# Clustering-Based Modified Ant Colony Optimizer for Internet of Vehicles (CACOIOV)
## 1 Introduction
Technology for routing is a well-known research core of conventional networks. While routing is still a vital aspect of an inter-vehicle network, for VANETs, delivering the control messages is of equal importance [3]. A VANET model has been proposed for highway scenario purposes and it originated from the high-speed movement of vehicles on highways with fewer RSUs than in urban areas. Now is the proper time to state the general classification of routing protocols for VANET and IoV. Based on the literature, five distinct categories of routing protocols can be mentioned: delay sensitivity; routing in various scenarios' dimensions; transmission strategy, which includes broadcast, geocast, and unicast; information required for performing routing, which includes path-based, position-based, topology-based, and map-based; and routing network types, which includes heterogeneous and homogeneous networks. Since the concern of our study is the IoV environment, the heterogeneous network is addressed. In this regard, clustering algorithms are taken into account. However, this technique classifies them into nine distinct categories, which are mentioned in Figure 2.  
While one of the highly important issues for the designers of network is VANET scalability, a model based on clustering is one of the best solutions for such designers of network. VANET communication, which is cluster-based, is affected by the selection of the most appropriate cluster head (CH). When selecting the CH, a consideration of the distance and velocity of the vehicle is necessary for the increment of the stability and efficient message communication in clusters that are intra and inter. To maintain the connectivity of the VANETs, the range of transmission is highly important. If the transmission range is assumed to be static, the network's connectivity is affected and cannot be maintained, due to the non-uniform vehicle distribution and the rapid alterations in the conditions of the networks. Hence, a dynamic transmission range is necessary to sustain the great connectivity in networks that are non-uniform. This issue motivated us to consider dynamic transmission range for each vehicle in the IoV network. Within this aim, we propose a novel algorithm to allocate a dynamic transmission range to each node/vehicle based on local traffic density. Establishment of an appropriate clustering-based nature Ant Colony Optimization (ACO) on the VANET has illustrated successful results [5]. Numerous works have been shown in mobile ad hoc network algorithms based on nature inspiration i.e., swarm intelligence and bio-inspired by insects, as in ACO. This method can be feasible for successfully developing routing algorithms that are efficient. A temporary network is created from self-configuring nodes, in VANET. This is achieved in the absence of any managements that are centralized or infrastructures that are predefined. Forming a structure of clustering in a VANET environment is one of the means of managing such ad hoc networks. Clustering enables the creation of backbones that are dynamically virtual in a network, aiding the maintenance of the Service Quality in such networks. The experiences of VANET include constrained bandwidth issues and constant changes in topology. Therefore, cluster formation is one of the solutions for managing a network efficiently [5]. Since ideal clustering is a difficult problem in IoV, and is also an NP-hard problem [6], swarm intelligence-based optimization can be used to find near-optimal solutions. By following the principles that are nature-inspired, a swarm-based clustering is made. Such principles can be deduced from bird behavior, genes, and insect behavior. In this context, we can name some well-known algorithm approaches for VANET clustering, such as ACO-based [7; 8], the Grey Wolf Optimizer (GWO) [9], and Particle Swarm Optimization (PSO) [10]. To be precise, most of the research in this area is done based on a single-objective problem, learning weights and static ranges of transmission, except for Dragon Fly Algorithm [11].