# Analysis of Bluetooth RSSI for Proximity Detection of Ship Passengers
###### Abstract  
Concern about the health of people who traveled onboard was raised during the COVID-19 outbreak on the Diamond Princess cruise ship. The ship's narrow space offers an environment conducive to the virus's spread. Close contact isolation remains one of the most critical current measures to stop the virus's rapid spread. Contacts can be identified efficiently by detecting intelligent devices nearby. The smartphone's Bluetooth RSSI signal is essential data for proximity detection. This paper analyzes Bluetooth RSSI signals available to the public and compares RSSI signals in two distinct poses: standing and sitting. These features can improve accuracy and provide an essential basis for creating algorithms for proximity detection. This allows for improved accuracy in identifying close contacts and can help ships sustainably manage persons onboard in the post-epidemic era. COVID-19; received signal strength indicator; proximity detection; ship passenger Article Analysis of Bluetooth RSSI for Proximity Detection of Ship Passengers  
Significance: 0000-0001-998-0001-9981-0001-99  
mobile sensor data to track close contacts. Bluetooth RSSI is precisely one of the mobile sensor data. The idea of our paper is to use Bluetooth RSSI from smartphones for proximity detection for close contact tracking. Using location information to track close contacts is not the only method. This can also be accomplished by adjusting device distances between cellphones. This technology separation is also known as proximity detection. When faced with the threat of COVID-19, a highly contagious virus, maintaining proximity detection is an effective method to prevent infection. Specifically, the risk of COVID-19 transmission increases when an uninfected person is less than 6 ft away for more than 15 min from an infected person (also known as \"too close for too long\" (TCTL)). If a list of TCTL people could be detected and passively tracked via smartphones, users could be notified if they test positive for COVID-19. Existing radio frequency (RF) location technologies could be used to track the daily movements of infected smartphone users. Owners of neighboring smart devices can then be notified to maintain proximity detection or be tested if they are exposed to infected people. Ubiquitous Bluetooth Low Energy (BLE) attracted significant attention due to its short-range and low energy consumption. The Massachusetts Institute of Technology (MIT) leads the Private Automated Contact Tracing (PACT) consortium [9]. The consortium provides several high-quality BLE Received Signal Strength Indicator (RSSI) datasets [9]. These data are collected in various proximity scenarios. The reliability analysis of RSSI-based BLE ranging is a complex issue. Practical measurement studies and characterization of proximity detection using BLE RSSI have been carried out in various scenarios [10]. Several previous studies have proposed methods to improve RSSI-based proximity detection. In addition to proximity, other researchers have used RSSI to estimate mutual orientation between users [11] and energy consumption of BLE RSSI proximity detection [12]. In most cases, Bluetooth RSSI is used in indoor positioning technology. In comparison to room size, the most critical components impacting RSSI during the development of indoor positioning algorithms are walls, metal materials, and water. The transmitter emits signals to the surrounding environment, although the signs are generally reflected due to walls and metal elements. As a result, misleading signals are generated. Human mobility is required in typical interior placement settings, and the human body is primarily made of water, which has a significant impact on RSSI signals. The preceding describes a typical internal placement scenario. Proximity detection, on the other hand, is not the same as traditional interior positioning scenarios. The transmitter and receiver in a proximity detection application are commonly two cellphones. The transmitter is frequently on a wall in classic indoor localization methods.