## 3 Literature Review on IoV Analytics
### Main Research Problems
The'softwarization' of mobile networks has been one of the enablers of network slicing as a way to provide services with the required quality of service. In this context, and given the different requirements of V2X applications, as described in Section 2, network slicing has also attracted the interest of academia and industry in the framework of IoV [49; 50; 51; 52]. [PERSON] et al. [49] analyze how distributed remote radio heads and centralized baseband unit controllers must be paired to guarantee the requirements of best-effort and ultra-reliable low-latency communications slices in the IoV framework. The solution, based on genetic algorithms, takes into account capacity and delay of the network. [PERSON] et al. [50] propose the deployment of an emergency network slice to prioritize critical mission operations. The proposal is based on the popular deep recurrent neural network long-short term memory (LSTM), known as an effective solution for the forecasting of time series, and characterize how road events propagate over distant base stations. [PERSON] and [PERSON] [51], instead, address a more classical network slicing problem in the edge of IoV, where a Q-learning solution is proposed as the solution. Further, based on deep reinforcement learning, [PERSON] et al. [52] propose a soft actor-critic solution combined with an algorithm denoted as alternative slicing ratio search (ASRS) to provide radio access network slices with the required resources. Different from the research problems discussed above, researchers have also been active in the problem of user association and RSU distribution. Regarding the user association problem, [PERSON] et al. [53] develop a user association solution based on deep learning. In that work, feature learning is used to learn the link quality, and the strategy learning--either Q-learning or actor-critic--decides the association of each user. Moving a step forward, [PERSON] et al. [6] formulate the user association problem in vehicular networks as an uncapacitated facility location problem (UFLP) for jointly solving the RSU-to-vehicle allocation problem while managing the RSUs switch-on and -off processes. Differently from traditional UFLP approaches, the authors propose a fast-heuristic approach based on a dynamic multi-period time scale mapping. [PERSON] and [PERSON] [54] estimate the dwelling time of vehicular nodes in each cell, and a handover procedure based on these estimates is proposed. A problem slightly different is addressed in [PERSON] et al. [55], where not only user association is studied through a simulation-based analysis, but also the antenna selection in the vehicle side. The massive data exchanged in IoV brings the capacity of the network to the limit; thus, risking the ability of the network of meeting the required quality of service. In this context, edge caching has also been investigated [56, 57, 58, 59]. [PERSON] et al. [56], given the massive amount of data to be shared among vehicles, propose caching the shareable content in RSUs to reduce the V2N traffic. The problem is modeled as an integer nonlinear program (INLP). Instead, [PERSON] et al. [58] present a reinforcement learning-based solution, namely Res6 Edge. Reinforcement learning is one of the most popular approaches to address the content caching update. Further, heuristics have been proposed to solve the edge caching problem. For instance, in [59] a caching solution aimed at the distribution of large size files. The large dynamics of vehicular networks pose challenging requirements for routing algorithms. Although routing has been extensively studied in the past, there are some recent works proposing routing algorithms for IoV. The connectivity of vehicular ad hoc networks has been analyzed though a stochastic geometry approach when the distribution of vehicles is not uniform over the whole scenario in [60]. The impact of connectivity and energy efficiency is pointed out. In [61], instead, a graph-theory-based routing algorithm is presented.