## 3 Optimization Problems in Vehicular Networks
### Optimization Challenges in Traffic Improvement Application of Vehicular Networks  
VANETs promise to improve transportation efficiency, accident prevention, and pedestrian comfort by providing a variety of services and applications to drivers and travelers. These services and applications can be classified as safety-related, infotainment, traffic improvement, or driving system monitoring applications based on their applicability [77]. Figure 6, shows the taxonomy of the literature reviewed in this section on the traffic improvement application of VANET. There are several challenges to overcome in order to optimize road traffic and reduce travel times by avoiding traffic congestion. One of the difficulties concern infrastructure deployment, where UAVs can act as flying RSUs, relaying data to vehicles outside the RSUs' coverage range. In this context, a collaborative network coverage enhancement scheme was proposed by [PERSON] et al. [78] to bring these uncovered vehicles within the  
Figure 6: Operation of traffic improvement application. infrastructure's coverage. The PSO algorithm was used to determine the best positions to deploy the UAVs, taking into account factors such as vehicle density, heading direction, and previous coverage information. The new positions of the dispatched UAVs were calculated after each time frame, and the UAVs were instructed to move to these positions. Using the traffic simulation tool SUMO, the authors compared the performance of their proposed scheme to other UAV-assisted VANETs schemes, including those without UAVs, fixed UAV-assisted VANETs, and hovering UAV-assisted VANETs, in terms of PDR, hop counts (HOPs), EED, and throughput. The results showed that their proposed scheme outperformed its competitors in the simulation of Daegu, South Korea. Deploying RSUs in urban areas can be a complex task due to the high cost of installing them at intersections and the large number of possible combinations when there are many intersections. To address this issue, [PERSON] et al. (2019) used various metaheuristics, including genetic algorithms (GA), simulated annealing (SA), and improved versions of these algorithms, to determine the best approach for achieving high coverage rates on roads in the target area while deploying a minimum number of RSUs at intersections. The GA-Basic approach includes a probability of performing a mutation operation, where two bits are chosen randomly, while the GA-Improved approach focuses on individuals that increase the overlap of coverage areas. The SA-Basic approach generates an initial solution randomly, while the SA-Improved approach generates it after a preprocessing step that avoids placing RSUs at closely spaced intersections. The authors used the OMNeT-5.0 and SUMO simulators to evaluate the routing performance in terms of PDR and EED based on the number of RSUs deployed in the urban area. The results showed that the GA-Improved approach required fewer RSUs and provided better routing performance in terms of PDR and EED compared to the other approaches. Additionally, various recent studies address different diversity problems, where [PERSON] et al. (2019) presented mathematical formulations for combinatorial optimization such as MaxMin, MaxSum, MaxMinSum, and MinDiff and solved the problem using the commercial CPLEX solver. [PERSON] et al. (2019) proposed new instances, tested them through computational experiments, and demonstrated how these problems have evolved over time from an operations research standpoint. Moreover, based on other previous works [PERSON] et al. (2019) formulated a new MILP model to propose an exact and heuristic approach to solve the CDP and later [PERSON] et al. (2019) proposed a BR algorithm that uses the construction-destruction concept to generate high-quality solutions for the CDP in short computing times. Furthermore, [PERSON] et al.