Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/2606
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dc.contributor.authorSwain, Rakesh Ranjan-
dc.contributor.authorKhilar, Pabitra Mohan-
dc.date.accessioned2017-01-06T12:18:24Z-
dc.date.available2017-01-06T12:18:24Z-
dc.date.issued2016-11-
dc.identifier.citationIEEE TENCON 2016 — Technologies for Smart Nation, Marina Bay Sands, Singapore , 22-25 November 2016en_US
dc.identifier.urihttp://hdl.handle.net/2080/2606-
dc.descriptionCopyright belongs to the proceeding publisheren_US
dc.description.abstractThis paper presents a fault diagnosis protocol for wireless sensor networks (WSNs) based on neural network approach. A particle swarm optimization based fuzzy multilayer perceptron used in the fault detection and classification phase of the protocol. The proposed protocol handled the composite fault model such as hard permanent, soft permanent, intermittent, and transient fault. The performance of proposed algorithm evaluated by using the generic parameter such that detection accuracy, false alarm rate, and false positive rate. The simulation is carried out by the standard network simulator NS-2.35 and the performance is compared with the existing fault diagnosis protocols. The result shows that the proposed protocol performs superior than the existing protocols.en_US
dc.publisherIEEEen_US
dc.subjectWireless Sensor Networksen_US
dc.subjectFuzzy MLPen_US
dc.subjectFault Diagnosisen_US
dc.titleA Fuzzy MLP Approach for Fault Diagnosis in Wireless Sensor Networksen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

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