Enhancing the Route Optimization Using Hybrid MAF Optimization Algorithm for the Internet of Vehicle

A smart city involves different types of sensors and electronic devices that collect data to develop sustainable growth in the urban area. The development of internet-based driving is referred to as the IoV in the new modern era of the IoT. In IoV, real-time data is collected and the vehicles communicate with each other to transmit the data. However, there are some issues, like finding the shortest route between a travelling source and a destination, high packet delivery ratios, congestion, low connectivity probability, and high delays. To overcome these parameter issues, this paper proposes a hybrid optimization approach that combines modified Ant Colony and Firefly optimization techniques (MAF) to calculate the average speed and find the best route to the destination. The MAF algorithm combined attractiveness and pheromones to find the optimal path and reduce the travelling time. The proposed MAF algorithm for shortest route selection is compared to recent state-of-the-art methods such as RAVP, ECRA, EECM, and AISM. Two simulators, NS2 and SUMO, were used to conduct this experiment. These simulation findings reveal that the performance of the proposed MAF optimization is increased in terms of increased connectivity probability, reduced delays, and increased packet delivery ratio of the vehicles when the entire system was considered.