{"title":"物联网网络中的无线链路质量预测","authors":"Miguel Landry Foko Sindjoung, P. Minet","doi":"10.23919/PEMWN47208.2019.8986920","DOIUrl":null,"url":null,"abstract":"The knowledge of link quality in IoT networks allows a more accurate selection of wireless links to build the routes used for data gathering. The number of retransmissions is decreased, leading to a shorter end-to-end latency, a better end-to-end reliability and a larger network lifetime. We propose to predict link quality by means of machine learning techniques applied on two metrics: RSSI and PDR. The accuracy got by Logistic Regression, Linear Support Vector Machine, Support Vector Machine and Random Forest classifier is computed on the traces of a real IoT network deployed in Grenoble.","PeriodicalId":440043,"journal":{"name":"2019 8th International Conference on Performance Evaluation and Modeling in Wired and Wireless Networks (PEMWN)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Wireless Link Quality Prediction in IoT Networks\",\"authors\":\"Miguel Landry Foko Sindjoung, P. Minet\",\"doi\":\"10.23919/PEMWN47208.2019.8986920\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The knowledge of link quality in IoT networks allows a more accurate selection of wireless links to build the routes used for data gathering. The number of retransmissions is decreased, leading to a shorter end-to-end latency, a better end-to-end reliability and a larger network lifetime. We propose to predict link quality by means of machine learning techniques applied on two metrics: RSSI and PDR. The accuracy got by Logistic Regression, Linear Support Vector Machine, Support Vector Machine and Random Forest classifier is computed on the traces of a real IoT network deployed in Grenoble.\",\"PeriodicalId\":440043,\"journal\":{\"name\":\"2019 8th International Conference on Performance Evaluation and Modeling in Wired and Wireless Networks (PEMWN)\",\"volume\":\"67 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 8th International Conference on Performance Evaluation and Modeling in Wired and Wireless Networks (PEMWN)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/PEMWN47208.2019.8986920\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 8th International Conference on Performance Evaluation and Modeling in Wired and Wireless Networks (PEMWN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/PEMWN47208.2019.8986920","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The knowledge of link quality in IoT networks allows a more accurate selection of wireless links to build the routes used for data gathering. The number of retransmissions is decreased, leading to a shorter end-to-end latency, a better end-to-end reliability and a larger network lifetime. We propose to predict link quality by means of machine learning techniques applied on two metrics: RSSI and PDR. The accuracy got by Logistic Regression, Linear Support Vector Machine, Support Vector Machine and Random Forest classifier is computed on the traces of a real IoT network deployed in Grenoble.