## 3 Optimization Problems in Vehicular Networks
### Optimization Challenges in Traffic Improvement Application of Vehicular Networks
(2019) proposed an RSU optimized deployment scheme as a multi-objective optimization problem for mathematical modeling based on large vehicle data, which improves the quality of time-sensitive services while also reducing deployment costs. They proposed a two-step solution in which they obtained the initial RSU deployment location based on road topology and analyzed big vehicle data. They also used the K-nearest neighbor algorithm to remove the overlapping intersection of bidirectional lanes based on the actual road topology situation. Later, the branch-and-bound algorithm was used to achieve optimal RSU deployment. According to the results, the proposed scheme used a small number of RSUs to achieve high coverage. Another challenge is prediction accuracy, where knowing about potential traffic problems can aid in congestion relief and road capacity expansion. Based on collected vehicular data and the Continuous Time Markov Chain (CTMC), [PERSON] et al. (2019) developed traffic behavior in multi-lane roads and near intersections. In order to analyze system performance, the queuing theory was used to describe urban traffic dynamics, and CTMC in continuous time was used to forecast long-run average quantities such as congestion rates and average waiting times. Long-term estimates of traffic distribution can be obtained using this method, which employs a numerical method for solving the stationary distribution. In order to validate their model, the results were compared to a queue-based model and realistic traces. The numerical results show that the model accurately reflects real-world urban traffic behavior when historical traffic data are used. [PERSON] et al. [86] presented a VANET system with software-defined networking (SDN) for forecasting traffic flow behavior using computationally intelligent models. They proposed an architecture made up of RSUs and OBUs that is managed by an SDN controller framework and is linked to cloud infrastructure for real-time data storage and high computational capacity. They used a three-phase algorithm, including a configuration phase, a clustering phase, and a running phase, to identify the system's congestion-sensitive spots before implementing a machine learning model to learn traffic patterns for each spot. They also used the K-means algorithm to find three-dimensional spatiotemporal clusters, which were then processed to finalize the congestion-sensitive spots under a specific RSU. They used the LSTM recurrent neural network architecture to learn time series with long-term traffic flow dependency on the identified congestion-sensitive spot. In addition, detailed and precise LSTM hyperparameter tuning was performed to finalize the set of optimal hyperparameters required for convergence to an optimal traffic flow prediction solution. The results showed that their proposed method can predict future densities with an accuracy of 97% on the entire dataset. Another issue to consider in traffic management applications are packet storm problems where VANET sends warning messages to vehicles near congested roads in order to keep drivers informed of road conditions and provide the best possible routes to their destinations. This generates a significant number of alert messages, which may cause network congestion and QoS breakdown. In this context, [PERSON] et al. [87] proposed a simulation model to reduce broadcast storms by reducing redundancy. Based on three factors--position, distance, and orientation, they developed the next forwarder vehicle (NFV) protocol and, by using the DDP4V technique, analyzed each of these features. The proposed protocol reduced broadcast storms by using DDP4V NFV isolation known as wagon wheels to select the next forwarding vehicle, which can transport data packets 60% faster. In addition, when compared to AID and DBRS, DDP4V has fewer dispersed packets, which reduces retransmissions, and it outperforms standard techniques in high-traffic areas.