Neuro-Heal: An AI-Powered Autonomous Self-Healing Framework for Resilient IoT Networks

The rapid growth of the Internet of Things (IoT) has connected billions of devices across various fields, from smart cities to industrial automation. However, the large and diverse nature of IoT networks makes them very vulnerable to node failures, connectivity issues, and performance drops. Traditional fault management methods depend heavily on manual intervention. This approach leads to increased downtime and lower service reliability. This paper presents Neuro-Heal, an AI-powered self-healing framework designed to improve the resilience of IoT networks. Using predictive analytics and deep learning models, Neuro-Heal identifies potential faults before they happen, categorizes failure types in real time, and automatically reconfigures network routes to restore connectivity with minimal delays. The framework uses reinforcement learning to refine recovery strategies based on previous fault-handling experiences, thus increasing healing efficiency over time. Extensive simulations using NS-3 and OMNeT++ show that Neuro-Heal achieves recovery that is up to 35% faster, reduces packet loss by 28%, and enhances overall network availability compared to traditional fault management methods. This work paves the way for sustainable, self-sufficient IoT infrastructures that can maintain uninterrupted service in changing and failureprone situations.