## 3 Literature Review on IoV Analytics
### Optimization Tools  
As described in Section 3.1, the range of research issues in the field of IoV is vast and is being actively researched. However, although there is consensus on the research open issues, there is not actually an agreement on which optimization tools are the most convenient ones to meet the requirements of IoV while, in parallel, fitting the network architecture, characterized by distributed nodes, most of them with limited computing capacity, and with large dynamics. Recently, artificial intelligence (AI) is gaining ground as the key optimization tool for the challenges dealt in IoV [75]. Yet, the range of different optimization possibilities goes far beyond AI. In the following, the main optimization approaches mentioned in the previous section are listed and discussed.  
AI has emerged as a promising approach to face IoV. As discussed previously, both supervised and unsupervised machine learning are proposed in two different directions. On the one hand, unsupervised learning is mainly used for clustering vehicles or base stations. Conversely, recurrent neural networks, such as LSTM, are becoming popular as supervised machine learning solutions able to forecast time series [76; 77; 78]. The main drawback of these approaches in IoV is two-fold. First, the amount of data and time required to train the network can be an obstacle in some scenarios, particularly when some aspects of the scenario vary. For instance, the installation of an additional RSU, the blockage of some streets, etc. Secondly, machine learning approaches do not perform properly when unexpected events occur. If low probability but critical events are not identified and conveniently processed in the training phase, the performance of the solution is degraded. In parallel, (deep) reinforcement learning has also undergone a considerable boom as a model-free approach to solve optimal control problems in IoV [74, 79]. Although reinforcement learning is appropriate for IoV problems, it suffers from slightly different drawbacks from supervised/unsupervised learning. In this case, reinforcement learning is able to adapt to changes that occur in the environment, though at the expense of extended convergence time, which may degrade the performance if changes are frequent. However, recently a new ML training paradigm in the form of DML has emerged as a promising technique, especially in the case of latency-critical vehicular networking scenarios. Various DML techniques such as federated learning (FL) [80], decentralized learning (DL) [11], and collaborative learning (CL) [81] have shown great promises in terms of reduced training costs and frequent/online ML model updates for reducing the model drift, allowing their use in dynamic scenarios.  
Game theory is also a popular approach to deal with IoV optimization problems [82]. It is a particularly appropriate framework for decision making when there is a clear interdependence between the decisions of the different 'players' involved. However, game theory can present scalability issues and it assumes that all users have knowledge on their own pay-off functions, which is not always the case.  
The optimization framework can rely on a model. This is the case of approaches such as stochastic geometry, graph theory, integer programming, or Lyapunov optimization. In all these cases, the need of an accurate model limits the actual real life application. Moreover, in integer programming and graph theory, scalability must be carefully analyzed when the expected number of vehicles is large.  
Table 1 summarizes the main challenges identified in the literature on IoV analytics, as well as the methodologies usually employed to cope with each of them.