采用压缩感知与智能优化的大规模WSNs移动稀疏数据收集

Data Collection Method of Large Scale WSNS Mobile Node Based on Compressed Sensing and Intelligent Optimization

针对大规模无线传感网数据处理网络流量大、任务时延高的缺陷,提出了一种基于自适应块压缩感知与离散弹性碰撞优化算法的移动节点数据收集方案。首先,通过分析网络分块与节点部署之间的关系,提出自适应块压缩感知数据采集策略,实现传感器节点基于自适应网络块压缩感知数据采集;设计移动节点数据采集路径规划策略和多移动节点协同计算机制,通过采用适应度值约束变换处理技术和并行离散弹性碰撞优化算法,达到均衡网络节点能耗和降低数据处理任务时延的目的。最后,仿真结果表明,该数据收集方案能够有效实现大规模传感网数据高效处理,而且降低了网络流量和网络任务时延,更好均衡了网络节点能耗。

Aiming at the defects of large-scale large scale wireless sensor network data processing network traffic and high task latency,a data collection scheme of mobile node based on discrete elastic collision optimization algorithm and adaptive block compression sensing is proposed.Firstly,by analyzing the relationship between the network partitioning and the node deployment,an adaptive block compressed sensing data collection strategy is proposed to realize sensor node based on adaptive network block compressed sensing data collection.Designing mobile node data acquisition path planning strategy and multiple mobile nodes The collaborative computer system adopts the fitness value constraint transformation processing technology and the parallel discrete elastic collision optimization algorithm to achieve the purpose of balancing network node energy consumption and reducing data processing task delay.Finally,the simulation results show that the data collection scheme can effectively realize high-efficiency processing of large-scale sensor network data,and reduce network traffic and network task delay,and better balance network node energy consumption.