# Clustering-Based Modified Ant Colony Optimizer for Internet of Vehicles (CACOIOV)
## 3 Proposed CACOIOV Architecture
### IoV's 5G Interface  
Frequently, the IoV works with an adherent of ad hoc manner. However, the infrastructural method can sometimes be employed in a scenario where a node lacks a neighbor [41]. In this situation, a semi- or full infrastructure can be employed to ensure a network which is connected fully. VANETs have communication ranges and coverages that are small in comparison to the infrastructure of the 5G. Issues with the IoV include mobility, broadcast of information, and the shortest route paths that can be mentioned in scenarios based on 5G. The 5G interfaces equipping IoVs will adopt additional features in comparison to the conventional IoVs, as well as V2X environments too. The access interface of 5G is made up of resources that are limited, hence having efficacious use of resources user clustering in 5G network are an advantage. 5G has adequate bandwidth for any service types, but desirable use of that is as infrastructure for back-up, since the spectrum of the 5G is paid and has limited channel numbers. Thus, the effectiveness and controlling of 5G access should be observed with algorithms that are dynamic [42]. For the networks that are heterogeneous, different devices communicate with each other with the use of distinctive service types.  
Nature-inspired clustering such as swarm intelligence-based algorithms are more reliable than conventional methods. However, these methods require more power of computation, which will affect the energy of the nodes. Hence, the methods are employed on such incidents where the primary issue is not energy consumption. Since OBU is a part of all vehicles in the IoV network and charges easy, energy is not a primary issue for vehicles.  
Another aspect of this is that services which are location-based are usually provided by GPS in conventional IoVs. However, in networks with 5G, the management of entities of mobility contains servers of user location which have a connection to the interface of the eNodeB (E-UTRAN Node B/Evolved Node B, which is hardware that enables communication between transceiver station and mobile phone network) access. Therefore, the server of user location can be taken in place of GPS to localize the vehicles' position.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Definition** \\ \hline V & Networks’ nodes \\ \(V_{total}\) & Total nodes in network \\ TR & Nodes’ transmission range-based defined range in standard of DSRC \\ \(\Delta T_{adj}\) & Degree of adjustment \\ T & Maximum iterations \\ t & Current iteration \\ D & Degree difference value for Load balancing in ad hoc network = 10 \\ \(f_{1}\) & The delta difference value of the clusters in t \\ \(f_{2}\) & Summation Distance of CMs from CHs \\ \(f_{t}\) & Quality of ant tour \\ \(w_{1,t}w_{2}\) & Equivalent weights assigned to two objective functions \(f_{1}\) and \(f_{2}\), respectively = 0.5 \\ ABS & Absolute value \\ ED & Euclidian Distance \\ \(|CM_{i}|\) & Total CMs in cluster \\ \(|t|\) & Tour length or total number of clusters \\ \(\tau_{ij}(t+1)\) & Pheromone trail value encountered in iteration t \\ \(\rho\) & Pheromone evaporation rate \\ He & Heuristic value \\ \(TR_{max}\) & Maximum transmission range \\ \(\lambda^{\prime}\) & Traffic service level of the road \(\simeq\) 0.1 \\ \(a\) & Traffic constant from traffic flow = 0.25 \\ \(T_{D}\) & Traffic density \\ \(T_{TH}\) & Traffic Threshold \\ \(L_{D}\) & Local vehicle density \\ \(V_{N}\) & Number of Detected vehicles’ neighbors \\ \(d_{front}\) & Distance between vehicles and it is furthest neighbor in front \\ \(d_{back}\) & Distance between vehicles and it is furthest neighbor behind \\ NL & Number of roads’ Lanes \\ \hline \hline \end{tabular}
\end{table}
Table 2: Notations list.