{"title":"跟踪智能家居中的物体","authors":"Vinicius Prado da Fonseca, P. Rosa","doi":"10.1109/BRICS-CCI-CBIC.2013.101","DOIUrl":null,"url":null,"abstract":"A RSSI-based localization system on a home wireless sensor network is proposed in this work. In order to support a robot assistant in pick-and-place tasks, our current system is capable of estimating the localization of an object using the signal strength received by a mobile device in a ZigBee sensor network. Two models were utilized (a) log-distance path loss - model in which signal lost has a random influence with log-normal distribution, and (b) free space decay law - based on the decay law for a signal on an open space. RSSI measurements were done in laboratory for applying the estimation method. Moreover experiments with satisfactory results were done with a public dataset to benchmark our results.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Tracking Objects in a Smart Home\",\"authors\":\"Vinicius Prado da Fonseca, P. Rosa\",\"doi\":\"10.1109/BRICS-CCI-CBIC.2013.101\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A RSSI-based localization system on a home wireless sensor network is proposed in this work. In order to support a robot assistant in pick-and-place tasks, our current system is capable of estimating the localization of an object using the signal strength received by a mobile device in a ZigBee sensor network. Two models were utilized (a) log-distance path loss - model in which signal lost has a random influence with log-normal distribution, and (b) free space decay law - based on the decay law for a signal on an open space. RSSI measurements were done in laboratory for applying the estimation method. Moreover experiments with satisfactory results were done with a public dataset to benchmark our results.\",\"PeriodicalId\":306195,\"journal\":{\"name\":\"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-09-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.101\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.101","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A RSSI-based localization system on a home wireless sensor network is proposed in this work. In order to support a robot assistant in pick-and-place tasks, our current system is capable of estimating the localization of an object using the signal strength received by a mobile device in a ZigBee sensor network. Two models were utilized (a) log-distance path loss - model in which signal lost has a random influence with log-normal distribution, and (b) free space decay law - based on the decay law for a signal on an open space. RSSI measurements were done in laboratory for applying the estimation method. Moreover experiments with satisfactory results were done with a public dataset to benchmark our results.
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