Microgrid based VANET monitoring and energy management in 5G networks by reinforcement deep learning techniques

Several novel technologies have evolved as a result of the advancement of wireless communication, such as the vehicle-to-vehicle communication system known as VANETs (Vehicular Ad hoc Networks). Geographic routing is the routing protocol with the highest scalability and lowest overheads that is best suited for VANETs. This study proposes a novel energy management and monitoring system based on microgrids. The development of a microgrid for an energy management system is the goal here. The reinforcement stacked adversarial neural networks are then used to analyze the VANET monitoring data. Energy efficiency, network lifetime, training accuracy, QoS (quality of service), and communication overhead are the focus of the experimental analysis. Vehicle densities and mobility conditions, the developed system offers a low delay and fewer packet transmissions, according to simulation results. The proposed technique attained energy efficiency of 83%, network lifetime of 63%, training accuracy of 96%, QoS of 68%, communication overhead of 48%.