## 2 The IoV Scenario and Use Cases
### Communications and Computation  
Standardization of vehicular communications took two different directions that still remain nowadays--one of them led by IEEE, and the other one carried out in the framework of 3 GPP as a part of cellular technology [17]. Initially, the automotive industry, along with IEEE, led the standardization of dedicated short range communications (DSCR) with the development of IEEE 802.11p [18]. This standard is based on carrier sense multiple access/collision avoidance (CSMA/CA) and incorporates additional features to handle high mobility. However, and despite the new features adapted to vehicular scenarios, the medium access is still contention-based. In order to improve its performance, a new amendment has been developed as IEEE 802.11 bd [19,20].  
In parallel, and motivated by the importance of the automotive vertical, 3 GPP incorporated the vehicular communications in Release 14, which has been enhanced since then in the successive releases until Release 17 [21, 22, 23], frozen in 2022. This standard is usually known in general as Cellular V2X (C-V2X), or LTE-V2X or 5G NR-V2X depending on the release. Details on C-V2X can be found in outstanding works [24,25, 20, 26]. Focusing on the 3 GPP standard, two links are defined: the link between the vehicle and the base station, and the link between a vehicle and another vehicle or pedestrian, namely the sidelink. The resources of the link between the vehicle and the base station are managed by the base station, but for the sidelink, four different modes of operation are defined, two of them where resources are allocated without the support of the network, and two more where resources are granted by the network. The management of these resources is one of the key open research challenges.  
As stated above, the operation of V2X is intimately bound up with the exchange and process of massive data, and therefore the computational paradigm is one of the key aspects in IoV. In [27], the two main computational paradigms for IoV, which are cloud computing and edge/fog computing, are identified and described. The main idea lying behind these two paradigms is the existence of centralized network computing capacity, able to aggregate data from different parts of the network, and edge computing capacity, which reduces the communication latency between the vehicle and the computational node. The centralization of computational capacity in the cloud benefits from cost-efficiency and scalability. However, moving computational capacity towards the edge of the network, enabled by technologies such as multi-access edge computing (MEC), allows instantiating application servers in the edge [28]. The implementation of MEC in the IoV architecture is also known as vehicular edge computing (VEC), and stands for a distributed computation between the vehicles themselves and the edge servers [29]. Some of the current research areas in VEC are the mobility management, since some computational tasks must be handed over and migrated when vehicles move away; the loss of connectivity between the task computing node (either a vehicle or an edge server) and the node that offloaded the task computation; the problem of allocating computational resources to each task in the existing nodes, either vehicles or servers [30]. In this context, optimization techniques must be able to take into account the IoV communications and network architecture nature in a convenient manner. Real time or near-real time approaches are required to keep the pace of the scenario dynamics.