# Device-Free Crowd Size Estimation Using Wireless Sensing on Subway Platforms
## 3 Experimental Setup  
We demonstrate our solution in a subway station in Antwerp, Belgium. We mounted sensor nodes on both the walls and the ceiling above the edge of the platform, as shown in Figure 1. This allowed us to make separate virtual sensor networks for crowd size estimation depending on the state of the environment. The nodes were separated into three groups: 5 \"platform nodes\" were mounted at waist height on the walls of the platform, 8 \"ceiling nodes\" were mounted on the ceiling near the edge of the platform at a height of 2.7 m, and 7 \"bedding nodes\" were mounted at waist height as well but on the opposite wall across from the platform. The number and placement of the nodes are based on the available hardware and the spatial limitations of the environment (e.g., furniture, timetables, platform width,...). As the optimization of node placement is not within the scope of this work, we aimed to distribute the nodes as evenly as possible throughout the environment. Three nodes disappeared or malfunctioned over the course of the experimental measurement period and were omitted from this work. We combined these three groups of nodes to make three virtual sensor networks, as shown in Figure 2. The nodes and links to be used for each scenario were selected based on training and evaluating each scenario on the training and test sets for all 64 possible combinations with 3 node groups. When no rail vehicle is present, we use all nodes, but we exclude the links between ceiling nodes and bedding nodes, as well as the links in between the different bedding nodes. This results in 20 nodes forming 113 unique links. When a rail vehicle is present in the environment, we only use the ceiling nodes and the platform nodes, containing all links involving any of these nodes. In total, this virtual sensor network contains 13 nodes, resulting in 78 unique links. We empirically determined that the large RSSI variances resulting from the obstruction of the line of sight (LoS) led to a lower correlation between the crowd size and the RSSI values. When excluding the nodes mounted against the bedding wall, we saw an improvement in the correlation and the subsequent estimation accuracy. For the detection of the rail vehicles, we use all nodes, but we exclude the links between the ceiling and the platform wall, as well as the links formed between the platform  
Figure 1: This diagram shows the layout of our experimental environment, a subway station. The sensor nodes are divided into 3 groups that can be combined into different virtual sensor networks depending on the application. Figure 2: This diagram shows the links that are used for the 3 virtual sensor networks, which can be created by combining the platform, ceiling, and bedding node groups. The blue lines are links used by the virtual network, and the dashed blue lines represent the multipath propagation associated with the line-of-sight links. wall nodes and those formed between the bedding wall nodes. This virtual sensor network contains 20 nodes and 119 unique links after link exclusion. This combination of links proved to maximize the correlation between attenuation values and vehicle presence or absence. The communication sequence in the network is displayed in Figure 3. After receiving the initialization message from the gateway (Figure 3a), all nodes will transmit a message consecutively (Figure 3b-d). These messages are received by the other nodes, which measure the RSSI value with which this communication is received. The recorded values will be the data body of the message in the next cycle. This allows the gateway that is receiving the messages to also collect the data and forward them to the back-end for processing. The frequency of this measurement cycle is dictated by the gateway. In our experiment, we used a sample rate of a cycle every 10 s. The used sensor nodes are WizziLab Wolt-XL nodes containing custom firmware. All communication in the network happens using DASH7 messages in the 868 MHz Short-Range Devices (SRD) band.