NDLI: Genetic algorithm based fault tolerant clustering in wireless sensor network

Content Provider	IET Digital Library
Author	Rajeswari, Kasilingam Neduncheliyan, Subbu
Abstract	In wireless sensor network (WSN), during fault detection and recovery, average energy loss, and message loss occurs including the link failure. Also, the number of faulty nodes and the traffic overhead is increased with the size of WSN. In order to overcome this issue, in this study, a cluster-based fault tolerance technique using genetic algorithm is proposed. Here the network is clustered according to energy-efficient distance-based clustering algorithm. For each cluster head, a set of backup nodes are selected using genetic algorithm based on the sponsored coverage and residual energy parameters. This helps in detecting the faults occurring in cluster members and cluster heads. Simulation results show that the proposed technique minimises the energy loss and overhead.
Starting Page	1927
Ending Page	1932
Page Count	6
ISSN	17518628
Volume Number	11
e-ISSN	17518636
Issue Number	Issue 12, Aug (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-com/11/12
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-com.2016.1074
Journal	IET Communications
Publisher Date	2017-05-11
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Average Energy Loss Backup Nodes Cluster Head Cluster Members Cluster-based Fault Tolerance Technique Energy Conservation Energy-efficient Distance-based Clustering Algorithm Failure Analysis Fault Detection Fault Diagnosis Fault Recovery Fault Tolerance Genetic Algorithm Link Failure Message Loss Optimisation Technique Pattern Clustering Reliability Residual Energy Parameter Telecommunication Network Reliability Telecommunication Power Management Telecommunication Traffic Traffic Overhead Wireless Sensor Network WSN
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Computer Science Applications

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