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Bayesian maximum entropy and interacting multiple model based automatic sensor drift detection and correction in an IoT environment
conference contribution
posted on 2018-01-01, 00:00 authored by Punit Rathore, D Kumar, Sutharshan RajasegararSutharshan Rajasegarar, M PalaniswamiWith the advancement in the Internet of Things (IoT) technologies, a variety of sensors including inexpensive, low-precision sensors with sufficient computing and communication capabilities are increasingly deployed for monitoring large geographical areas. One of the problems with the use of inexpensive sensors is the drift that they develop over time. These drifting sensors need to be calibrated automatically for continuous and reliable monitoring. In this paper, we present a new methodology to automatically detect and correct both the smooth and steep drifts by employing Bayesian Maximum Entropy and Interacting Multiple Model based techniques. The evaluation on real IoT data gathered from an indoor and an outdoor deployment reveals the superiority and applicability of our method in correctly identifying and correcting the smooth and abrupt (sensor) drifts in the IoT environment.
History
Event
IEEE Internet of Things. Forum (4th : 2018 : Singapore)Series
IEEE Internet of Things ForumPagination
598 - 603Publisher
Institute of Electrical and Electronics EngineersLocation
SingaporePlace of publication
Piscataway, N.J.Publisher DOI
Start date
2018-02-05End date
2018-02-08ISBN-13
9781467399449Language
engPublication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2018, IEEEEditor/Contributor(s)
[Unknown]Title of proceedings
IEEE WF-IoT 2018 : Proceedings of 4th IEEE World Forum on Internet of ThingsUsage metrics
Categories
Keywords
Bayes methodscomputerised instrumentationInternet of Thingsmaximum entropy methodssensorsScience & TechnologyTechnologyComputer Science, Theory & MethodsEngineering, Electrical & ElectronicComputer ScienceEngineeringInformation SystemsArtificial Intelligence and Image ProcessingDistributed Computing
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