刘保见(1989-),男,河南信阳人,硕士研究生,主要研究方向:无线传感器网络组网;张效义(1966-),男,河南郑州人,教授,硕士,主要研究方向:无线通信系统、软件无线电、无线传感器网络、可见光通信;李青(1975-),女,河北正定人,副教授,博士,主要研究方向:无线传感器网络、可见光通信。
  • 基金资助:
    国家科技重大专项(2014ZX03006003)。

Evolutionary game theory based clustering algorithm for multi-target localization in wireless sensor network

LIU Baojian, ZHANG Xiaoyi, LI Qing   

  1. School of Information System Engineering, Information Engineering University, Zhengzhou Henan 450001, China
  • Received:2016-02-02 Revised:2016-03-24 Online:2016-08-10 Published:2016-08-10
  • Supported by:
    This work is partially supported by the National Science and Technology Major Project (2014ZX03006003).

摘要: 针对大规模无线传感器网络多辐射源定位中,辐射源公共覆盖范围内监测节点能耗过高造成网络寿命降低的问题,提出一种基于演化博弈理论(EGT)的传感网监测节点分群算法。通过将最优节点集的搜索空间映射到博弈的策略组合空间,以博弈的效用函数为目标函数构建了非合作博弈模型;利用纳什均衡分析及均衡的扰动恢复过程实现目标优化;设计了分群算法以优化节点集组成相应的群参与最终的定位。以接收信号强度指示(RSSI)/信号到达时间差(TDOA)两轮定位为例,将该算法与典型的最近邻算法、基于离散粒子群优化(DPSO)的分群算法在定位精度和网络寿命方面作对比。仿真结果表明,该分群算法避免了多辐射源公共覆盖区域内节点能耗较高的问题,延长了网络寿命,同时保证了对辐射源的定位。

关键词: 无线传感器网络, 多辐射源定位, 分群算法, 演化博弈论, 纳什均衡, 网络寿命, 定位精度

Abstract: Aiming at the problem that the network lifetime was reduced because of the high energy consumption of the nodes covered by multiple radiation sources in large scale Wireless Sensor Network (WSN), a new clustering algorithm based on Evolutionary Game Theory (EGT) was proposed. The non-cooperative game theory model was established by mapping the search space of the optimal node sets to the strategy space of the game and using the utility function of the game as objective function respectively; then the optimization objective was achieved by using Nash equilibrium analysis and the perturb-recover process of equilibrium states. Furthermore, a detailed clustering algorithm was presented to group the optimal node sets into clusters for further location. The proposed algorithm was compared with the nearest-neighbor algorithm and the clustering algorithm based on Discrete Particle Swarm Optimization (DPSO) algorithm in the location accuracy and the network lifetime under the RSSI (Received Signal Strength Indication)/TDOA (Time Difference of Arrival) two rounds cooperative location scheme. Simulation results show that the proposed algorithm decreases the energy consumption of the nodes covered by multiple radiation sources, prolongs the network lifetime and guarantees the precise location.

Key words: Wireless Sensor Network(WSN), multiple radiation sources localization, clustering algorithm, evolutionary game theory, Nash equilibrium, network lifetime, location accuracy

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