## 9 Conclusions  
Due to the fast growth of IoT and IoV, the conventional host-based networking paradigm faces a number of issues, including those related to scalability, mobility, addressing and security. Furthermore, recent applications demand real-time data which necessitates the rethinking of network models. The Internet of Vehicles (IoV) is different from the traditional Internet and other IoT networks, such as weather monitoring, due to the highly dynamic nature, mobility and high density of vehicles, and thus requires special attention. Security is a major concern in vehicle communication. In this paper, we have presented detailed reviews of the convergence of various technologies supporting IIoV, including ICN, edge computing, and AI. Edge intelligence accelerates V2X communication, vehicular edge computing, and intelligent networking. We also analyzed different techniques reported in the literature that use ICN-based communications for IoV networks. Lastly, we presented the requirements and challenges of building an intelligent communication networks architecture and intelligent computation for IoV. We hope that this work will serve as a useful reference in the area of intelligent IoV and inspire new topics of research.  
Conceptualization, S.S.M. and M.Z.; methodology, M.Z.; investigation, S.S.M., M.Z., M.L. and E.P.; resources, S.S.M.; writing--original draft preparation, S.S.M. and E.P.; writing--review and editing, E.P.; visualization, S.S.M.; supervision, M.Z. and M.L.; project administration, M.Z.; funding acquisition, M.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors would like to thank the STEP and TRIL programmes of the Abdus Salam International Centre for Theoretical Physics (ICTP).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l l l} AI & Artificial Intelligence & LRU & Least Recently Used \\ ANN & Artificial Neural Network & LSTM & Long Short-Term Memory \\ APU & AI Processing Unit & MANET & Mobile Ad Hoc Networks \\ BS & Base Station & MDP & Markov Decision Process \\ C-RAN & Cloud-Radio Access Networks & MEC & Mobile (Multi-access) Edge Computing \\ CDN & Content Delivery Network & ML & Machine Learning \\ CNN & Convolutional Neural Network & MLP & Multi-Layer Perceptron \\ CAM & Cooperative Awareness Message & NDN & Named Data Networking \\ CS & Content Store & NFV & Network Function Virtualization \\ CV & Computer Vision & NLP & Natural Language Processing \\ D2D & Device to Device & NPU & Neural Processing Unit \\ DNN & Deep Neural Network & PIT & Pending Interest Table \\ DQL & Deep Q-Networks & QoE & Quality of Experience \\ DRL & Deep Reinforcement Learning & QoS & Quality of Service \\ EC & Edge Computing & RL & Reinforcement Learning \\ ETSI & European Telecommunication & RSU & Roadside Unit \\  & Standardization Institute & \\ FCNN & Fully Connected Neural Network & SDN & Software Defined Network \\ FIB & Forwarding Interest Table & SGD & Stochastic Gradient Descent \\ FIFO & First In First Out & TL & Transfer Learning \\ FL & Federated Learning & V2I & Vehicle to Infrastructure \\ GAN & Generative Adversarial Network & V2V & Vehicle to Vehicle \\ GNNs & Graph Neural Networks & V2X & Vehicle to Everything \\ ICN & Information Centric Networks & VANET & Vehicular Ad Hoc Networks \\ IIoV & Intelligent Internet of Vehicles & VR & Virtual Reality \\ IoV & Internet of Vehicle & WAVE & Wireless Access Vehicular Environment \\ ITS & Intelligent Transport Systems & & \\ \end{tabular}