# Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues
## 3 Networking Technologies toward Collaborative Intelligence
### Radio Access Technology Selection in IoV  
As mentioned above, there are many types of communication methods that can be used for IoV, and they cannot be replaced with each other. At the same time, the bandwidth demand of users is also increasing, so wireless networks coexist to provide the best service. Vehicles can use different RATS to communicate with other vehicles, infrastructure, networks, etc. Therefore, it is necessary to select the best communication mode and reduce the waste of resources in the multiple access network coexisting environment to ensure cooperation [37]. The RAT selection method can have two schemes [38]. The centralized approach considers the needs of all users in the network and maximizes the throughput or minimizes the delay to centrally optimize the network task. The distributed approach is one in which mobile users improve their own performance as much as possible and regardless of global collaboration, generally using heuristic methods. RAT selection problem is usually treated as a multiple-attribute decision-making problem, or one which uses AI-based algorithms such as neural networks and Q-learning methods. Table 1 summarizes the recent studies on the RAT selection in IoV system. Figure 5: Taxonomy of IoV communication technologies with communication range. [PERSON] et al. [39] studies the network selection and routing when different communication modes exist at the same time, including DSRC, 4G LTE, and 5G mmWave. In terms of network selection, the Shannon entropy rule is used to dynamically set the signal strength, and dynamic Q learning is applied to decide whether the communication mode has to be switched. At the same time, a total of 32 fuzzy rules are formulated after considering factors such as distance, line of sight, vehicle density, signal strength, and data type, and then a fuzzy convolutional neural network (CNN) is adopted to execute RAT. In the aspect of routing between vehicles, the objective function based on channel and vehicle state is designed, and the jellyfish optimization algorithm is improved to calculate the routing. There are also many studies comparing DSRC, Wi-Fi, and cellular factors. In Ref. [40], the communication performance of DSRC and LTE in several application scenarios of IoV is discussed, and corresponding software and hardware are developed for verification. The results show that 4G LTE is more suitable for non-secure applications with low communication speed requirements such as internet access, traffic data transmission, and file download, while DSRC performs better in secure applications such as traffic signs and collision avoidance. Finally, it is suggested to combine the two methods to handle communication problems in IoV, so as to better support high-quality remote information transmission services and security services. On the premise of studying the advantages and limitations of DSRC and cellular network technology, [PERSON] et al. [41] proposes an integration scheme combining these two potential networking technologies to complete more efficient vehicle communication. In this paper, an algorithm combining DSRC and 4G-LTE with a new data forwarding protocol based on optimal clustering is proposed. This algorithm is used to intelligently monitor and evaluate the level of packet loss rate to reduce the handover delay. In Ref. [42], [PERSON] et al. proposed a heterogeneous structure that fully integrated several wireless interfaces, along with WAVE, LTE, and Wi-Fi, by utilizing a radio approach for network access. Furthermore, this architecture was proposed to meet requirements such as pervasive connectivity in order to make VANETs adaptable and expandable for