AI Assisted Energy Optimized Sustainable Model for Secured Routing in Mobile Wireless Sensor Network

With the rapid development of cognitive computing and the Internet of Things (IoT), sensing systems have produced a wide range of real-time communication applications. They use 5G/6G-enabled technologies to connect to the outside world to collect data and process different end-user requests. Wireless systems and artificial intelligence (AI) have led to significant development in the optimization process of network communication. Due to various constraints of wireless systems, many solutions have been presented to cope with routing and connectivity concerns. However, topology awareness and attaining management of quality of services are still demanding research challenges for sustainable development. This study proposes an AI-assisted routing model for mobile wireless sensor networks (MWSN) to optimize energy and detect communication link failures. Moreover, the proposed intelligent security approach increases the trustworthiness of the constraint devices on unpredictable routes. Firstly, it explores a genetic algorithm, a metaheuristic optimization technique to determine the feasible solutions, and based on independent metrics it generates an optimal set of routes. In the proposed model, the genetic algorithm provides a fault-tolerant solution for dynamic environments, specifically under unpredictable conditions. Second, new routes are established using dynamic decisions that satisfy the energy considerations. In the end, the proposed model performs regular auditing to detect malicious devices based on unexpected behavior. The proposed model is tested and it outperforms IMD-EACBR and AGRIC in terms of realistic performance metrics.