WSN低功耗低时延路径式协同计算方法

针对云计算应用于无线传感器网络(Wireless Sensor Network,WSN)时延敏感型业务时存在的高传输时延问题,提出了一种WSN低功耗低时延路径式协同计算方法。该方法基于一种云雾网络架构开展研究,该架构利用汇聚节点组成雾计算层;在数据传输过程中基于雾计算层的计算能力分步骤完成任务计算,降低任务处理时延;由于汇聚节点计算能力较弱,时延降低将导致能耗增加,WSN工作寿命减短,为此提出能耗约束下的任务映射策略,并利用离散二进制粒子群优化(Binary Particle Swarm Optimization,BPSO)算法解决能耗约束下的时延优化问题。仿真结果表明,在相同的能耗约束下,对比其他算法,基于BPSO算法得出的映射方案能有效降低业务处理时延,满足时延敏感型业务的需求。

Low-power Low-latency Path-based Collaborative Computing Scheme for WSN

A low-power low-latency path-based collaborative computing scheme is proposed to solve the problem of high transmission delay when cloud computing is applied to delay sensitive service in wireless sensor networks.The scheme is based on a cloud and fog network architecture,and the fog layer is composed of the sink nodes.The computing capacity of fog nodes is used to gradually complete the task in the process of data transmission.The computing power of sink nodes is weak and the reduction of latency would lead to increase of power consumption,the working life of WSN is shorted.Thus,a task mapping strategy under energy consumption is proposed and a binary particle swarm optimization(BPSO)algorithm is used to solve delay optimization problem under energy consumption constraints.Simulation results show that the proposed path computing scheme can effectively reduce the latency compared with other algorithms under the same energy consumption constraints.