Self-Healing IoT Sensor Networks with Isolation Forest Algorithm for Autonomous Fault Detection and Recovery

Developing Internet of Things (IoT) sensor networks across various use cases calls for effective failure detection and recovery systems. This research uses the Isolation Forest algorithm to provide a new method for autonomous defect detection and recovery in IoT sensor networks. The Isolation Forest algorithm, originally designed for anomaly detection, has been modified to locate and fix network problems. The suggested system can minimize downtime and provide continuous data collection by automatically recognizing abnormalities in sensor data and quickly initiating recovery methods. Intelligent decision-making procedures that dynamically rearrange the network to lessen the effect of errors enable self-healing capabilities. The results of the experiments show that the suggested method effectively improves the robustness of IoT sensor networks, opening up a viable path for autonomous fault management in the real world. This architecture significantly impacts the development of self-repairing IoT systems by responding to the pressing need for automatic fault diagnosis and repair in highly dynamic, hardly-resourced settings.