{"title":"基于无线传感器网络的环境监测空间预测自适应采样研究进展","authors":"Linh V. Nguyen, Nalika Ulapane, J. V. Miró","doi":"10.1109/ICIEA.2018.8397740","DOIUrl":null,"url":null,"abstract":"The paper presents a review of the spatial prediction problem in the environmental monitoring applications by utilizing stationary and mobile robotic wireless sensor networks. First, the problem of selecting the best subset of stationary wireless sensors monitoring environmental phenomena in terms of sensing quality is surveyed. Then, predictive inference approaches and sampling algorithms for mobile sensing agents to optimally observe spatially physical processes in the existing works are analysed.","PeriodicalId":140420,"journal":{"name":"2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA)","volume":"398 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Adaptive sampling for spatial prediction in environmental monitoring using wireless sensor networks: A review\",\"authors\":\"Linh V. Nguyen, Nalika Ulapane, J. V. Miró\",\"doi\":\"10.1109/ICIEA.2018.8397740\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper presents a review of the spatial prediction problem in the environmental monitoring applications by utilizing stationary and mobile robotic wireless sensor networks. First, the problem of selecting the best subset of stationary wireless sensors monitoring environmental phenomena in terms of sensing quality is surveyed. Then, predictive inference approaches and sampling algorithms for mobile sensing agents to optimally observe spatially physical processes in the existing works are analysed.\",\"PeriodicalId\":140420,\"journal\":{\"name\":\"2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA)\",\"volume\":\"398 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-06-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIEA.2018.8397740\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIEA.2018.8397740","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Adaptive sampling for spatial prediction in environmental monitoring using wireless sensor networks: A review
The paper presents a review of the spatial prediction problem in the environmental monitoring applications by utilizing stationary and mobile robotic wireless sensor networks. First, the problem of selecting the best subset of stationary wireless sensors monitoring environmental phenomena in terms of sensing quality is surveyed. Then, predictive inference approaches and sampling algorithms for mobile sensing agents to optimally observe spatially physical processes in the existing works are analysed.
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