# Clustering-Based Modified Ant Colony Optimizer for Internet of Vehicles (CACOIOV)
## 2 Related Work
### Clustering in IoV  
A fuzzy system based on logic (FIS) is used in the proposed algorithm of distributed multi-channel and mobility aware cluster-based protocol (DMMAC) to solve the media access issue in hidden terminals for IoVs that were brought forward by [30]. Due to DSRC coverage and a constricted amount of channel communication, a greater opportunity to use the same channel by any node exists. Using FIS sub-channels, this is efficient for this issue, with efficient distribution in the network to curb delays in transmission [30]. The adroit algorithm is introduced for the purpose of efficacious routing in the 5G-cloud-VMesh network (5 CVN). On the access layer of 5G, the effectual usage of the source is mentioned by the choosing of a proper gateway of vehicles that are optimal, and aggregator B node (ANB). The performance of the adroit algorithm was well received in performance as well as with the scenarios with respect to other means, although it used only 3 parameters [31]. [PERSON] et al. proposed another 5G and VANET model for Device-to-Device (D2D) communication. For the sake of optimal connectivity, a transmission parameter sensing algorithm is employed considering the resources of D2D spectrum, as these are targeted by sensing algorithms to achieve efficacious connectivity in the D2D environment [32]. The proposition of an SDN-based algorithm on 5G communication is made by [PERSON] [33]. The use of this algorithm is for the management of urban traffic in VANET and for improving repercussion time for emergency circumstances. Some parameters, such as the localization of the vehicle, traffic prediction mechanisms, and control, are involved in this architecture's method. [PERSON] and [PERSON] [34] constructed an IoV optimizer algorithm based on dynamic route with the aid of algorithms inspired by nature. Two widely known nature-motivated algorithms, PSO and ACO, are introduced to optimize routing in IoV, while PSO efficiency is less than the ACO, via the provision of longer routes, which require more traveling time. The method of the removal of the network IP's deficiency network, which is contact centric-based (CCN), is proposed in [35]. [PERSON] et al. [36] made a design of the CCN-IoV framework for the CCN methodology in IoV. The performance of this architecture is scale based on the parameters of the network, as in delay and throughput in transmission. This algorithm targeted IoV scalability but is slow for the provision of solutions that are optimal for any proposed scenarios. The network performance of an algorithm based on ACO for VANET is assessed by comparing the algorithm with two well-known algorithms, which are metaheuristics-based. These algorithms are comprehensive learning particle swarm optimization (CLPSO) and multi-objective particle swarm optimization (MOPSO) [7]. The cost enhancement of packet routing was done by this algorithm, and a number of clusters were minimized, but the weights were still not dynamic. In this method, several parameters of communication, i.e., the range of transmission, pattern of mobility, and topology, are taken into consideration, but are assumed to be uniform for the entire node. On the other hand, having each node with similar range in the network is very unlikely. To conclude, because of the dynamic nature, techniques of evolution can uncover interesting solutions and answers from parametric networks that are larger and non-uniformed. The dynamic behavior causes evolutionary algorithms to be more synchronized and persistent, to address MOPs. The provision of solution sets, and not just single solutions, explains why the algorithms are built. Several algorithms of evolution are upheld for problems of diverse types, such as the artificial neural network (ANN), the artificial immune system (AIS) [37], the adaptive neuro-fuzzy interface system (AFIS) [38] and the genetic algorithm (GA), which are very popular. Thus, methods based on swarm intelligence are explored for solving MOP problems in the literature [39].