TOWARDS SELF-HEALING IOT NETWORKS: LEVERAGING BLE FOR DISTRIBUTED FAULT DETECTION AND RECOVERY

Abstract

Self-healing capabilities in Internet of Things (IoT) networks represent a critical advancement for ensuring robust and reliable operations in increasingly complex distributed systems. This article presents a novel approach to autonomous fault detection and recovery in IoT networks by leveraging Bluetooth Low Energy (BLE) technology. This article introduces a distributed architecture that enables peer-to-peer health monitoring and collaborative recovery mechanisms among resource-constrained devices. The proposed system implements lightweight machine learning algorithms for anomaly detection, allowing devices to collectively identify and respond to network failures while optimizing energy consumption. Through a proof-of-concept implementation, this article demonstrates the feasibility of embedding self-healing capabilities within standard BLE protocol constraints. The experimental results show significant improvements in network resilience and recovery time compared to traditional centralized approaches while maintaining minimal overhead on device resources. This article contributes to the evolving field of autonomous IoT systems by establishing a framework for self-healing networks that can be readily adopted in critical applications such as industrial automation, smart cities, and healthcare monitoring.