组移动模型中基于模型特点的数据收集分簇算法

如今移动传感器网络在各个领域已起到重要作用。目前，移动传感器网络在军事、民用、科研等领域的应用价值都很高。而数据收集问题一直是这方面科研中必须被突破的难题。组移动模型是移动传感网络中的一个重要的移动模型，在本领域内都起到重要作用，然而有关移动传感器网络组模型的数据收集算法却屈指可数。提出了一种组移动模型中基于模型特点的数据收集分簇算法——MCBC算法。该算法根据节点的速度和角度之间的关系确定两节点是否同组，再从中选择簇头，有效地利用了组移动模型中节点的移动特征。仿真结果表明，在组移动模型中，该算法能取得较好的性能。

A Model-Characteristic-based Clustering Algorithm for Data Collecting in the Group Mobility Model

Today, mobile sensor network has played an important role in various fields. Currently, the mobile sensor network is very valuable in the military, civilian, research and other application areas. The data collection is always the research problem in this area. It should be broken. Group mobility model is an important model for mobile sensor network, which plays an important role in many areas, however, data collection algorithms of the group model about mobility sensor network are very few. In this article, a model-characteristic-based clustering algorithm for data collecting in the group mobility model, referred to as MCBC, is put forward. The algorithm determines whether the two nodes are in the same group based on the relationship of the node speed and angle between them, and then chooses the cluster head. This method makes effective use of movement characteristics of the group mobility model node. Simulation results show that in the group mobility model, the algorithm can achieve better performance.