基于动态联盟和蚁群算法的任务协同框架

Task Collaboration Framework Based on Dynamic Coalition and Ant Colony Algorithm

单个节点能力受限,无线传感器节点需要协同完成任务。针对该问题,将协同任务分为感知子任务和计算子任务,提出基于动态联盟和蚁群算法的任务协同框架。根据应用需求选择感知节点形成初始联盟分配感知任务,当感知节点与节点总数的比值小于32%时,网络监测性能最优,引入自适应蚁群算法构建数据汇集路由树,利用同一任务数据的强相关性优化数据传输路径,从而降低通信能耗。

Single node capability is limited,node in Wireless Sensor Network（WSN） need collaborate with neighbors to complete a task.Aiming at this problem,this paper divides the collaborate task into sensor subtask and computing subtask,and proposes a task collaboration framework based on dynamic coalition and Ant Colony Algorithm（ACO）.It chooses the perception node according to the application need,those nodes form an initial dynamic coalition,and assign perception task on the coalition member.When the ratio of perception nodes to the whole nodes is below 32%,the network monitoring performance is best.It adopts self-adaptive ACO to construct data aggregation routing tree,optimizes data transfer path based on strong data relation of a same task,and reduces communication energy consumption.