# Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues
## 3 Networking Technologies toward Collaborative Intelligence
### Routing Protocol in IoV
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Publication** & **Research Summary** & **Communication Technology** & **Scenarios** \\ \hline
[47] & A survey about geographic routing protocols of three type of VANETs: Delay Tolerant, Non-Delay Tolerant and hybrid type. & Position based & V2V \\ \hline
[48] & An opportunistic and position-based routing protocol with candidate relay node set selection strategy, the priority scheduling scheme and removing the expired links mechanism. & Opportunistic and Position based & V2V \\ \hline
[49] & A multi-hop greedy position-based routing algorithm with fuzzy logic techniques is proposed. & Position-based routing with greedy fuzzy logic & V2V \\ \hline
[50] & A mobility-aware dynamic-clustering-based routing which forms clusters based on Euclidean distance, uses a Mayfly optimization algorithm to select cluster heads and forwards data to RSU is proposed. & Position-based clustering method & V2I \\ \hline
[51] & A routing protocol based on movement prediction is studied using the intention of the driver. & Position based & V2V \\ \hline
[52] & An improved position-based routing protocol with a Kalman filter and an extended Kalman filter is studied. & Position based with Kalman filter & V2V \\ \hline
[53] & A routing protocol considering network connectivity to dynamically clustered vehicles and select gateway nodes is studied. & Connectivity prediction-Based clustering & V2V \\ \hline
[54] & Proposed a vehicle-density-prediction-based routing protocol in which optimal relay nodes are selected on the road grid according to the real-time traffic information. & Grid and vehicle-density-prediction based, neural network & V2V \\ \hline
[55] & A method of actively selecting routing and communication interface with Q-learning is proposed in the case of multi-access vehicular edge computing environment. & Reinforcement-learning based & V2V, V2I \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Recent studies on Routing Protocol in IoV. distribution of vehicles is uneven, and the effect is not ideal because the scope to be covered is too large and the global structure of the network is not fully structured. [PERSON] et al. [54] studies a routing protocol named VDPGrid using vehicle density prediction. The vehicle density prediction model uses CNN and long short-term memory (LSTM) to mine spatial and temporal related characteristics in an urban trajectory dataset. The results of the above prediction model are then combined with link quality and path length to assign a weight to each path. In order to reduce communication overhead and computational complexity, after dividing the map into multiple grids, the optimal routing paths are stored in packets, so that data packets can be forwarded between different grids according to road weights. The established vehicle trajectory prediction model can select strategies based on different relay nodes of vehicle cooperation and allocate optimal paths to transmit data packets. This method has less overhead and avoids the grid with poor communication conditions when considering grid selection, so it has better performance in terms of delivery rate. In the process of path selection, the collaborative decision mechanism is adopted, and the overall performance of the method is good. In Ref. [55], route selection in the edge computing of multi-access vehicles is discussed. The main idea is to learn route selection using the Q-learning method in reinforcement learning, and to actively select routes and communication interfaces to reduce the cost of finding routes after receiving communication requests. A reward rule is designed to learn the route selection method from the feedback of cloud or communication partners and the feedback received by the next hop during the learning process, which is combined with the collaboration of end-edge-cloud and different QoS requirements of delay-sensitive and traffic-intensive applications. Vehicular edge selecting scheme based on fuzzy logic methods is also designed.