An AI-Driven Self-Healing Framework for Fault Management in Internet of Things Networks

Author(s)	Manju, Dr. V.K. Srivastava*
Country	India
Abstract	dynamic networks that are increasingly prone to faults, failures, and performance degradation. Conventional fault management techniques, which rely heavily on manual intervention and predefined rules, are inadequate for meeting the real-time and scalability requirements of modern IoT environments. This paper presents an AI-driven self-healing IoT network framework that integrates intelligent fault detection, diagnosis, and autonomous recovery mechanisms. Machine learning and deep learning models are employed to analyze network behavior, identify anomalies, predict potential failures, and initiate recovery actions with minimal human involvement. The proposed methodology enhances network resilience, reduces downtime, and improves overall system reliability. Experimental evaluation demonstrates that the AI-driven self-healing approach significantly outperforms traditional fault management techniques in terms of accuracy, response time, and operational efficiency. The results highlight the potential of artificial intelligence as a transformative technology for building robust, adaptive, and future-ready IoT infrastructures.
Keywords	Internet of Things, Self-healing networks, Artificial Intelligence, Fault detection, Fault recovery, Machine learning
Field	Computer Applications
Published In	Volume 8, Issue 1, January-February 2026
Published On	2026-02-12

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International Journal For Multidisciplinary Research