AI-Integrated Self-Healing System for Robust Fault Detection and Automatic Recovery in IoT Environments

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.