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dc.contributor.authorZhang, Y.en
dc.contributor.authorSzabo, C.en
dc.contributor.authorSheng, Q.en
dc.identifier.citationIntelligent Data Analysis, 2016; 20(5):979-995en
dc.description.abstractEnvironmental sensing using multitudes of wirelessly connected sensors is becoming critical for resolving environmental problems, given recent technology advances in the Internet of Things (IoT). Current environmental sensing projects typically deploy commodity sensors, which are known to be unreliable and prone to produce noisy and erroneous data. Moreover, the majority of current sensor data cleaning techniques have not moved beyond using the mean or the median of spatially correlated readings, thus providing unsatisfying accuracies. In this paper, we propose a sensor reliability-based cleaning method, called Influence Mean (IM), which uses weighted aggregation based on individual sensor reliabilities. We investigate whether reducing or removing unreliable sensors can be more effective to provide accurate cleaning results, by designing and testing respective algorithms on synthetic and real datasets. The experimental results show that our method generally improves the data cleaning accuracy, particularly when the behaviors of unreliable sensors vary drastically from reliable sensors.en
dc.description.statementofresponsibilityYihong Zhang, Claudia Szabo and Quan Z. Shengen
dc.publisherIOS Pressen
dc.rights© 2016 – IOS Press and the authors. All rights reserveden
dc.subjectData cleaning; internet of things; environmental sensingen
dc.titleReduce or remove: individual sensor reliability profiling and data cleaningen
dc.typeJournal articleen
dc.identifier.orcidSzabo, C. [0000-0003-2501-1155]en
Appears in Collections:Computer Science publications

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