Reinforced resource management in vehicular fog computing using deep beacon power control protocol

Vehicular fog computing (VFC) plays a vital role in the mobile ad hoc network. In vehicular fog computing, a deep beacon power control (DBPC) protocol is utilised for the sending of the periodical message in the VANET. This algorithm increases the effectiveness in the coverage of the broadcast of safety and security-related information and satisfies the constraints on both the link state and delay. The induction of deep learning model in beacon power control approach aims to overcome the optimisation issue by improving the amount of a fading multiuser interference channel. VANET is one of the ad hoc network real-life applications for communication between near-by equipment such as roadside equipment and vehicles and between vehicles. The proposed technique leads to optimised data transmission in vehicular fog computing. Unnecessary network overhead and also channel congestion can be minimised using this proposed technique. The proposed deep BPC technique is implemented in both Keras and NS2 simulators. Outcomes of both simulations reveal that when deep learning embedded with BPC protocol, the performance increases rapidly.