{"title":"从低功耗物联网传感节点的接收功率统计估计运动统计","authors":"Waltenegus Dargie","doi":"10.1109/LSENS.2024.3486582","DOIUrl":null,"url":null,"abstract":"Low-power Internet of Things (IoT) sensing nodes can be embedded into various physical environments to monitor vital parameters. Some of these environments impose rough and extreme operation conditions, severely limiting the performance of these nodes. Modeling these environments is vital to make the nodes adaptive. In this letter, we propose a model to estimate the complex motion of nodes deployed on the surface of different water bodies. The model relies on received power statistics only. Experimental results confirm that the model is reliable, achieving an estimation accuracy of 93%.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"8 12","pages":"1-4"},"PeriodicalIF":2.2000,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Estimation of Motion Statistics From Statistics of Received Power in Low-Power IoT Sensing Nodes\",\"authors\":\"Waltenegus Dargie\",\"doi\":\"10.1109/LSENS.2024.3486582\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Low-power Internet of Things (IoT) sensing nodes can be embedded into various physical environments to monitor vital parameters. Some of these environments impose rough and extreme operation conditions, severely limiting the performance of these nodes. Modeling these environments is vital to make the nodes adaptive. In this letter, we propose a model to estimate the complex motion of nodes deployed on the surface of different water bodies. The model relies on received power statistics only. Experimental results confirm that the model is reliable, achieving an estimation accuracy of 93%.\",\"PeriodicalId\":13014,\"journal\":{\"name\":\"IEEE Sensors Letters\",\"volume\":\"8 12\",\"pages\":\"1-4\"},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2024-10-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Sensors Letters\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10735365/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Sensors Letters","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10735365/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Estimation of Motion Statistics From Statistics of Received Power in Low-Power IoT Sensing Nodes
Low-power Internet of Things (IoT) sensing nodes can be embedded into various physical environments to monitor vital parameters. Some of these environments impose rough and extreme operation conditions, severely limiting the performance of these nodes. Modeling these environments is vital to make the nodes adaptive. In this letter, we propose a model to estimate the complex motion of nodes deployed on the surface of different water bodies. The model relies on received power statistics only. Experimental results confirm that the model is reliable, achieving an estimation accuracy of 93%.
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