A Practical Approach for Missing Wireless Sensor Networks Data Recovery

作     者：Song Xiaoxiang Guo Yan Li Ning Ren Bing Song Xiaoxiang;Guo Yan;Li Ning;Ren Bing

作者机构：The College of Communications EngineeringArmy Engineering University of PLANanjing 210007China The 63891 Unit of PLALuoyang 471000China 

出 版 物：《China Communications》 (中国通信（英文版）)

年 卷 期：2024年第21卷第5期

页      面：202-217页

核心收录：

学科分类：0810[工学-信息与通信工程] 08[工学] 080202[工学-机械电子工程] 0804[工学-仪器科学与技术] 080402[工学-测试计量技术及仪器] 0802[工学-机械工程] 

基　　金：supported by the National Natural Science Foundation of China(No.61871400) the Natural Science Foundation of the Jiangsu Province of China(No.BK20171401)。 

主　　题：average cross correlation matching pursuit missing data wireless sensor networks 

摘      要：In wireless sensor networks(WSNs),the performance of related applications is highly dependent on the quality of data collected.Unfortunately,missing data is almost inevitable in the process of data acquisition and transmission.Existing methods often rely on prior information such as low-rank characteristics or spatiotemporal correlation when recovering missing WSNs data.However,in realistic application scenarios,it is very difficult to obtain these prior information from incomplete data sets.Therefore,we aim to recover the missing WSNs data effectively while getting rid of the perplexity of prior information.By designing the corresponding measurement matrix that can capture the position of missing data and sparse representation matrix,a compressive sensing(CS)based missing data recovery model is established.Then,we design a comparison standard to select the best sparse representation basis and introduce average cross-correlation to examine the rationality of the established model.Furthermore,an improved fast matching pursuit algorithm is proposed to solve the model.Simulation results show that the proposed method can effectively recover the missing WSNs data.