## 5 Evaluation Results
### Simulation Setup  
The data for 13 February 2015, is used to simulate the proposed routing protocol. The remaing 89 days are used to train and test the human location prediction model. The simulation's duration is set to nine hours (i.e., from 9 a.m. to 6 p.m.). The number of edge nodes and the number of sensors are set to 5 and 50, respectively. These nodes are randomly placed at locations of frequently scanned APs. Specifically, from 20 access points (APs) that have the highest number of scanned times by mobile users, five APs are randomly selected as locations to deploy five edge nodes. From 150 APs that have the highest number of scanned times by mobile users, 50 APs are randomly selected as locations to deploy sensors. Using this method, the edge nodes and the sensor nodes are deployed to locations that are frequently visited by mobile nodes. In other words, they are deployed at locations with a high density of mobile users. Note that in order to reflect a more realistic scenario in this work, a scenario in which some of the users have predictable mobility and the rest have unpredictable mobility is considered. Therefore, the 50 most active users were selected from the UB dataset and were considered users with predictable mobility. The human location prediction model is trained and tested using the movement history of those people. Thus, their future locations can be predicted using the model. Moreover, we also select 100 additional mobile users from the UB dataset and assume that they have no movement history. The locations of those nodes are unpredictable. Therefore, their packet delivery predictability is set to zero. However, in the network model, it is also supposed that each node knows its encounter history with other nodes. Therefore, the social strength of those 100 mobile users can still be determined. Finally, the total number of mobile nodes is 150 (i.e., 50 mobile users with movement history and 100 mobile users without movement history).  
The media access control (MAC) layer of Bluetooth 5.0 with a transmission rate of 2 Mbps is used. Bluetooth 5.0 is designed for very low power operation. That reduces power usage and extends battery life for nodes. The UB dataset provides the Wi-Fi scan records of mobile devices. Indoor Wi-Fi APs often have short transmission ranges of tens of meters. There is no physical location information for APs and mobile users in the UBdataset. The real distance between nodes cannot be obtained. Therefore, we assume that a mobile user communicates with another mobile user if they scan and detect at least one common AP. In addition, a mobile user communicates with a sensor or an edge node if the user scans and detects the AP where the sensor or the edge node is placed. Packets are generated with a size of 500 bytes (e.g., the size of sensing data or a text message), and the generation interval is randomly set at between 25 s and 30 s. We consider the network model that allows messages with a long expiration time. Therefore, the time to live (TTL) for packets was set to four hours. Each node has a buffer that can store 150 packets. The first-in-first-out (FIFO) buffer is used. The initial value of forwarding token C is set to 64. In addition, the proposed protocol is also compared with other routing protocols. Common parameters, such as the number of nodes and the MAC layer are the same in all routing protocols. The forwarding token under spray-and-wait is set at the same value in the HLPRP. Under PROPHET, first, the initialization constant of delivery predictability, \(P_{init}\), was set to 0.75. Then, the scaling constant, \(\beta\), and aging constant, \(\gamma\), were set to 0.25 and 0.98, respectively. For CORP, the threshold of minimum probability and the threshold of maximum probability are set to 0.45 and 0.88, respectively. The \(K\) value for \(K\)-clique in BUBBLE Rap is set to three. Table 3 shows the details for the simulation parameters.