MWSN中基于马尔可夫链的节点移动预测算法

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.

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

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.