Abstract:
In many applications of wireless sensor networks, data collection happens to be generally unreliable and inaccurate. In order to improve the quality and the accuracy of t...Show MoreMetadata
Abstract:
In many applications of wireless sensor networks, data collection happens to be generally unreliable and inaccurate. In order to improve the quality and the accuracy of this collected data, a deep analysis should be applied in order to spot and remove abnormalities that this data may contain, generally referred to as data outliers. This paper tackles this problem and presents a novel Distributed Outlier Detection Approach, dubbed DODA, that works in two steps: (1) first, it models the problem of detecting outliers with a Naïve Bayesian network within each cluster of sensors, and (2) then detects these outliers using a Mahalanobis distance at the cluster heads. Through extensive experiments, conducted on real datasets from Intel-Berkeley Laboratory, we demonstrated that the proposal performs better than the recent state-of-the-art algorithms under several metrics such energy consumption, false alarm rate and detection accuracy.
Published in: 2019 International Conference on Smart Applications, Communications and Networking (SmartNets)
Date of Conference: 17-19 December 2019
Date Added to IEEE Xplore: 20 April 2020
ISBN Information: