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The Internet of Vehicles (IoV) has recently become an emerging promising field of research due to the increasing number of vehicles each day. IoV is vehicle communications, which is also a part of the Internet of Things (IoT). Continuous topological changes of vehicular communications are a significant issue in IoV that can affect the change in network scalability, and the shortest routing path. Therefore, organizing efficient and reliable intercommunication routes between vehicular nodes, based on conditions of traffic density is an increasingly challenging issue. For such issues, clustering is one of the solutions, among other routing protocols, such as geocast, topology, and position-based routing. This paper focuses mainly on the scalability and the stability of the topology of IoV. In this study, a novel intelligent system-based algorithm is proposed (CACOIOV), which stabilizes topology by using a metaheuristic clustering algorithm based on the enhancement of Ant Colony Optimization (ACO) in two distinct stages for packet route optimization. Another algorithm, called mobility Dynamic Aware Transmission Range on Local traffic Density (DA-TRLD), is employed together with CACOIOV for the adaptation of transmission range regarding of density in local traffic. The results presented through NS-2 simulations show that the new protocol is superior to both Ad hoc On-demand Distance Vector (AODV) routing and (ACO) protocols based on evaluating routing performance in terms of throughput, packet delivery, and drop ratio, cluster numbers, and average end-to-end delay.

This study focuses on Vehicular Ad-hoc Networks (VANETs) stability in an environment that is dynamic which often leads to major challenges in VANETs, such as dynamic topology changes, shortest routing paths and also scalability. One of the best solutions for such challenges is clustering. In this study, we present five novel routing protocols based on Dynamic Flying Ant Colony Optimization (DFACO) algorithm to achieve minimum number of clusters, high accuracy, minimum time and solution cost by selecting the best cluster-head which is obtained from a new mechanism of dynamic metaheuristic-based clustering. In this regard, major improvements are applied on classical DFACO by adjusting the procedure for updating the pheromone and tuning the evaporation rate that has a major role in DFACO. In this research two individual phases of experiments are conducted for performance evaluation of proposed routing protocols. The presented solution is verified and compared to classic Ant Colony Optimization (ACO), DFACO and ACO Based Clustering Algorithm for VANET (CACONET) algorithms in phase one; and compared to clustering algorithms such as Center Position and Mobility CPM), Highest-Degree algorithm (HD), Angle-based Clustering Algorithm (ACA) in phase two through NS-2 and SUMO simulation tools. Simulation results have confirmed the expected behaviour and show that our proposed protocols achieve better node connectivity and cluster stability than the former.

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