{"title":"A practical approach for missing wireless sensor networks data recovery","authors":"Xiaoxiang Song, Guo Yan, Li Ning, Ren Bing","doi":"10.23919/JCC.ea.2021-0283.202401","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":9814,"journal":{"name":"China Communications","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"China Communications","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.23919/JCC.ea.2021-0283.202401","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"TELECOMMUNICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
China Communications (ISSN 1673-5447) is an English-language monthly journal cosponsored by the China Institute of Communications (CIC) and IEEE Communications Society (IEEE ComSoc). It is aimed at readers in industry, universities, research and development organizations, and government agencies in the field of Information and Communications Technologies (ICTs) worldwide.
The journal's main objective is to promote academic exchange in the ICTs sector and publish high-quality papers to contribute to the global ICTs industry. It provides instant access to the latest articles and papers, presenting leading-edge research achievements, tutorial overviews, and descriptions of significant practical applications of technology.
China Communications has been indexed in SCIE (Science Citation Index-Expanded) since January 2007. Additionally, all articles have been available in the IEEE Xplore digital library since January 2013.