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

Abstract

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.