An Intelligent Fault Detection Approach Based on RL Systems in WSN

This paper shows how we made and tested the faults in a given message using RL system. The Internet of Things (IOT) has established a solid infrastructure through the commercialization of innovative technologies. IOT networks facilitate smart devices in gathering environmental data and transmitting it to users via an IOT gateway. However, the rapid growth in the number of IOT devices and sensors leads to network congestion, which significantly drains the energy of these devices. The wireless network serves as a crucial layer for IOT, characterized by its dynamic nature. To address the challenges posed by environmental unpredictability and the random distribution of network weights, developing energy- efficient routing protocols is essential. Learning-based routing systems are emerging as viable solutions due to their adaptability and precision. Nonetheless, routing in dynamic IOT networks presents difficulties due to the fluctuating nature of link connections and access statuses. Thus, contemporary learning- based routing systems must be capable of real-time adaptation to changes within the network. This research introduces an intelligent routing technique that leverages reinforcement learning for fault detection, energy efficiency, and quality of service, aiming to identify optimal routes with minimal end-to- end latency. Notably, the selection of cluster heads is influenced by the residual energy of cluster nodes, which can affect the overall lifespan of the network. This approach not only prolongs network longevity but also mitigates energy consumption during data transmission, enhancing network resilience.