基于存储缩减方案的WSN数据恢复算法

文章摘要

基于存储缩减方案的WSN数据恢复算法

The WSN Data Recovery Algorithm Based On Storage Reduction Scheme

投稿时间：2020-10-17 修订日期：2020-10-17

DOI：

中文关键词: 无线传感器网络存储缩减传输优化线性规划理论骨干汇聚链路网络抖动

英文关键词: Wireless sensor network Storage reduction Transmission optimization Linear programming theory Backbone convergence link Network jitter

基金项目:1、课题名称：2018年度校级科研立项课题“基于物联网技术的智慧实验室智能管理系统研究与设计”2、课题名称：2019年度安徽省高校科研立项课题“基于人工智能技术的“平安校园”智能安防系统研究与设计”3、课题名称：2019年安徽省级校企合作示范实训中心“滁州职业技术学院滁州市易搜信息技术有限公司软件开发实训中心”教学研究项目

作者	单位	邮编
王文飞^*	滁州职业技术学院，信息工程学院	239000

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中文摘要:

针对WSN网络数据接收过程中存在的恢复成本高昂，准确率较低等不足，提出了一种利用存储缩减方案的WSN数据恢复算法。首先，利用恢复过程中存在的中间数据，使用二次型优化方式进行传输优化的同时，进一步引入线性规划理论对传输过程进行降噪操作，降低剩余数据恢复量的前提下提升数据恢复准确率，改善压缩感知收敛速度。随后，采取自感知方法设计骨干汇聚链路并确定传输节点，从骨干链路树中迅速搜寻出具有优良传输性能的最佳路径，降低网络抖动现象。仿真实验表明：与当前常用的基于信任模型的无线传感器网络压缩感知数据恢复算法（Trust Based Data Prediction, Aggregation And Reconstruction Using Compressed Sensing for Clustered Wireless Sensor Networks，TDBP-AR算法）和基于单纯压缩感知机制的数据恢复算法（In-Network Data Processing Based on Compressed Sensing in WSN: A Survey，DP-CS算法）相比，本文算法数据恢复性能较强，恢复准确率较高，数据恢复时间较短，具有很强的实际部署价值。

英文摘要:

In order to solve the problems of high recovery cost and low accuracy in the process of receiving data in WSN network, a data recovery algorithm based on storage reduction scheme is proposed. Firstly, the algorithm uses the intermediate data in the recovery process to optimize the transmission by quadratic optimization. At the same time, it further introduces the linear programming theory to denoise the transmission process to improve the accuracy of data recovery and the convergence speed of compressed sensing while reducing the amount of residual data recovery. Then, the self sensing method is adopted to design the backbone convergence link and determine the transmission node. The best path with excellent transmission performance is quickly searched from the backbone link tree to reduce the network jitter. The simulation results show that: compared with the commonly used trust based data prediction, aggregation and reconstruction using compressed sensing for clustered wireless sensor networks (tdbp-ar algorithm) and in network data processing based on the trust model (in network data processing based on), the simulation results show that the proposed algorithm is more effective than the other two algorithms Compared with dp-cs algorithm, this algorithm has better data recovery performance, higher recovery accuracy, shorter data recovery time, and has strong practical deployment value.

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