## 2 Related Work
Based on that, traffic congestion and delays can be avoided. Based on nodes' encounter histories, a number of routing protocols were proposed [12; 13; 27; 28; 29; 30]. In [13; 27], delivery predictability was proposed based on encounter history. This value indicates how likely a node will be able to send a message to the destination. If a node encounters another node with a higher value for delivery predictability to the destination, the node replicates the message. Under [12; 28; 29; 30], delivery predictability was also used for relay selection. Furthermore, those protocols control and limit the number of replications to reduce the \(DC\). The community-based opportunistic routing protocol (CORP) [14] considers both delivery predictability and network communities. First, network communities are discovered, and then a communication probability value between two communities is defined. In particular, if nodes in one community often encounter nodes from another community, the two communities have a high community probability with each other. Then, a node with high delivery predictability is preferred as a relay node if the destination community and the source community are the same; otherwise, it chooses nodes in the community of the destination and nodes in communities with a greater community probability with the destination community. These routing protocols' metrics (e.g., delivery predictability and community probability) could represent how frequently two nodes have interacted in the recent past. However, they cannot estimate when the two nodes will encounter each other in the future. Two nodes may interact frequently in the present, but their next contact may happen in the distant future. For example, two students frequently meet each other in the morning when they attend the same class but do not meet again until the next morning. Therefore, the delivery predictability and community probability might be high at present, but the two nodes might not meet in the near future. As a result, optimal relay nodes might not be selected, and the routing protocols are unable to achieve high performance. In [15], a day was divided into time slots, and then, based on nodes' encounter histories, the meeting probability between nodes was estimated for each time slot. If two nodes have met frequently in a time slot in the past, they will have a high probability of meeting again in that time slot in the future. The estimated value of the meeting probability between two nodes in a time slot only depends on the statistics of their previous encounters in that time slot. Nevertheless, in the real world, the meeting probability in a time slot can be influenced by encountering information from other time slots during the day. Therefore, using statistics on node encounters may not be the best method for estimating meeting probability. As a result, high network performance might not be obtained when using only meeting probability for relay selection. Our proposed routing protocol can overcome this problem by developing and using human location prediction-based relay selection based on a RNN and LSTM cells. The prediction model takes the information of mobile users in current and previous time slots (e.g., mobile users' identity, mobile users' positions, the time slot of the day, the day of the week) as input, and outputs the location of mobile nodes for the next several time slots. Based on the predicted information, we can estimate whether a node will meet or not meet the destination in the future. Then, optimal relay nodes can be selected. Our routing protocol achieves better network performance with a low \(DC\), a low \(PDL\), and a high \(PDR\).