## 6 ICN and Edge Computing Convergence Suitability Analysis for IIoV
### Converged IoV Infrastructure  
The benefits of ICN for IoV stem from the fact that a requested content might be located in a server far away from the current vehicle position, but it also might have been cached in a closer repository, easily accessed by its named content, thus reducing the latency and backhaul traffic. This is particularly relevant due to the dynamic aspect of IoV; the content might even be cached in a nearby vehicle. ICN promises efficient content delivery, and supports mobility, scalability, and security. It allows Internet applications to be developed based on named requests, caching, and any-casting. Because of its various advantages, ICN offers an interesting solution for IoV communications and seems to fit perfectly with many IoV applications [18; 19]. Its in-network content caching and forwarding methods enable the dissemination of information between different vehicles in the network. In the ICN context, vehicles can be consumers, producers, and data forwarders at the same time; RSUs, BSs, and core network elements can provide NDN forwarding and caching capability. For an efficient ICN-based IoV network, all ICN core elements described in Section 2 should be optimized to meet the frequent topology changes of IoV networks. Intelligent and adaptive naming, routing/forwarding, security/privacy, and mobility-aware design are necessary to provide efficient IoV applications and services, as described in the following section. Edge computing is another enabling technology that meets the time-sensitive requirements of IoV to offer pervasive services for massive numbers of connected vehicles with low latency and very large bandwidth. Traditionally, cloud computing offered computational, storage, and on-demand services to a far away user. However, as many IoV applications require low latency and high bandwidth, edge computing provides services in close proximity to end-users. Future Internet applications require low latency, high security, mobility, and scalable content distribution. Edge computing for IoV is an ideal solution to remove the burden on backhaul networks. However, existing TCP/IP-based edge computing solutions have some weaknesses when applied to support cloud services at the edge of the network. Edge computing offers many benefits associated with latency challenges, as it provides high bandwidth close to the user. In addition, advanced computing enables the processing of real-time tasks closer to the source or even on it. In recent years, the marriage of edge computing and AI has spawned a new research area called edge intelligence [70]. Similarly, the marriage of ICN and AI has spawned intelligent networking. Recent advances in deep learning have accelerated the realization of autonomous vehicles. Edge computing, which is one of the strategies of 5G, provides an optimal service for real-time access and high bandwidth. ICN and edge computing can be combined, resulting in synergistic benefits for IoV. Several AI approaches have been used  
Figure 10: Typical IoV TinyML. for solving problems in IoV and autonomous vehicles. Table 4 shows the mutual benefits of the synergy of ICN and edge computing. Figure 11 shows the convergence of ICN, edge computing and AI for IIoV. ICN and edge computing are essential for achieving this goal. By leveraging the ICN perspective, vehicular users can obtain multimedia chunks via the vehicle-edge-cloud approach to improve the delivery quality. ICN with edge computing is an excellent fit and