Fault diagnosis algorithm for WSN based on clustering and credibility

Lidan Wang, Xin Xu, Xiaofei Zhang, Cheng Kuan Lin*, Yu-Chee Tseng

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Fault diagnosis is one of the challenging problems in wireless sensor network (WSN). This paper proposes a fault diagnosis algorithm based on clustering and credibility (FDCC). Firstly, the network is divided into several clusters according to both geographic positions and measurements of sensor nodes for the purpose of improving the accuracy of network diagnostic result. The process of clustering can be divided into five phases: region division, head selection, coarse clustering, coarse cluster merge and cluster adjustment. Then, in order to further improve the accuracy of diagnostic result, a credibility model based on historical diagnostic result and remaining energy is established for each neighbor node. At last, nodes with higher credibility are selected to participate in diagnostic process. Simulation results show that the proposed algorithm can guarantee higher diagnostic accuracy.

Original languageEnglish
Title of host publicationAlgorithms and Architectures for Parallel Processing - 18th International Conference, ICA3PP 2018, Proceedings
EditorsJaideep Vaidya, Jin Li
PublisherSpringer Verlag
Pages145-159
Number of pages15
ISBN (Print)9783030050535
DOIs
StatePublished - 1 Jan 2018
Event18th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2018 - Guangzhou, China
Duration: 15 Nov 201817 Nov 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11335 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2018
CountryChina
CityGuangzhou
Period15/11/1817/11/18

Keywords

  • Clustering
  • Credibility model
  • Fault diagnosis
  • Sensor network

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