# Analysis of Bluetooth RSSI for Proximity Detection of Ship Passengers
## 3 The PACT Proximity Datasets  
The Too Close for Too Long (TC4 TL) challenge, organized by the National Institute of Standards and Technology (NIST) in collaboration with the MIT PACT project, aims to improve proximity detection for Bluetooth Low Energy (BLE)-based contact tracking. The PACT consortium published seven datasets. MRAS dataset is well documented in other datasets. Moreover, it contains measurements at different distances relevant to our study goals of performance evaluation of COVID-19 proximity detection from BLE RSSI measurements. The MRAS dataset also contains different tester pose settings. Environment settings specify the properties of the testing area, such as the room size and the tester's location in the room. Tester settings define how testers use devices and how they hold smartphones in addition to testers' poses. Testers pose either by \"sitting\" or \"standing\" at the marked location. Figure 1 shows BLE RSSI measurement scenarios for short-range operations of up to 15 ft. Eight stationary locations for measurement begin at 3 ft, are increased at intervals and end at 15 ft. The distances are identified relative to a person who holds a smartphone with BLE beacons. RSSI measurement data are collected by another person (a receiver) positioned at the eight labeled distances. These datasets are collected by using three versions of the Range-Angle Collection Protocol [20]: Short, Mid, and Full. The Full protocol consists of 40 datasets with RSSI measurements at eight different distances, as shown in Figure 1, and this study uses these datasets for performance evaluation for various distances. This study did not include Short and Mid versions, which had only two different distances of 3 ft and 8 ft and did not offer adequate diversity in measurement distances. HANNARA is a Korean Maritime and Ocean University student training ship. Figure 2 depicts the layout of the HANNARA ship's student living quarters. The purpose of showing a plan of the HANNARA is that the ship environment is similar to that of a cruise ship, and there are enough persons on board as well. In addition, the ship has many stateroom structures similar to those on a cruise ship. It is possible to demonstrate the natural application environment of proximity detection to the maximum extent. The HANNARA ship is used as an experimental environment for this study in subsequent research. As shown in Figure 2, the vessel is compact, and proximity detections of less  
Figure 1: Eight distances for measurements of the RSSI database (source: PACT website). than 6 ft are more likely in this context. Due to the fact that ship space is restricted, the passengers onboard must engage in a full range of activities. Socialization distances of less than 6 ft and duration of more than 15 min are more frequent in this instance. As a result, one of the most critical aspects of locating close relationships is accurately calculating proximity detection of ship riders. Figure 3 illustrates a practical application case of proximity detection. Firstly, the location information of the person riding the boat is obtained by an indoor positioning algorithm. It is assumed that the trajectory data of the COVID-19 confirmed person is known. The trajectories similar to COVID-19 are found by a clustering algorithm (DBSCAN (Density-based spatial clustering of applications with noise), as an example), and its user ID is obtained in order to classify it as a close contact. The results of proximity detection are combined at this time to finalize close contacts. Proximity detection can also confirm whether the close contacts are in the room. Let us suppose the door lock of a room has a Bluetooth signal transmitter. When the close contact leaves the room, proximity detection is suspended as RSSI strength changes dramatically and fades to nothing. Not obtaining proximity makes it possible to determine whether the close contact has left the isolated room without permission.