J4 ›› 2010, Vol. 37 ›› Issue (6): 1125-1131.doi: 10.3969/j.issn.1001-2400.2010.06.026

• 研究论文 • 上一篇    下一篇

一种无线传感器网络数据包转发的博弈论算法

赵永辉;史浩山   

  1. (西北工业大学 电子信息学院,陕西 西安  710072)
  • 收稿日期:2009-11-11 出版日期:2010-12-20 发布日期:2011-01-22
  • 通讯作者: 赵永辉
  • 作者简介:赵永辉(1981-),男,西北工业大学博士研究生,E-mail: zyh909090@126.com.
  • 基金资助:

    教育部博士点基金资助项目(20050699037)

Game theoretical packet forwarding algorithm in wireless sensor networks

ZHAO Yong-hui;SHI Hao-shan   

  1. (Dept. of Elec. & Info., Northwestern Polytechnical Univ., Xi'an  710072, China)
  • Received:2009-11-11 Online:2010-12-20 Published:2011-01-22
  • Contact: ZHAO Yong-hui

摘要:

提出一种基于动态贝叶斯博弈的无线传感器网络数据包转发算法(PFDBG).该算法将数据包转发视作一种可观察行动的多阶段不完全信息博弈,相邻节点之间根据对方历史行为的评估,采取贝叶斯修正方法对其能量水平(私有类型)进行合理的计算和预测,并利用该预测值计算最大化期望收益函数的解,来作为双方在下个博弈阶段的最优发包策略; 证明了数据包转发博弈中贝叶斯纳什均衡的存在性.理论和仿真结果表明,PFDBG算法在保证节点获得较高吞吐量的同时,能够很好地适应网络规模的变化,具有计算开销小、传输可靠性高和能量消耗低的特点,其性能优于现有的其他算法.

关键词: 无线传感器网络, 博弈论, 数据包转发, 最优策略, 贝叶斯纳什均衡, 可靠性

Abstract:

A packet forwarding algorithm based on the dynamic Bayesian game named PFDBG is presented for wireless sensor networks. Packet forwarding is regarded as a multi-stage game with incomplete information and observable actions, the adjacent nodes take the Bayesian amendment method to reasonably calculate and predict the opponent's energy level (private type) based on evaluation of its historical actions, and use the predicted value to obtain the solution which maximizes the expected utility function. The solution is also the optimal forwarding strategy that the nodes will adopt in the next stage of the game; the existence of the Bayesian Nash Equilibrium in the game is proved. Theoretical analysis and experimental results show that the PFDBG algorithm not only ensures the higher throughput of nodes, but also adapts well to the network size, and that it has a small computational overhead, high transmission reliability and low energy consumption, with its performance superior to that of other existing algorithms.

Key words: wireless sensor networks, game theory, packet forwarding, optimal strategy, Bayesian Nash Equilibrium, reliability

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