Effective Fault Clustering Management Approach Based Self-recovery Mechanism for Decentralized Wireless Sensor Networks | ||||
| Damanhour Journal of Intelligent Systems and Informatics | ||||
| Volume 1, Issue 1 - Serial Number 20240100, January 2025 PDF (634.92 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/djis.2025.349258.1002 | ||||
 View on SCiNiTO | ||||
| Author | ||||
Walaa Elsayed 
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| Department of information technology, Faculty of computer and information systems, Damanhour University, Egypt | ||||
| Abstract | ||||
| Wireless sensor networks (WSNs) have several uses and provide endless future possibilities. Nodes in wireless sensor networks are prone to failure owing to energy depletion, communication link problems, malicious attacks, and so on. As a result, self-recovery mechanisms are one of the most important challenges in WSNs. Fault detection is the primary strategy in the self- recovery mechanism in wireless sensor networks (WSNs), with each cluster head frequently checking the readings of its members. According to previous research, most comparing approaches will fail if more than half of a sensor's nearby nodes are incorrect. Furthermore, these comparing approaches cannot discover common mode failures. The suggested fault self-recovery method works by comparing the pulse sequence number generated by surrounding nodes and disseminating the choice made regarding each node. This paper presents an approach which can both locate and recover malfunctioning nodes in sensor networks. The proposed model is integrating capabilities of isolating the defective cluster sensors, which cause WSN malfunctions, from the cluster cycling and advertising the new path coordinates for the base station (BS). The simulation findings reveal that the suggested Effective Fault Clustering Management (EFCM) approach is very precise in discovering malfunctioning nodes and very fast in finding a cover free of such nodes when using the NS3 simulator | ||||
| Keywords | ||||
| WSNs; Clustering; Node failure; Self-detect; Self-recovery | ||||
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