基于群智能算法的WSNs动态联盟任务协同

Dynamic alliance collaborative tasks of WSNs based on swarm intelligence algorithm

针对无线传感器网络(Wireless sensor networks,WSNs)中单个节点的计算能力有限、完成数据发送任务比较困难的问题,提出一种协同处理的方式传送数据,可以将协同任务分为感知子任务和计算子任务。在传感节点任务协同的动态联盟中,引入基于粒子群算法优化蚁群算法(Particle swarm optimization ant colony algorithm,PSO-ACO)构建传感网的数据汇集路由树。利用传感器网络在采集数据之间的相关性,运用群智能算法来优化节点发送数据的传输路径,以保证动态联盟执行任务时的连续性,在一定程度上保证传感网的性能,从而降低了通信能耗。仿真实验表明:当传感器网络的感知节点与网络节点总数的比值小于28%时,网络监测性能最优,该文方案可以消除同一任务检测传感器节点冗余、降低系统能量消耗。

In response to the limited computing power and difficulties in completing data transmission of a single node in wireless sensor networks （ WSNs ） , this paper proposes a co-processing data-transmitting method that can divide the cooperative task into perception and computation. By introducing the particle swarm optimization ant colony algorithm （ PSO-ACO ） into the dynamic alliance of task collaboration of sensing nodes,the data collection routing tree of the sensor network is built. Based on the correlation of data collection in the sensor network, the swarm intelligence algorithm is used to optimize the transmission path of the data sent by the nodes to ensure the continuity of the dynamic alliance in executing the tasks. In this way,the performance of the sensor network is ensured to some extent and the communication energy consumption is reduced accordingly. Simulation experiments indicate that,the sensor network achieves optimal monitoring performance when the percentage of sensing nodes in the total number of nodes in the network is less than 28%. The scheme proposed here can eliminate the sensor node redundancy detected in the same task and reduce energy consumption of the system.