# Clustering-Based Modified Ant Colony Optimizer for Internet of Vehicles (CACOIOV)
## 2 Related Work  
### VANETs' Clustering  
The challenges in the transition phase of VANET and IoV networks depends greatly on communication security and communication stability in congested traffic. Therefore, VANET connectivity level must be wisely evaluated before real-world application is deployed. Diversity of VANET characteristics has an impact on VANET connectivity level. These characteristics can be named as vehicle/node communication range, vehicle flow and density, traffic lights and RSUs, and the communication equipment's market penetration. Research in this area reveals that with more vehicles equipped with OBU (communicating radios), the enhancement probability of vehicular communication will be increased, which directly leads to higher market penetration and the end result is the improvement of the VANET connectivity level [12]. As mentioned earlier in our study, organizing efficient and reliable intercommunication routes between vehicular nodes based on VANET characteristics is a challenging issue. For such issues, clustering is one of the solutions. The number of changes in clusters ought to be reduced by a good VANET clustering algorithm, which enhances the stability of the network. The other important issues, alongside clustering robustness and connectivity in VANET, include bandwidth, higher link quality to the RSU and the Internet application (i.e., application of Road Vehicle Communications such as a traffic management app, traffic safety app, road monitoring app, and comfort applications such as contextual information and entertainment app, weather information, etc.) that should be taken into account. To facilitate and manage a vehicle user's daily life, the vision of implementing VoIP applications will provide future drivers with the availability to use VoIP services (i.e., Skype, Google Talk, etc.) during congested traffic. Subsequently, the effectiveness of Internet applications is influenced by the routing protocol performance in terms of delay, etc. [13]. There are two classes of algorithms which belong to VANETs' clustering: connectivity-based algorithms and identifier based ones. For the identifier-based ones, the functionality of the node is in the form of the CH if it has the least ID among its neighbors. The connectivity-based clusters, on the other hand, pick a node such as the CH on the condition that the most neighbors are present in the range of transmission. [PERSON] and [PERSON] [14] believed a connectivity-based algorithm was not a better option in comparison to the clustering based on identifier. A CBRP algorithm, which is the transformation of the algorithms' lowest ID, was also presented by them [15]. The introduction by [PERSON] et al. [16] of the ant colony system (ACS) is presented based on outcomes in [17]. This has enlarged the scope of the algorithm's application for resolving various clustering-based issues that have arisen recently. As much as limited studies have been carried out via the use of the data from ACS, the subsequent outcomes have shown proof of the speed and accuracy of the algorithms based on ACS. \"Ant Colony Optimization with Different Favor (ACODF)\" was introduced by [PERSON] et al. [18]. This algorithm is made up of favorable ants and the adoption of Stimulated Annealing (SA) to visit the quantity of cities at a reductive rate. Moreover, a strategy of tournament election is engaged to rapidly select a path. The higher trailed intensity nodes are closer in this algorithm, while the farther ones are lower. Thus, visiting the nearby nodes will be preferred by ants and the trail reactivation will be done by ant pheromones. Lastly, construction of the cluster will be made via the division of the pheromone that was laid in between the data point edges. The other variation of the ACS algorithm is presented to cluster data as an ant by [PERSON] et al. [19]. [PERSON] et al. have made a proposition of two types of clustering algorithms for resolving analysis issues of clustering based on ant K-means (AK) [20] and ant systems (ASCA) [21].