基于蚁群优化的WSN负载均衡数据汇集算法

Load-balancing data gathering algorithm for wireless sensor networks based on ACO

针对无线传感器网络(WSN)数据汇集应用中负载分配不均衡,使得网络节点出现早死,网络寿命缩短的问题,提出了一种基于蚁群优化(ACO)的负载均衡的数据汇集(Load-balancing data gathering based on ACO,L-ACO)算法。根据不同的任务,L-ACO算法将蚂蚁分为三类:前向探索蚂蚁(FD-ANT)、前向运输蚂蚁(Fr-ANT)和后向蚂蚁(B-ANT)。此算法定义蚂蚁的转移概率与路径信息素成反比,并采用父节点负载作为启发因子,使得蚂蚁趋向于走负载低的路径。通过前向蚂蚁与后向蚂蚁的分工合作,使得各条路径上的负载逐渐趋于均衡,从而延长网络寿命。仿真实验表明L-ACO算法可行。

To solve the problem that the unbalanced load in wireless sensor networks （WSNs） performing data gathering causes premature death of sensor nodes and shortens the network lifetime, the paper proposes an algorithm of L-ACO, a new load-balancing data gathering algorithm based on the ant colony optimization （ACO） for WSNs. According to different tasks, the algorithm divides ants into three categories： forward ants （FD-ANT）, forward transport ants （FT-ANT） and backward ants （B-ANT）. In L-ACO, ants choose the next hop according to the lower pheromone higher probability principle, and the load of parents is taken as the heuristic factor. Through the cooperation of forward ants and backward ants, a certain degree load-balancing is reached step by step, and the network lifetime is extended. The simulation results validated the effectiveness of this approach.