## 8 Issues and Recommendations for Future Research
### Intelligent Connected Platform  
Following the success of cloud-based AI services, products such as Google's Cloud IoT Edge, Amazon Edge, and Azure IoT Edge, are being provided which extend data processing, analysis, and storage close to endpoints and provide edge computing services. Thus, 5 GAA was established with the aim of bringing the automotive and telecommunication industries together to develop end-to-end solutions for future mobility and transportation services. Further partnering has occurred with standardization organizations, such as ETSI. Thus, edge intelligence can become a pervasive paradigm allowing edge devices to develop powerful edge AI functionalities. IIoV as an intelligent connected platform relies on the support of high-tech technologies, such as mobile internet, edge computing, cloud clouding, big data, and AI. The platform should support flexible service deployment, splitting and deploying independently as needed to meet the needs of specific vendors. To fully harness the potential of edge intelligence in IoV, several key challenges must be overcome. Intelligent connected platforms should be compatible with heterogeneous systems. The  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{Naming and lookup} & \multirow{2}{*}{[89]} & \multicolumn{2}{c}{Hierarchical and hash-based naming with} \\  & & & efficient Compact Trie name management scheme & \\  & & & for vehicular content-centric networks (VCCN) & \\ \cline{2-4}  & & [94] & NLP based naming & NLP \\ \cline{2-4}  & & [93] & Smart name lookup for NDN & Neural network \\ \hline \multirow{8}{*}{**ICN**} & \multirow{8}{*}{Routing and forwarding} & \multirow{8}{*}{[101]} & Reinforcement learning-based algorithm for} \\  & & & efficient and adaptive forwarding in named data & RL \\ \cline{3-4}  & & \multirow{8}{*}{[102]} & \multirow{8}{*}{[101]} & Forwarding plane enables each router to select \\  & & & the next forwarding hop independently without \\  & & & relying on routing & \\ \cline{3-4}  & & \multirow{8}{*}{[124]} & Select suitable forwarding interface based on the \\  & & & current network status & \\ \cline{3-4}  & & [125] & Adaptive forwarding strategy & RL DQN \\ \cline{3-4}  & & [103] & Adaptive forwarding strategy & RL with random NN \\ \hline \multicolumn{2}{c}{Producer Mobility} & \multirow{2}{*}{[115]} & Predict the location of producer node using its \\  & & & past movements & \\ \hline \multirow{4}{*}{Security} & \multirow{2}{*}{[122]} & Guarantee the security of NDN-based IoV & Blockchain-based mechanism \\ \cline{3-4}  & & \multirow{2}{*}{[120]} & Mechanism for cache poisoning protection and \\  & & & access control in NDN vehicular edge & Blockchain based solution \\  & & computing networks & \\ \hline \multirow{8}{*}{**Edge**} & \multirow{8}{*}{Caching} & \multirow{8}{*}{[85]} & DeepCache & LSTM \\ \cline{3-4}  & & [86] & Caching scheme based on content popularity and \\ \cline{3-4}  & & [86] & user location & Optimization \\ \cline{3-4}  & & [87] & Delay-aware cache update policy & Dueling DQN \\ \cline{3-4}  & & \multirow{8}{*}{[74]} & Efficient route planning for automated vehicles \\  & & & through big data acquisition and analysis & Game theory \\ \cline{3-4}  & & & architecture in ICN & \\ \cline{3-4}  & & \multirow{8}{*}{[76]} & Three-layer offloading framework in intelligent \\  & & IoV to minimize the overall energy consumption & deep reinforcement learning \\ \cline{3-4}  & & & while satisfying users’ delay constraints & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of AI techniques in different aspects of ICN and edge computing.  
market potential of this technology will attract many vehicle manufacturers and IT vendors in the future. Thus, a common open standard should be set such that IoV stakeholders can enjoy seamless and smooth services across heterogeneous intelligent connected platforms anytime and anywhere.