# Clustering-Based Modified Ant Colony Optimizer for Internet of Vehicles (CACOIOV)
## 1 Introduction
This study focuses on answering some important questions about routing in a wireless mesh network that are listed below. * How is the stability of communication in IoV based on the dynamic nature enhanced? * How is a vehicular network that is more manageable, optimized, and scalable created to have equal distribution of network load? * How is higher performance achieved in regard to delivery ratio of packet, throughput, and end-to-end delay? During this research, the aforementioned questions will be answered, the routing protocol's overall performance will be addressed, and the best protocol will be chosen from the two introduced algorithms, which are AODV and ACO. In real-world application, the node's transmission range is not similar, and the clustering of IoV is multi-objective. Therefore, the dynamic behavior of IoV has propelled this study to develop a modified ACO-based technique. The aim here is to propose a novel framework named CACIOV to achieve sustainable transportation on the highway with smart mobility via two phases. Firstly, we propose a unique metaheuristic algorithm using an ACO algorithm to select the best vehicles such as CHs, and enhance network stability and provide the most appropriate and shortest routes during V2R communications. The CACIOV algorithm is developed based on two new ideas: (1) Intelligent first-node selection and (2) Dynamic weight-tuning for pheromone evaporation. Secondly, we propose the DA-TRLD algorithm based on local traffic density to assign dynamic transmission range to each vehicle (Section 3.2). The DA-TRLD algorithm used in the CACIOV protocol is to maintain the connectivity of IoV network, which is heterogenous and includes non-uniform vehicle distributions. The proposed framework includes different properties: (1) a newness of both algorithms, (2) dynamic parameters of network, and (3) 5G interfacing. For more emphasis, the method which is proposed is built to have multi-objective optimization capacities to have a solution which is more predictable. Our study objective is to provide a routing model to enhance route discovery for vehicles and to maintain network stability in an IoV network. Also, this work can be extended beyond 5G networks and other communication environments such as D2D in future communication. Contributions of our study are described as follows:  
* Introduction of a realistic communication model for clustering heterogeneous network that supports V2V and V2I communication. * Avoiding network dissemination problems, by introducing a novel metaheuristic modified ACO-based clustering algorithm \"CACIOOV\" to stabilize topology for cluster-based packet route optimization. * Avoiding the local optimum problem, which is a common problem during route-finding by introducing new approaches in searching space and pheromone evaporation. * Adapting the dynamic range of transmission in regard to density of local traffic by applying the DA-TRLD algorithm on CACIOOV, ACO, and AODV algorithms to maintain the connectivity of network. * Contributing to Sustainable Transportation and smart cities by measuring and analyzing the performance of these three routing protocols for the IoV. The structure of this study is as follows: Section 2 describes the research topic proposed and related works. Section 3 discusses the CACIOOV architecture, proposed DA-TRLD algorithm, and the 5G interfacing of IoV. Section 4 describes the channel models and stimulators. Section 5 illustrates the implementation of the proposed technique along with the outcomes obtained and a discussion about simulation outcomes. In this section, for the purpose of comparative analysis, and better description, mathematical modeling of each segment of the technique, is introduced. Finally, Section 6 provides the conclusion and a discussion of directions for the future.