{"title":"智能环境下的智能设备:真实环境下的无设备被动检测","authors":"May Moussa, M. Youssef","doi":"10.1109/PERCOM.2009.4912826","DOIUrl":null,"url":null,"abstract":"Device-free Passive (DfP) localization is a system envisioned to detect, track, and identify entities that do not carry any device, nor participate actively in the localization process. A DfP system allows using nominal WiFi equipment for intrusion detection, without using any extra hardware, adding smartness to any WiFi-enabled device. In this paper, we focus on the detection function of the DfP system in a real environment. We show that the performance of our previously developed algorithms for detection in a controlled environments, which achieved 100% recall and precision, degrades significantly when tested in a real environment. We present an alternative algorithm, based on the maximum likelihood estimator (MLE), that has a significant performance increase in a real environment. Our results show that the recall of the system increases by more than 10% when using the proposed MLE without affecting the system's precision.","PeriodicalId":322416,"journal":{"name":"2009 IEEE International Conference on Pervasive Computing and Communications","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"193","resultStr":"{\"title\":\"Smart cevices for smart environments: Device-free passive detection in real environments\",\"authors\":\"May Moussa, M. Youssef\",\"doi\":\"10.1109/PERCOM.2009.4912826\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Device-free Passive (DfP) localization is a system envisioned to detect, track, and identify entities that do not carry any device, nor participate actively in the localization process. A DfP system allows using nominal WiFi equipment for intrusion detection, without using any extra hardware, adding smartness to any WiFi-enabled device. In this paper, we focus on the detection function of the DfP system in a real environment. We show that the performance of our previously developed algorithms for detection in a controlled environments, which achieved 100% recall and precision, degrades significantly when tested in a real environment. We present an alternative algorithm, based on the maximum likelihood estimator (MLE), that has a significant performance increase in a real environment. Our results show that the recall of the system increases by more than 10% when using the proposed MLE without affecting the system's precision.\",\"PeriodicalId\":322416,\"journal\":{\"name\":\"2009 IEEE International Conference on Pervasive Computing and Communications\",\"volume\":\"26 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-03-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"193\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 IEEE International Conference on Pervasive Computing and Communications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PERCOM.2009.4912826\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 IEEE International Conference on Pervasive Computing and Communications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PERCOM.2009.4912826","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Smart cevices for smart environments: Device-free passive detection in real environments
Device-free Passive (DfP) localization is a system envisioned to detect, track, and identify entities that do not carry any device, nor participate actively in the localization process. A DfP system allows using nominal WiFi equipment for intrusion detection, without using any extra hardware, adding smartness to any WiFi-enabled device. In this paper, we focus on the detection function of the DfP system in a real environment. We show that the performance of our previously developed algorithms for detection in a controlled environments, which achieved 100% recall and precision, degrades significantly when tested in a real environment. We present an alternative algorithm, based on the maximum likelihood estimator (MLE), that has a significant performance increase in a real environment. Our results show that the recall of the system increases by more than 10% when using the proposed MLE without affecting the system's precision.