# A Novel Opportunistic Network Routing Method on Campus Based on the Improved Markov Model
## 2 Related Works
When the routing path to the destination node is not successfully established by using the ad hoc network protocol, the virtual source node is selected according to the predictability of the delivery of the destination node by the Prophet protocol, and then the delivery rate is effectively increased at the cost of overhead again. [PERSON] et al. [19] explored a reducted-delivery delay routing (RDR) strategy in disaster relief operations, using a simulated annealing algorithm to optimize the message delivery process in the network and achieve an optimal routing method for efficient message distribution. [PERSON] et al. [20] proposed a fair credit-based routing incentive mechanism (FCIM) that uses incentives to the selfishness problem of nodes, uses some trust mechanisms to avoid nodes from cheating the network and ensures fairness among nodes. A few of the algorithms discussed in this paper include the following:  
* Epidemic [21], which is a flooding-based routing method where a node passes a message copy to every node it encounters. By creating numerous message duplicates, it increases the probability that the message will be delivered when it comes across the destination node. However, a lot of copies use up network resources, such as cache space and node energy. * Prophet [22] is a method that is frequently used to send messages based on predictions. Two nodes exchange vectors of transmission probabilities for recognized destinations when they come into contact. Messages can be sent to nodes that meet regularly by updating the transmission probability between nodes based on how long it has been since their last encounter. Nevertheless, it ignores the location information of the nodes and the number of encounters between them. * RDR, which chooses the next-hop node based on the node's estimated latency, estimated speed variation, the direction of motion, available space in the buffer, and previously sent messages. It provides a constrained amount of replicas, reducing the network resource footprint. With this approach, the amount of network resources used can be drastically decreased, and the size of the cache area has less of an impact. However, messages may not be delivered for a long time, and it requires a longer message survival time. * FCIM, where each relay node is rewarded with some points when the source node sends a message to its target, increases the message delivery rate by motivating selfish nodes to actively participate in message forwarding. Nodes are permitted to engage in some acceptable selfish behaviors under this strategy, such as rejecting messages when the cache is full. However, no more properties are considered, such as the energy consumption of nodes to forward messages. Three types of node misconduct are discussed by [PERSON] et al. [23], and they look into how these types of misconduct may affect nine VDTN routing algorithms. The third category of misconduct of nodes is specifically presented. The node reduces the message TTL by storing the message for a long time in its own memory after it has been received. An incentive and punishment strategy is suggested by [PERSON] et al. [24; 25; 26] to incentivize the selfish cluster nodes to forward messages. An active node can raise its reputation by passing messages and engaging in conversations. Nodes that exhibit selfish behavior repeatedly are punished. The reward and punishment mechanism can effectively enhance the degree of cooperation among nodes and improve the probability of packet delivery. In this paper, some novel studies are made in relation to some of the methods previously mentioned. The innovation points of this paper are specified in Table 1.  
The literature listed above has looked at social relationship analysis, route prediction, and energy conservation, but it has not developed a workable route prediction method based on the regularity of student node mobility from the context of campus opportunity networks. These schemes are not applicable to the case of regular group movement of nodes in campus opportunity networks.