Link Prediction with Efficient and Reliable Routing Using Bat Algorithm in UAV-Assisted Vehicular Network

An integration of UAVs into VANET has emerged as a best solution to address the challenges of routing, privacy, scalability, and quality of service. In this context, the proposed LPERBA approach presents a comprehensive framework for optimizing communication and resource allocation in rural VANET environments. Leveraging UAVs equipped with Mobile Edge Computing (MEC) servers, LPERBA enhances coverage, capacity, and energy efficiency. Through innovative methodologies such as link prediction processes and Bat Algorithm-based routing, LPERBA effectively predicts and mitigates link failures, optimizing network stability and reliability. Extensive experimentation using NS-2 simulations demonstrates the performance of LPERBA compared to traditional methods like EALAR, RACQM, and FSOMU, across key metrics including routing overhead, energy efficiency, network throughput, and average delay. These findings underscore the potential of LPERBA to revolutionize communication systems in rural VANETs, offering enhanced efficiency and reliability for future ITS deployments.