Efficient and secure routing with UAV: GuidedPheromone update based on improved Ant colony optimization and fuzzy logic for congestion control in vehicular ad-hoc network

Vehicular Ad-hoc network represents a group of mobile ad-hoc networks with the potential to enhance road safety. Every vehicle with Onboard Unit (OBU) while moving quickly, exchanges information by using Road Side Unit (RSU) with the next closest vehicle. Due to high vehicle density and high speed at which each vehicle moves and exchanges information with the next closest vehicle, the system periodically experiences packet loss and congestion. In order to provide effective management of road traffic and to reduce the frequency of road accidents, vehicle communications have gained popularity through a period. This resulted in developing intelligent transport system. Traditional swarm intelligence method such as Ant Colony Optimization algorithm (ACO) is effective for combinatorial optimization issues. Swarm Intelligence based Ant colony optimization is currently applied dynamic environment path planning to determine the most optimal, efficient, and secure routes. Typically, Ant colony optimization technique has problems and fall victim to the local optimum trap. In order to accomplish paths optimization in a dynamic environment, this research proposes an improved Ant colony optimization method that combines fuzzy logic (IACOFL) with a pheromone updating model that raises pheromone levels on edges by using unmanned aerial vehicle (UAV).This work proposes IACOFL enables to dynamically identify utmost reliable and safe route for vehicles. This results in improved performance by enhancing packet delivery ratio, end-to-end delay, and throughput in Vehicular Ad-hoc networks. MATLAB simulation is used for simulation-based testing. The packet delivery ratio and throughput of IACOFL showed a significant increase to 48.24% and 65.47% when compared to Ant colony optimization. End-to-End delay of IACOFL decreased to 50.41% when compared with Ant colony optimization. In this IACOFL, it has been found that there is gradual decrease in traffic congestion time.