# Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues
## 3 Networking Technologies toward Collaborative Intelligence
### Radio Access Technology Selection in IoV
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Publication** & **Research Summary** & **Communication Technology** & **Scenarios** \\ \hline
[39] & A network selection approach that The dynamic & & \\ Q-learning algorithm was used to verify the & & DSRC, 4G-LTE and 5G & \\ necessity of handover, and the fuzzy CNN was used & mmWave & V2V \\  & to select the network. & & \\ \hline
[40] & The communication performance of DSRC and LTE & & \\ in three typical IoV applications is tested in a real & DSRC, 4G-LTE & V2V, V2I \\  & road environment. & & \\ \hline
[41] & An Integrated Approach of 4G LTE and DSRC for & & \\ IoV by Using a Novel Cluster-Based Efficient Radio & DSRC, 4G-LTE & V2V \\  & Interface Selection Algorithm. & & \\ \hline
[42] & A heterogeneous network architecture incorporating & & \\ multiple wireless interfaces and a Best Interface & & WAVE, Wi-Fi, 4G-LTE & V2I \\  & Selection algorithm are proposed. & & \\ \hline
[43] & A conceptual multi-RAT OBU architecture for & & \\ personal mobility vehicles such as shared bikes, & & \\ segways or electric scooters and its RAT selection & & LPWAN, Wi-Fi, Cellular & V2I, V2N \\  & approach are discussed. & & \\ \hline
[44] & A vertical handover architecture inside the vehicle is & & \\ implemented based on the logical interface in & & IEEE 802.11P, cellular, Wi-Fi & V2X \\  & TCP/IP mode. & & \\ \hline
[45] & A Dynamic Radio Access Selection and Slice & & \\ Allocation algorithm for 5G and above & & \\ heterogeneous networks is proposed. & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Recentstudies on RAT selection in IoV. IoV to support a wide range of emergency services. This architecture also used the Best Interface Selection algorithm to ensure reliable communication by using the best wireless interfaces to provide suitable connectivity required for successfully forwarding data in vehicular networks in order to avoid single point failure. The results demonstrated the method's suitability in IoV. Vehicle classification based on application needs is advised in future research in order to avoid access control issues with increasing vehicles and demand. [PERSON] et al. [43] considers the incorporation of new environmentally friendly mobile devices, such as motorcycles or bicycles, into collaborative intelligent transportation systems, and a variety of communication techniques--for instance, vehicular Wi-Fi, low-power WAN, and cellular networks--are already available. These communication methods, however, are not fully covered and do not meet the demands of energy consumption and quality of service (QoS). As a result, they propose a decision support frame by applying supervised ML classification to select the best transmission interface in the multi-radio access technology (RAT) device to send a specific message. Various ML algorithms are investigated while considering the computational and energy usage of IoV terminal devices and traffic types. Their method is tested using a decision tree-based microcontroller unit decision support system, and it demonstrates that it can save energy while performing communication tasks and meeting the QoS requirements of some urgent messages. Vertical handover refers to switching among two distinct networking techniques, while horizontal handover refers to handover among distinct cells of a single network technology. [PERSON] et al. [44] studies a mobile internal vertical handover mechanism in a Heterogeneous Vehicle network (HVN) with IEEE 802.11P, cellular network, and Wi-Fi.They suggest a vertical handover management strategy that cuts down on setup time to speed up binding updates. By simultaneously connecting to several available networks utilizing the PMIP-HD architecture, this method enhances handover outcomes by predicting the subsequent handover. It uses a proposed logical interface to profit from the connectivity session. The performance of the proposed approach is verified through stimulations of packet delivery rate, packet error rate (PER), handover connection duration and latency, jitter, and throughput.