## 2 Vehicular Networks: An Overview
### Vanets and IoT, Edge Computing  
Network routing protocols are one of the two cornerstones of connected smart city mobility. They are enabled by IoT devices that, as the second cornerstone, are the foundation upon which interconnected ITS are built [45]. The IoT paradigm envisions communication between objects that are already part of everyday life to create an infrastructure of devices that are embedded into larger networks [46]. In the context of vehicular mobility, a vast array of use cases have been investigated and implemented in research: IoT devices can be used to reserve and guide vehicles to parking spots [47; 48] or to avoid vehicle collisions [49] by providing an exchange of information between network nodes. The challenge of transmitting large amounts of data over a vehicular network has led to research into how external processing and storage could ameliorate the exchange of time-critical information. Data-center facilitated cloud computing can dynamically integrate into a VANET application, allowing network nodes to off-load data-intensive applications [50]. [PERSON] et al. [51] was the first to propose a cloud-based VANET architecture to connect vehicles and support application loads. These vehicular cloud computing networks allow for scalable network architectures that support the ever-increasing stream of data and help alleviate connectivity limitations [52]. As [PERSON] et al. [53] argue, cloud computing might reach its limitations in the context of more demanding VANET environments where large numbers of vehicles demand real-time applications. Consequently, they propose to enhance VANETs with edge computing. Edge computing further develops the key functionalities of cloud computing by moving data processing units closer to each network node: Calculations to support network nodes are performed at the edge of a network [54]. [PERSON] et al. [55] demonstrate that using edge nodes as an \"intermediate interface between network and cloud\" in VANETs can indeed improve network latency and facilitate overall data flow. It is important to note that network structures supported by edge computing are not restricted to mobility on the ground; the concept can be extended to any network node in three-dimensional space, such as unmanned aerial vehicles (UAVs) [56; 57]. These UAVs can even be used to flexibly support a VANET architecture if needed [14]. Aside from providing a more capable architecture, edge computing also proves to be resistant to network attacks [58]; fast data transfers and processing allow for reliable message verification to ensure no malicious communication occurs in the network [59]. Recent proposals even go as far as integrating blockchain technology to ensure network integrity [60]. VEC is a promising technology that can be used to support ITS services, smart city applications, and urban computing. Figure 3 shows the problems and methods that are used in the literature reviewed in VEC. Figure 3: Operation of VEC. [PERSON] et al. [61] introduced a knowledge-driven (KD) service offloading decision framework for the Internet of Vehicles (IoV), which provides a unique platform for various vehicular services and aims at achieving long-term optimal performance experienced by vehicular users, and based on that, they proposed offloading decisions as a resource scheduling problem with single or multiple objective functions and constraints, where some customized heuristics are used. The framework consists of a decision model which uses deep reinforcement learning (DRL) to learn decision knowledge, and an observation function to obtain vehicular mobility and edge computing node data. To realize online optimization of offloading decisions, they proposed a KD service based on an online A3C algorithm. Evaluating the performance of KD service offloading decisions, they showed that the framework achieves low service delay, can learn the distribution of task data dependency, and almost always chooses a proper destination for large data transmission tasks. [PERSON] et al.