Samia Al Fallah, M. Arioua, A. Oualkadi, Jihane El Asri
{"title":"On the performance of piecewise linear approximation techniques in WSNs","authors":"Samia Al Fallah, M. Arioua, A. Oualkadi, Jihane El Asri","doi":"10.1109/COMMNET.2018.8360262","DOIUrl":null,"url":null,"abstract":"Energy consumption is the major constraint in the design and the deployment of Wireless Sensor Networks (WSNs). Since the transmission of data induces high energy costs in WSN device, many research efforts focus on reducing the transmission of the raw data by using lossy compression methods in order to improve energy efficiency with an acceptable data reconstruction tolerance. Thus, an intricate trade-off exists between energy saving using sampling compression, and the distortion of reconstructed data samples. In this paper, we present a survey on Piecewise Linear Approximation methods. A comparative analysis aims to evaluate the performance of the selected techniques in terms of energy consumption, compression ratio and distortion.","PeriodicalId":103830,"journal":{"name":"2018 International Conference on Advanced Communication Technologies and Networking (CommNet)","volume":"44-46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 International Conference on Advanced Communication Technologies and Networking (CommNet)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/COMMNET.2018.8360262","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
Energy consumption is the major constraint in the design and the deployment of Wireless Sensor Networks (WSNs). Since the transmission of data induces high energy costs in WSN device, many research efforts focus on reducing the transmission of the raw data by using lossy compression methods in order to improve energy efficiency with an acceptable data reconstruction tolerance. Thus, an intricate trade-off exists between energy saving using sampling compression, and the distortion of reconstructed data samples. In this paper, we present a survey on Piecewise Linear Approximation methods. A comparative analysis aims to evaluate the performance of the selected techniques in terms of energy consumption, compression ratio and distortion.