Authors : Manju; Dr. V. K. Srivastava

Volume/Issue : Volume 10 - 2025, Issue 12 - December

Google Scholar : https://tinyurl.com/2s3mrv6t

Scribd : https://tinyurl.com/yyfduj6r

DOI : https://doi.org/10.38124/ijisrt/25dec1326

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The fast growth of the Internet of Things (IoT) has resulted in the creation of more complicated and interdependent systems, which are prone to frequent failures, slowdown of performance, and disruption of its functioning. The conventional methods of fault management tend to be largely manual, resource-consuming, and reactive, which do not apply to large-scale and dynamic IoT systems. In this study, the researcher presents an AI-based self-healing system that aims to provide autonomous fault detection, diagnosis, and recovery on heterogeneous IoT networks. The model includes lightweight edge-AI models used to detect anomalies in real-time, a decentralized decision engine used to classify faults, and a self-managed recovery system that can support dynamic rerouting, node isolation, and service restoration in a short period of time. Experimental tests show significant gains in fault detection, recovery time, energy efficiency, and resilience of the overall network as opposed to current methods. The suggested system maximizes service availability, minimizes downtime, and allows scalable deployment to smart homes, industrial internet of things, healthcare, and smart city use. The study also adds a powerful and adaptive self-healing design with the view of enhancing resilient, intelligent, and autonomous next- generation of IoT ecosystems.

Keywords : AI-based Fault Detection, Self-Healing IoT Networks, Automatic Recovery, Edge Intelligence, IoT Resilience, Anomaly Detection, Autonomous Systems, Network Reliability, Fault Tolerance, Smart Environments.

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