## 3 Literature Review on IoV Analytics
### Main Research Problems  
Given the importance of both computational and communications in IoV, one of the main research topics addressed in the literature is the joint management of computational and communications resources, aiming at allowing a distributed and collaborative processing environment as well as optimizing the communication infrastructure. Due the limited computational capacity of vehicular nodes and the need for intensive computation, computation offloading techniques have attracted the interest of the research community [34]. Task offloading is defined as the transferring of computational tasks from nodes with limited computational capacity--usually vehicles--to nodes with more extensive computational capacity--e.g., RSUs or the cloud servers. Several works address the task offloading problem from similar perspectives [35; 36; 37; 38; 39; 40]. [PERSON] et al. [35] propose a reinforcement learning-based solution to distribute tasks among the available MECs. Specifically, the policy gradient-based distributed computational offloading problem (PGDCO) is proposed and takes into account the dependency between tasks, parallelizing in different MEC servers, the execution of independent tasks, and serializing the computation of dependent tasks. Similarly, in [36], the problem is addressed without taking into account the tasks dependency, and a deep deterministic policy gradient solution is proposed. [PERSON] et al. [37] introduce a scenario where an SDN controller manages a set of MEC servers, each one allocated in an RSU. The distribution of tasks among MEC servers, along with the management of resources such as power transmission, is modeled and solved with a two level solution based on Q-learning and potential game theory. Further, by modeling the task offloading problem with game theory, [PERSON] et al. [38] set the framework of a problem to decide whether a task is not offloaded and executed in the vehicle, or it is offloaded to neighboring vehicles or edge servers of cloud servers. [PERSON] et al. [39] propose a solution based on stochastic network calculus to compute the delay upper bounds and a federated Q-learning algorithm to distribute the tasks. Finally, [PERSON] et al. [40] propose a network selection strategy for optimizing the computation offloading between vehicles and RSUs, acting as VEC nodes. The problem has been solved resorting to both on-line and off-policy algorithms by exploiting the multi-armed bandit theory. As for the communication resource allocation problem, two outstanding surveys have been published [41; 42]. Focusing on the specific resources allocation problems, the distribution of resources in sidelink communications operated in a distributed fashion--i.e., modes 2 and 4 of operation--is addressed in some works [43; 44; 45]. Thus, [PERSON] et al. [43] pose a simulation based analysis of scheduling performance in mode 2 sidelink communication. In particular, the work studies a scenario where part of the users have full-sensing capacity and part of the users have limited sensing capacity. It is shown that defensive transmission strategy employed by full-sensing users mitigates the packet delivery ratio degradation. Instead, [PERSON] and [PERSON] [44] propose a resources reservation for mode 2 aimed at efficient allocation for aperiodic arrivals. Further, focused on the distributed reservation of resources for sidelink communications, in this case in mode 4, [PERSON] and [PERSON] [45] propose a semi-persistent scheduling solution combining full-duplex transmission/reception and a deep Q-learning algorithm. In [46; 47], deep reinforcement learning solutions are proposed for power and channel allocation. In particular, [PERSON] et al. [47] introduce a multi-agent reinforcement learning solution to predict the position of the vehicles and allocate the channels in a distributed manner. Conversely, stochastic geometry is used to analyze the performance of distributed resources allocation in ultra-dense scenarios, such as traffic jams, in [48].