摘要
在加权分簇算法(WCA)中引入预测机制,即在算法的路由维护阶段嵌入时间序列模型(ARIMA),用以预测网络节点的地理位置。利用ARIMA模型实时预测出节点下一时刻的地理位置,并以此计算出节点的累计保持时间预测值。将通过预测得到的累计保持时间值与时间预警阈值进行比较,在簇结构即将不稳定时,即在链路断开之前,提前启动预修复过程,寻找新的路由,降低网络拓扑动态变化的影响,维护簇结构的稳定。仿真结果表明,相对于LO-WID以及没有加入预测机制的RLWCA,ARP-LWCA算法大幅度提高了网络的分组投递率,降低了网络的归一化开销,并且使得路由中断次数有了明显减少,改善了网络的整体性能。
This paper introduced ARIMA prediction mechanism in weighted clustering algorithm （WCA）. During routing maintaining process, the ARIMA is used to predict the network node location. Using the established ARIMA model, the algorithm is able to predict geographical position of the node at next time. In this way, it can calculate aggregate holding time of the nodes. Then, the predicted aggregate holding time is compared with time warning threshold, if cluster structure will be unstable, route recovery process will be activated before the link fails. And it will search new rou- ting in order to avoid frequent link failures. Thus, the influence to the routing protocols brought by the dynamic changes of network topology can be reduced, and the stability of the cluster structure will be maintained. The simulation results show that, compared with LOWID and RLWCA not joined the forecasting mechanism, the proposed ARP-LWCA algorithm can dramatically improve the network packet delivery rate, reduce the network normalized overhead and the number of routing interruptions significantly. So, the network performance is improved.
出处
《计算机科学》
CSCD
北大核心
2012年第3期47-50,共4页
Computer Science
基金
国家自然科学基金(10878017)资助
