基于小波神经网络预测的Ad Hoc网络分簇算法

针对Ad Hoc网络拓扑结构的动态特性,利用小波神经网络预测模型对节点地理位置进行预测.将预测的总保持时间与阈值比较,可以测得簇在下一时刻的稳定性.如果该簇结构在下一时刻趋于不稳定,则在链路失效之前启动路由预修复机制,以避免链路频繁断裂,从而大幅提高了网络性能.仿真结果表明,与传统最小ID算法和未加预测机制的LWCA分簇算法进行比较,所提出的分簇算法分组投递率分别提高了7%和5%,路由中断次数降低了约63%和50%.

WNNP-Based Clustering Algorithm for Ad Hoc Networks

According to the dynamic characteristics of ad hoc network topology, a wavelet neural network prediction (WNNP) model was used to predict the geometrical location of the nodes. Comparing the predicted total holding time with the threshold, the stabilization of a cluster in next time can be measured. If the cluster tends to be unstable in next time, a routing pre-repair mechanism can be initiated before the link failure to avoid frequent breaks of links. Thus the network performance is significantly improved. Simulation results show that compared with the lowest-identifier (lowest ID) algorithm and location-based WCA (LWCA) which has no prediction model, WNNP-LWCA can improve by 7% and 5% of the packet delivery rate, reduce by 63% and 50% of the broken routing number, and maintain the stabilization of the cluster.