东北大学学报:自然科学版 ›› 2018, Vol. 39 ›› Issue (7): 949-953.DOI: 10.12068/j.issn.1005-3026.2018.07.008

• 信息与控制 • 上一篇    下一篇

MWSN中基于马尔可夫链的节点移动预测算法

朱剑, 李佳政   

  1. (东北大学 计算机科学与工程学院, 辽宁 沈阳110169)
  • 收稿日期:2017-03-06 修回日期:2017-03-06 出版日期:2018-07-15 发布日期:2018-07-11
  • 通讯作者: 朱剑
  • 作者简介:朱剑(1981-),男,江苏镇江人,东北大学讲师,博士.冯明杰(1971-), 男, 河南禹州人, 东北大学副教授; 王恩刚(1962-), 男, 辽宁沈阳人, 东北大学教授,博士生导师.
  • 基金资助:
    中央高校基本科研业务费专项资金资助项目(N150404011); 教育部重大科技创新项目(N161608001).国家自然科学基金资助项目(51171041).

An Algorithm for Predicting Nodes Movement Based on Markov Chain in MWSN

ZHU Jian, LI Jia-zheng   

  1. School of Computer Science & Engineering, Northeastern University, Shenyang 110169, China.
  • Received:2017-03-06 Revised:2017-03-06 Online:2018-07-15 Published:2018-07-11
  • Contact: ZHU Jian
  • About author:-
  • Supported by:
    -

摘要: 在以往的移动无线传感器网络(mobile wireless sensor network,MWSN)中,热点分配问题没有得到很好的解决,网络利用率较低.通过预测移动节点的轨迹可以优化网络结构,提出结合加速度进行轨迹预测的算法MTPA:首先对节点的运动状态进行建模;其次建立了一步运动状态概率转移矩阵;最后以马尔可夫链为基础设计多步概率转移矩阵计算算法.为了验证算法性能,在STM32F407平台上进行了实验,结果表明,MTPA算法相比于传统的匀速预测算法与频率统计算法,预测准确度具有一定的优势,相关研究成果可以为MWSN提供基础.

关键词: 运动趋势, 马尔可夫链, 概率矩阵, 轨迹预测, 嵌入式

Abstract: In existing MWSN(mobile wireless sensor network), the problem of hot spot allocation was not solved well, and the utilization rate of network was low. It is possible to optimize the network structure by predicting the trajectory of mobile nodes. A trajectory prediction algorithm named MTPA combined with acceleration was proposed. Firstly, modeling the motion state of the node was established. Secondly, a step motion state probability transfer matrix was done. Finally, Markov chain based multi-step probabilistic transfer matrix algorithm was presented. In order to verify the performance of the algorithm, experiments were carried out on the STM32F407 platform, the experimental results show that comparing with traditional uniform prediction algorithms and frequency statistics algorithms, the prediction accuracy of MTPA has certain advantages, and relevant research results can be used in MWSN.

Key words: movement trend, Markov chain, probability matrix, trajectory prediction, embedded

中图分类号: