Optimized Reinforcement Learning for Resource Allocation in Vehicular Ad Hoc Networks

The vehicular ad hoc networks VANET is an essential part of intelligent transportation systems (ITSs) since it may offer various multimedia services and safety services to pedestrians, passengers, and even drivers. A wireless communication protocol called dedicated short-range communication (DSRC) was created for toll collection systems. Nevertheless, DSRC standards are extremely constrained, necessitating the development of next-generation communication protocols appropriate for VANET. Here, intended to develop an Optimized Reinforcement Learning (ORL) for obtaining resource allocation in VANET. This proposed methodology is developed for achieving resource allocation with efficient data transmission. This proposed approach is utilized to adjust the control channel interval (CCI) and service channel interval (SCI) to empower network performance. Additionally, it is utilized to reduce data collisions and optimize the network’s backoff distribution. The proposed method is a combination of reinforcement learning (RL) and adaptive coati optimization (ACO). The coati optimization mimics the characteristics of coati in natures in which it depends upon the coati escape from predators and hunting and attacking behaviour at various climates.The RL is utilized to obtain an efficient channel access algorithm. In the RL, the Q value is optimally selected by using ACO. Based on this algorithm, the proposed method is utilized to enhance the performance of VANET data transmission by achieving optimal resource allocation. The proposed method is implemented in MATLAB, and performances are evaluated using performance measures. Additionally, to validate the performance of the proposed methodology, it is compared with conventional techniques.