## 7 Edge Intelligence and Networking for IoV
### Edge Computing Intelligence for IoV  
Intelligent IoV should use a variety of intelligent computing platforms, such as cloud computing intelligence, edge computing, and on-vehicle intelligence. Providing intelligence at each layer of the IoV network architecture helps to meet the different requirements of IoV applications. IIoV will facilitate urban traffic management, vehicle insurance, road infrastructure building and repair, logistics and transit, in addition to vehicle driving and safety. IoV must be interconnected with other systems, such as Smart City, as a special case of the Internet of Things (IoT). Cloud computing and intelligence are mostly employed in this scenario for training and inference of AI models with massive datasets, as well as aggregating non-real-time data for wide-area networks deployed on the cloud. To overcome excessive latency in cloud computing, edge computing and intelligence can support IoV for processing delay-sensitive computational tasks, such as road-side image processing, aggregating, and storing local information. Edge computing devices  
Figure 13: Collaborative driving—see through. can be deployed on different edge nodes, such as on the RSU, BS, and vehicles having powerful onboard processing capabilities. Edge nodes provide computational services. The vehicle can offload tasks using either binary offloading or partial offloading schemes. Binary offloading is when the device determines whether the task is to be executed locally or to be offloaded to the edge server, depending on the workload of the server and the QoS requirements. In partial offloading the computational task is partitioned and executed on both the local device and an edge server. In [73], an edge learning framework is proposed and is demonstrated to reduce network traffic by 80% and running time by 69% over state-of-the-art cloud-based solutions. The author of [74] proposes edge-based data analysis and task allocation. Edge computing resources are utilized and task distribution for edge nodes based on game theory is applied. In [75], a novel real-time video analytics process is demonstrated using low-cost IoT devices and LoRaWAN networks to realize new services and applications that include traffic management through IoT edge computing. Edge computing and intelligence also support privacy-aware services employing federated learning at the edge. Edge devices provide a communication efficient and privacy-preserving federated learning framework for enhancing the performance of IoV, whereby in-vehicle learning models are trained by exchanging inputs, outputs, and their learning parameters locally, as described in [22]. Vehicles can transform into mobile data centers, perform federated learning to enhance transmission control protocol (TCP) performance over WiFi, and react in a timely way to the Internet to meet vehicles' needs. An energy-efficient computational offloading scheme employing deep reinforcement learning for the Intelligent Internet of Vehicles is discussed in [76]. Addressing the issue of running DNN, which require tremendous computational resources, high energy consumption and significant latency, the authors of [70] propose a collaborative framework for DNN co-inference between edge and end devices. The effectiveness of the framework was tested on a Raspberry Pi. Newer vehicles are equipped with a powerful onboard unit that can provide computing capabilities for other sensors in the vehicle or cooperate with other vehicles. On-vehicle intelligence is an approach for processing high delay-sensitive tasks, such as real-time decision-making for vehicle control. Intelligent sensors utilize advanced signal processing techniques, data fusion, intelligent algorithms, and AI to understand sensor data, for better integration of sensors and feature extraction, leading to measures that can be used in smart sensing applications. In [77], a vehicle vibration sensor is used to classify and monitor road conditions.