Volume 38, Number 2, April 2020
|Page(s)||333 - 340|
|Published online||17 July 2020|
Data Collection Method of Large Scale WSNS Mobile Node Based on Compressed Sensing and Intelligent Optimization
School of Computer Science, Xi'an Aeronautical University, Xi'an 710077, China
2 School of information and Engineering Sciences, Norwegian University of Science and Technology, Alesund
3 School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, China
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.
Key words: wireless sensor networks / compressive sensing / discrete elastic collision optimization / data collection
关键字 : 无线传感器网络 / 压缩感知 / 离散弹性碰撞优化 / 数据收集
© 2019 Journal of Northwestern Polytechnical University. All rights reserved.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.