WSN latency optimization based on path calculation method

基于路径计算方法的WSN时延优化研究

¹ School of Communications and Information Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China
² State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an 710071, China

Received: 25 March 2022

Abstract

随着物联网(IoT)行业的快速发展, 无线传感器网络(WSN)融合云计算技术面临着任务处理时延高、传感器节点能量有限的挑战。因此, 提出了一种基于云雾网络架构的路径计算方法, 利用雾计算层的网络边缘设备计算资源, 将WSN监测任务合理地部署到指定边缘设备上完成处理, 以减少能耗制约下的任务处理时延。为了将任务有效地分配到雾计算层, 采用了一种任务映射规则, 将有向无环图表示的监测任务映射到无向图表示的雾计算层网络; 结合时延和能耗约束建立了一个关于寻求最优映射关系的二值优化问题; 采用模拟退火-离散二值粒子群优化(SA-BPSO)算法实现了对该优化问题的求解。仿真结果显示, 在数据量为10 Mb时, 该方法的时延性能相比较WSN融合云计算技术提高了约40%。

摘要

With the rapid development of the Internet of Things (IoT) industry, wireless sensor network (WSN) fusion cloud computing technology is encountering the challenges of high task processing latency and limited sensor node energy. Therefore, a path calculation method based on cloud computing network architecture is proposed. WSN monitoring tasks are deployed to specific edge devices reasonably by using the computing resources of network edge devices in the fog computing layer to reduce the task processing latency under the constraints of energy consumption. In order to efficiently assign tasks to the fog computing layer, a task mapping rule is used to map the monitoring tasks represented by the directed acyclic graph to the fog computing layer network represented by the acyclic graph. At the same time, a binary optimization problem for finding the optimal mapping relationship is established with time latency and energy constraints. Finally, the simulated annealing-discrete binary particle swarm optimization (SA-BPSO) algorithm is used to solve the optimization problem. The simulation results show that the latency performance under this method is about 40% higher than that of WSN fusion cloud computing technology when the data volume is 10 Mb.