WSN中遗传和K均值聚类的多跳路由算法

Multi-hop routing algorithm based on genetic algorithm and K-means clustering algorithm used in WSN

针对无线传感器网络中单跳传输能量消耗大、能耗不均衡以及簇间路由选择不合理的问题,提出一种基于遗传和K均值聚类的多跳路由算法。首先在最优分簇数的基础上,通过遗传算法优化K均值聚类的初始中心;其次为了减轻单簇首通信负载引入双簇首机制,主副簇首分别负责数据的收集、融合、发送、中继,用动态权值调整距离和能量因子对簇首选择的影响;最后综合考虑能量、传输距离等因素,建立中继节点的权值,各副簇首间形成簇间路由表,将数据以单跳或多跳的方式传输给基站。仿真结果表明,提出的路由算法能够有效地均衡网络能耗,延长网络的生命周期。

A multi-hop routing algorithm based on genetic algorithm(GA)and K-means clustering algorithm is proposed to eliminate high energy consumption,uneven energy consumption and unreasonable routing selection among clusters in wireless sensor network(WSN). On the basis of the optimal number of clusters,the initial center of K-means clustering is optimized by GA. Dual cluster heads mechanism is introduced to reduce the communication load of the single cluster head. The main and auxiliary cluster heads are respectively responsible for the data collection,fusion,transmission and relay. The dynamic weights are used to adjust the influence of distance and energy factors on the selection of cluster heads. The weights of the relay nodes are established by comprehensively considering the factors of energy and transmission distance. The inter-cluster routing table is formed among all the auxiliary cluster heads. The data is transmitted to the base station in the way of single-hop or multiple-hop.The simulation results show that the proposed routing algorithm can effectively balance the network energy consumption and prolong the network life cycle.