## 2 Related Work  
In this section, the existing routing protocols for MCS-based urban sensing are presented and compared with the proposed routing protocol. In early work, the flooding technique was used in a number of routing protocols [9; 10]. Messages are spread throughout the network as widely as possible under those protocols. There is a high \(DC\) because nodes continually replicate messages for newly found contacts that have not yet processed a copy of the messages. To reduce the \(DC\), several studies [11; 18] restricted the number of times a message was replicated. However, a method to quickly spread copies of packets throughout the networks was not considered in those protocols. To overcome this issue in our routing protocol, a forwarding token is used and updated based on degree centrality. The node with a greater degree of centrality will be assigned a larger value for the forwarding token. That helps the rapid spread of packets into the network and minimizes \(PDL\). Moreover, those other routing protocols did not take into account the importance of selecting optimal relay nodes. As a result, messages could be forwarded to nodes that rarely interact with the destination. That results in a low \(PDR\) and high latency. To address this issue, the HLPRP executes relay selection in order to choose the optimal nodes for packet relay. Numerous studies have been proposed for relay selection [19; 20; 21; 22; 23; 24; 25; 26]. For example, in [19; 20], the physical locations of nodes were used for relay selection. Specifically, the distances between nodes were used to determine which node should be chosen as a forwarding node. It is preferable to choose a node that is closer to the destination. The work in [21] tried to improve the spray-and-wait routing protocol [11] by taking into account node performance. Specifically, a node with a higher capability receives more copies of messages during the spray phase. Then, in the wait phase, it not only delivers packets to their destinations but also forwards them to higher capability nodes. However, the metrics considered in those routing protocols (e.g., node capability and distance between nodes) do not accurately represent the chance of a node encountering the destination in the future. As a result, the probability of sending messages to the destination is still low. Unlike those studies, our work proposes a human location prediction model based on a RNN to estimate whether a node will meet or not meet the destination in the future. Then, optimal relay nodes can be selected. To improve the relay selection method, a number of routing protocols have been proposed based on social communities and the properties of nodes. For instance, in [25], nodes are divided into communities, and packets were only forwarded to nodes belonging to the destination's community. In the BUBBLE Rap routing protocol [26], network communities were determined by \(K\)-clique algorithm. The local ranking of a node refers to the node's betweenness centrality in its community, whereas the global ranking refers to the node's betweenness centrality with all other nodes in the network. A message is routed to nodes that have a higher ranking. Until a node in the destination's community is found, global ranking is used. After that, local ranking is used. However, forming communities is challenging in those routing protocols since a node must have all the information on all other nodes. In [22; 23; 24], relay selection was based on centrality metrics. Specifically, packets were routed to nodes with a greater centrality value. Nevertheless, forwarding a large number of messages to central nodes results in congested traffic and long delays around those nodes. To address these problems, the HLPRP chooses nodes with a high probability of meeting the destination and a close relationship with the destination, rather than choosing a node with a high probability of interacting with all other nodes in the network. In other words, the HLPRP selects specific relays for each destination.