Hyperspherical cluster based distributed anomaly detection in wireless sensor networks Rajasegarar, Sutharshan, Leckie, Christopher and Palaniswami, Marimuthu 2014, Hyperspherical cluster based distributed anomaly detection in wireless sensor networks, Journal of parallel and distributed computing, vol. 74, no. 1, pp. 1833-1847, doi: 10.1016/j.jpdc.2013.09.005. Attached Files Name Description MIMEType Size Downloads Title Hyperspherical cluster based distributed anomaly detection in wireless sensor networks Author(s) Rajasegarar, Sutharshan orcid.org/0000-0002-6559-6736 Leckie, Christopher Palaniswami, Marimuthu Journal name Journal of parallel and distributed computing Volume number 74 Issue number 1 Start page 1833 End page 1847 Total pages 15 Publisher Elsevier Place of publication Amsterdam, The Netherlands Publication date 2014-01-01 ISSN 0743-7315 Keyword(s) Distributed processing Wireless sensor networks Anomaly detection Summary This article describes a distributed hyperspherical cluster based algorithm for identifying anomalies in measurements from a wireless sensor network, and an implementation on a real wireless sensor network testbed. The communication overhead incurred in the network is minimised by clustering sensor measurements and merging clusters before sending a compact description of the clusters to other nodes. An evaluation on several real and synthetic datasets demonstrates that the distributed hyperspherical cluster-based scheme achieves comparable detection accuracy with a significant reduction in communication overhead compared to a centralised scheme, where all the sensor node measurements are communicated to a central node for processing. . Language eng DOI 10.1016/j.jpdc.2013.09.005 Field of Research 080109 Pattern Recognition and Data Mining Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category C1.1 Refereed article in a scholarly journal ERA Research output type C Journal article Copyright notice ©2014, Elsevier Persistent URL http://hdl.handle.net/10536/DRO/DU:30089205 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Wed, 23 Nov 2016, 11:26:51 EST Thu, 24 Nov 2016, 04:00:30 EST Thu, 24 Nov 2016, 05:06:53 EST Mon, 28 Nov 2016, 15:43:53 EST Mon, 28 Nov 2016, 15:44:43 EST Tue, 06 Feb 2018, 09:25:30 EST Tue, 06 Feb 2018, 09:26:29 EST Filtered Full Citation counts:   Cited 47 times in TR Web of Science Cited 57 times in Scopus Search Google Scholar Access Statistics: 376 Abstract Views, 4 File Downloads  -  Detailed Statistics Created: Wed, 23 Nov 2016, 11:26:51 EST

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