基于学习自动机的无线传感网能量均衡分簇算法

Energy Balanced Clustering Algorithm Based on Learning Automata for Wireless Sensor Network

优化簇首选择、均衡节点能量负载以延长网络存活时间,一直是无线传感器网络分簇协议研究的重点。针对无线传感器网络节点随机分布的情况,在基于学习自动机(Learning Automata,LA)的ICLA算法基础上,提出一种兼顾节点密度的能耗均衡分簇算法。在簇头选举方面,综合考虑节点剩余能量和节点密度,利用学习自动机与周围环境进行信息交互和动作奖惩,选择出相对较优的簇头;根据簇首与基站距离和其节点密度构造大小非均匀的簇,实现不同位置不同网络疏密程度下簇内和簇间能耗互补均衡;构造了基于簇首剩余能量、簇内节点密度和传输距离的评价函数,并运用贪婪算法选择出最优中转簇首进行多跳传输。仿真实验结果表明,该算法能选择出更为合理的簇头,有效地均衡网络能量负载,延长网络生存时间。

It has been the focus of the research on clustering protocols in wireless sensor networks （WSNs）for cluster heads selection optimization and the energy load balancing among all sensor nodes to extend the network lifetime. Aiming at the random distribution of nodes in WSNs, basing on ICLA algorithm which adopts the learning automata （LA）, an energy balanced unequal clustering algorithm with the node density is proposed and evaluated in this paper. In the cluster head election phase, overall considering the residual energy and the node density, and moreover, adopting the LA for information exchange with the surrounding environment, it can choose relatively better cluster heads. According to the distance between cluster heads and the base station and the node density, it forms unequal clusters to balance energy load of intra- and inter-clusters in different positions and node density degrees of networks. The algorithm adopts an evaluation function of neighbor cluster heads, which considers the energy of cluster head, node density in cluster and distance from each cluster head to the base station, so it can choose the transit cluster heads using greedy algorithm for multi-hop transmission. Simulation results show that it can choose relatively more rea- sonable cluster heads, efficiently balance the energy load of all nodes and significantly prolong the network lifetime.