Dylan McDonald, Stewart Sanchez, S. Madria, F. Erçal
{"title":"A Communication Efficient Framework for Finding Outliers in Wireless Sensor Networks","authors":"Dylan McDonald, Stewart Sanchez, S. Madria, F. Erçal","doi":"10.1109/MDM.2010.95","DOIUrl":null,"url":null,"abstract":"Outlier detection is a well studied problem in various fields. The unique challenges of wireless sensor networks make this problem especially challenging. Sensors can detect outliers for a plethora of reasons and these reasons need to be inferred in real time. Here, we present a new communication technique to find outliers in a wireless sensor network. Communication is minimized through controlling sensor when sensors are allowed to communicate. At the same time, minimal assumptions are made about the nature of the data set as to minimize the loss of generality in the architecture.","PeriodicalId":373849,"journal":{"name":"2010 Eleventh International Conference on Mobile Data Management","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 Eleventh International Conference on Mobile Data Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MDM.2010.95","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Outlier detection is a well studied problem in various fields. The unique challenges of wireless sensor networks make this problem especially challenging. Sensors can detect outliers for a plethora of reasons and these reasons need to be inferred in real time. Here, we present a new communication technique to find outliers in a wireless sensor network. Communication is minimized through controlling sensor when sensors are allowed to communicate. At the same time, minimal assumptions are made about the nature of the data set as to minimize the loss of generality in the architecture.