## 2 Related Works
The main idea of the PeopleRank algorithm proposed by reference [21] is that nodes with a higher \"PeopleRank\" value are usually more \"centered\" in the network. The idea is to use this more stable social information to increase available partial contact information, to provide efficient data routing in opportunistic networks. The PeopleRank algorithm evaluates the node's actual movement and social interaction. It is proven that the algorithm can deliver the message with a near optimal success rate (i.e., close to Epidemic Routing), and at the same time reduce the number of message re-transmissions by 50% compared to Epidemic Routing. Reference [22] stated that existing social-aware routing protocols for pocket switched networks use the status information of nodes (for example, the history of past encounters) to infer information about the social structure of the network to optimize routing. SANE (Social-Aware D2D Relay Networks for Stability Enhancement) is the first routing algorithm that combines the advantages of both social-aware and stateless approaches. This algorithm shows the superiority of our proposed social-aware, stateless routing and interest-casting approaches compared with existing routing approaches. Reference [9] proposes an algorithm based on the social characteristics of nodes, such as online social connections, interests, and contact history. The hit ratio and transmission delay of the entire network are not affected. Reference [10] proposes a multi-layer social-network-based routing method, ML-SOR, which measures the forwarding capacity of nodes and selects an effective forwarding node in terms of center of nodes, connection strength and link prediction compared with the nodes encountered. Good routing performance can be achieved at a low cost. dLife, proposed by reference [11], is a routing protocol of DTN(Delay tolerant network), which uses two utility functions of time varying connection duration and node importance to implement forwarding decisions. Experiments show that buffer size and message size can affect the performance of dLife routing. Due to frequent node movement and sparse networks, the networked physical system of vehicles has the characteristics of opportunistic networks, which are seriously affected by intermittent connections. Reference [23] proposes a path-based TDOR(A Trajectory-Driven Opportunistic Routing Protocol) protocol for sparse networks. In this paper, the relay node is selected according to the distance to the track, rather than considering the destination as the standard for next-hop selection. Compared with other geographic routing protocols, this next hop selection strategy provides reliable and efficient messaging with high delivery rates and low transmission overhead. TDOR is more tolerant of network intermittency due to the unbiased movement of intermediate nodes. The results show that TDOR performs better than known opportunistic routing protocols and can achieve lower routing overhead for the same message transmission rate. Reference [24] illustrates that existing geographical routing usually contains hidden assumptions to obtain accurate location information, but this assumption is not true in real life. The accuracy of vehicle location information can determine the efficiency and extensibility of geographic routing. A location error elastic geographic routing (ler-gr) protocol is proposed in reference [24]. The position prediction and correction technology of Kalman Filtering was used to predict the position of adjacent vehicles, and the position error of adjacent vehicles was evaluated by Rayleigh distribution error calculation technology. Simulation results show that the position error resilience of ler-gr is strong in real application environments. With the amount of data that need to be transmitted in the communication process becoming very large, adopting the community-based mode for data forwarding can increase the packet delivery ratio and reduce the routing pressure of single nodes.