Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/2884
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSwain, Rakesh Ranjan-
dc.contributor.authorDash, Tirtharaj-
dc.contributor.authorKhilar, Pabitra Mohan-
dc.date.accessioned2018-01-12T06:43:09Z-
dc.date.available2018-01-12T06:43:09Z-
dc.date.issued2017-12-
dc.identifier.citationInternational Conference on Computational Intelligence: Theories, Applications and Future Directions(ICCI), IIT Kanpur, 6-8 December 2017en_US
dc.identifier.urihttp://hdl.handle.net/2080/2884-
dc.descriptionCopyright of this document belongs to proceedings publisheren_US
dc.description.abstractWireless sensor networks (WSN) are often inaccessible to human and are at least deployed in such environment such as deep forest, various hazardous industries, hilltop, and sometimes underwater. The occurrence of failures in sensor networks is inevitable due to continuous or instant change in environmental parameters. A failure may lead to faulty readings which in turn may cause economic and physical damages to the environment. In this work, a thorough investigation has been conducted on the application of adaptive neuro-fuzzy inference system (ANFIS) for automated fault diagnosis in WSN. Further, a kernelized version of ANFIS has also been studied for the discussed problem. To avoid the model’s undesired biases towards a specific type of failure, oversampling has been done for multiple version of the ANFIS model. This study would serve as a guideline for the community towards the application of fuzzy inference approaches for fault diagnosis in sensor networks. However, the work focuses on the automated fault diagnosis in open air WSN and has no applicability in underwater sensor network systems.en_US
dc.subjectFault diagnosisen_US
dc.subjectWireless Sensor Networksen_US
dc.subjectKernelizationen_US
dc.subjectANFISen_US
dc.titleInvestigation of RBF kernelized ANFIS for Fault Diagnosis in Wireless Sensor Networksen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

Files in This Item:
File Description SizeFormat 
2017_ICCI_RRSwain_Investigation.pdfPaper2.25 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.