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Fault Tolerance Techniques for Multi-Hop Clustering in Wireless Sensor Networks

Adel Rajab*

College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia

* Corresponding Author: Adel Rajab. Email: email

Intelligent Automation & Soft Computing 2022, 32(3), 1743-1761. https://doi.org/10.32604/iasc.2022.021922

Abstract

Wireless sensor networks (WSN) deploy many nodes over an extended area for traffic surveillance, environmental monitoring, healthcare, tracking wildlife, and military sensing. Nodes of the WSN have a limited amount of energy. Each sensor node collects information from the surrounding area and forwards it onto the cluster head, which then sends it on to the base station (BS). WSNs extend the lifetime of the network through clustering techniques. Choosing nodes with the greatest residual energy as cluster heads is based on the idea that energy consumption is periodically distributed between nodes. The sink node gathers information from its environment that is then transmitted to the base station. The clustering protocol uses a considerably amount of energy for data collection and transmission, with additional energy used for listening to the nodes. It also contributes to channel sensing and avoiding collisions alongside energy transmission. Most clustering techniques do not consider cluster fails, because of which detection through cluster heads or the BS is not possible. Terminated nodes and sub-cluster heads thus continue to transmit information to the failed sub-cluster head, which leads to higher energy consumption. In light of this, we propose a technique to choose cluster heads while reducing the use of CSMA/CA through fault tolerance to determine the failure of the cluster heads by consuming little energy. This work here contributes to increasing the life of the WSN and conserving its energy by more than a half-sensor node per round.

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Cite This Article

A. Rajab, "Fault tolerance techniques for multi-hop clustering in wireless sensor networks," Intelligent Automation & Soft Computing, vol. 32, no.3, pp. 1743–1761, 2022. https://doi.org/10.32604/iasc.2022.021922



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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