Intelligent Fault Detection and Self-Healing Mechanisms in Wireless Sensor Networks Using Machine Learning and Flying Fox Optimization

Almamoon Alauthman, Abeer Al-Hyari

WSNs play a critical role in many applications that require network reliability, such as environmental monitoring, healthcare, and industrial automation. Thus, fault detection and self-healing are two effective mechanisms for addressing the challenges of node failure, communication disruption, a energy constraints faced by WSNs. This paper presents an intelligent framework based on Light Gradient Boosting Machine integration for fault detection and a Flying Fox Optimization Algorithm in dynamic self-healing. The LGBM model provides very accurate and scalable performance related to effective fault identification, whereas FFOA optimizes the recovery strategies to minimize downtown and maximize network resilience. Extensive performance evaluation of the developed system using a large dataset was presented and compared with the state-of-the-art heuristic-based traditional methods and machine learning models. The results showed that the proposed framework could achieve 94.6% fault detection accuracy, with a minimum of 120 milliseconds of recovery time and network resilience of 98.5%. These results hence attest to the efficiency of the proposed approach in ensuring robust and adaptive WSN operations toward the quest for enhanced reliability within dynamic and resource-constrained environments.