## 3 Literature Review on IoV Analytics
### Main Research Problems
Results are promising, but the main drawback of the proposal has to do with scalability of the problem and the required computational time. [PERSON] et al. [62] propose an algorithm to control the routing of an official business vehicle flow. The algorithm makes use of data provided by the in-vehicle network environment. The authors make use of an expert system, and employ the K-means clustering algorithm to process traffic flow data. In the context of highly dynamic UAV networks, [PERSON] and [PERSON] [63] analyze different routing protocols considering factors such as network topology, UAV positions, hierarchies, and uncertainty. Similarly, in the context of vehicular ad hoc networks, [PERSON] and [PERSON] [64] also analyze several routing protocols for UAVs. The interested reader could refer to [65] for an overview of the different routing protocols considered to be used in IoV scenarios. Other less popular research topics can be found in the literature. For instance, the vehicle software over the air update is dealt in [66]. The objective of the problem is finding the minimum set of base stations from which software updates should be downloaded. To do so, the authors perform traffic pattern analysis to predict the over the air update delay by using a transfer learning approach, and then the K-means algorithm to cluster the base stations. Another example is the service migration. The idea of micro-services instantiated when and where needed becomes more challenging when the mobility of the scenario is large. In [67], a Lyapunov optimization approach is proposed to select where services must be instantiated, and how many instances of the service are required to guarantee the service continuity. The prediction is performed with a recurrent neural network. The mobility prediction has also been studied aiming at creating reliable mobility models. In the framework of IoV, road safety has been addressed as a mobility prediction problem in [68]. Specifically, LSTM is executed in the MEC to predict the trajectory of the vehicles and assist road safety with proactive actions. Similarly, [PERSON] et al. [69] analyze dangerous road locations and propose a multi-hop protocol to guarantee vehicles' safety. The proposal is modeled with a clustering approach. Optimizing the traffic flow and corresponding congestion management is an important aspect of modern vehicular networks. Here, we discuss some of the promising studies from recent times that are analyzing the congestion problem. [PERSON] et al. [70] have analyzed various bio-inspired optimal routing algorithms for IoV scenarios from recent studies. Comparisons are drawn based on the strengths, weaknesses, and various other critical characteristics leading towards a potential combined model of a multi-modular bio-inspired approach to IoV routing. In another case, [PERSON] et al. [71] proposed a new vehicular traffic congestion pricing system based upon reward/penalty policies. The system tends to reward the good behavior of users through the proposed T-Coin being a virtual currency and penalizing the users with bad behavior. The proposed virtual currency (T-Coin) can also be used to manage road reservations. [PERSON] et al. [72] have combined the IoV resources with the virtual currency for creating a reward/penalty-based framework for optimizing the traffic flow through congestion management. A hybrid optimization technique based upon the modified ant colony and firefly optimization techniques is proposed in [73] for finding the optimal path impacting the reduced traveling time. [PERSON] et al. [74] have effectively utilized edge computing facilities for optimizing traffic management via adequate traffic light control. In particular, a decentralized reinforcement learning (distributed deep Q-learning) scheme is proposed for solving the problem at the edge.