## 2 Vehicular Networks: An Overview
### Vanets and IoT, Edge Computing
[62] proposed a new edge caching scheme that optimizes content placement and delivery in VEC and networks with limited storage capacity and bandwidth by taking into account time-varying content popularity, dynamic network topology, and vehicle driving paths. Edge caching was modeled as a double time-scale Markov decision process (DTS-MDP). The joint content placement and the delivery problem is NP-hard long-term mixed integer linear programming (MILP). As a result, the variable participation of vehicles increases the operational complexity of the edge caching system, making it difficult to find the best solution. Thus, they proposed a deep deterministic policy gradient (DDPG) learning algorithm based on a DRL-based cooperative caching scheme to provide low-complexity decision making and adaptive resource management, and they accelerated the learning speed and improved caching performance by using mini-batch gradient descent. Furthermore, they concentrated on the model-free reinforcement learning approach to provide training guidelines based on a large number of historical experiences. This model-free approach is divided into three categories: critic-model (value-based approach), actor-model (policy-based approach), and actor-critic learning approach, which employs deep neural networks to provide an accurate estimation of deterministic policy function and value function. As a result, the actor-critic learning framework and the double time-scale content caching model combined to develop a DDPG-based cooperative caching technique. The performance was compared using two benchmark schemes: (i) random caching; and (ii) noncooperative caching. To improve the accuracy of vehicle destination prediction, the destination of the vehicle was predicted using a machine learning model based on shorter strings rather than longer strings to represent the transport region of smart vehicles. The analysis of caching performance based on the DDPG learning algorithm revealed that as the number of episodes increases, all content caching schemes can approach their stable cumulative average cost. The noncooperative caching scheme had the highest average system cost, which includes the cost of content storage and access. In addition, the proposed caching scheme yielded the lowest system cost, and lowest content access latency, and improved the content hit ratio, particularly in the low content delivery latency, when compared to the other benchmark schemes. [PERSON] et al. [63] proposed a task offloading scheme based solely on V2V communication, based on the gathered period of vehicles in urban environments due to traffic lights or areas of interest (AOI) to minimize task processing time. The Max-Min Fairness scheme is used to optimize the task execution time, which is then solved by the particle swarm optimization (PSO) algorithm. On the one hand, for the special case where all service vehicles participate in task processing, the proposed algorithm provides the optimal solution based on adapted Max-Min Fairness. On the other hand, the PSO algorithm is used for generating a feasible solution for the general case where it is unknown whether each service vehicle will participate in task processing or not. Furthermore, to evaluate the performance of their proposed scheme, they generated vehicle track files using the TIGER map and then used the IDM IM model provided by VanetMobiSim to compare the performance of computed schemes such as local computing, offloading with the Max-MinFairness Algorithm, and offloading with the PSO Algorithm. The results showed that the Max-Min Fairness algorithm and the PSO algorithm reduced task execution time based on the number of service vehicles used. Additionally, the increasing number of service vehicles showed that the PSO algorithm is slightly better than the Max-Min Fairness algorithm in terms of robustness over different task sizes for each scheme. [PERSON] et al.