A Robust Design of Fault Nodes Identification and Recovery Model Over Wireless Sensor Network

From environmental monitoring to industrial automation, wireless sensor networks (WSNs) are essential in many different applications. However, the reliability and robustness of WSNs are often compromised by faults such as node failures, communication disruptions, and data packet losses. This paper presents a comprehensive approach to fault management in WSNs, encompassing fault detection, localization, and recovery strategies. In the fault detection phase, machine learning techniques, including the utilization of the MobileNet model, are employed to classify normal and faulty behavior based on sensor data. Statistical analysis methods, such as hypothesis testing, complement machine learning approaches to detect anomalies in sensor readings. A hybrid approach combining machine learning with rule-based systems enhances fault detection accuracy. Subsequently, fault localization techniques leverage network topology analysis and localization algorithms to identify the location of faulty nodes within the network. By analyzing the connectivity between nodes and potential communication paths, vulnerable areas are pinpointed, facilitating efficient fault localization efforts. In the fault recovery phase, dynamic reconfiguration algorithms dynamically adjust network routing and data aggregation strategies in response to detected faults. Redundancy mechanisms, dynamic reconfiguration, and load balancing techniques are deployed to mitigate the impact of node failures and ensure uninterrupted data flow.