基于平滑高斯半马尔可夫模型的移动无线传感器网络生成树算法

A Smooth Gauss-Semi-Markov Model Based Spanning Tree Algorithm for Mobile Wireless Sensor Networks

摘要:

针对以往移动无线传感器网络研究只是单纯地对移动群体进行分簇而没有充分利用组群移动的内部稳定性的问题,结合组移动模型中节点运动的规律和内聚性原理,采用平滑高斯半马尔可夫移动模型刻画组内单个节点移动特征,构建了一种适合移动网络的稳定生成树算法(GM-base stable spanning tree algorithm, GSST)；实验证明,该算法从单个节点运动变化入手,在预测未来节点运动情况,选择稳定的链路构建网络结构方面,提高了移动网络的稳定性；同时,利用树的分层特征,简化移动网络的组网过程,并实现网络重组局部化；该算法有效延长节点存活率,均衡数据传输量。

Abstract:

Aimed at the previous research on wireless sensor nodes in a mobile group, the mobile population were only clustered and didn’t make full use of internal stability, combined with the cohesion principle and the law of the nodes’ motion in a group mobility model, using a smooth Gauss-Semi-Markov model to describe a single node’s moving characteristics within a mobile group, construct a stable spanning tree algorithm for mobile networks (GM-base stable spanning tree algorithm, referred to as GSST). Experiments show that the algorithm, in the light of a single node movement variety, predicts the future movement, selects stable links to construct the network structure, and improves the mobile networks stability, At the same time, which use tree’s hierarchical feature, simplify the networking process, and realize the network reorganization localization, which can effectively prolong the nodes’ survival rate, balance the amount of data transmission.