# Analysis of Bluetooth RSSI for Proximity Detection of Ship Passengers
## 2 Related Works  
Many studies on indoor positioning systems have pointed out the properties of the RSSI describing location fingerprints. In the seminal work of [13], the user's orientation may result in variations in RSSI levels of up to 5 dBm. However, no analysis of RSSI data is provided. Different directions of the user and mobile device concerning the access point may change the average value of RSSI at a location. The authors of [14] also suggest that the orientation should be included in calculating the user's location. The authors of [15] studied indoor positioning systems and considered user orientation by adding a digital compass to the mobile device to improve positioning accuracy. However, the increase from two to eight directions does not significantly improve localization performance. A preliminary study on the use of RSSI for location fingerprinting is reported in [16]. The researcher performs several RSSI measurements influenced by the user, orientation, and fade. The researcher finds that mean RSSI values changed, but RSSI values fluctuated more. This finding emphasizes the need to consider the influence of the user. Since RSSI can be used to calculate distance, it became the primary method for proximity detection, but user behavior significantly impacts RSSI. In other words, the user's pose fluctuates and affects RSSI signal. It is necessary to determine the impact that different user poses will have on the RSSI, which plays a crucial role in improving proximity detection accuracy. Proximity detection can be achieved by indoor positioning techniques, although it can also be reached directly by RSSI values. Applying machine learning algorithms to achieve RSSI proximity detection is a classification problem. The authors of [17] focused on using RSSI data to identify whether two persons are 6 ft apart by using machine learning classification techniques. The authors of [18] studied, using a machine learning approach, making inferences from the data collected by the sensor array to observe whether obtaining classifiers and a regressor on the projected distance between objects and the sensor is possible. The author of [19] uses classical estimation theory and several machine learning techniques to compare the accuracy of proximity distances and accompanying confidence levels. The author of [19] demonstrates that machine learning techniques may improve accuracy from 3.60 to 19.98 percent, bringing them closer to the feasible estimate bound. Therefore, this article uses distance to represent proximity. However, what our paper proposes is that there is a relationship between RSSI intensity and proximity, and proximity is not defined by distance but by RSSI power. This is the most fundamental difference between our paper and this article. However, most of the studies in the literature do not further investigate the actual statistical properties of RSSI samples. Most of the existing studies on indoor localization focus on algorithms but do not analyze RSSI data. Therefore, in this study, we analyze RSSI data statistically from people's sitting and standing poses in order to analyze what kind of influence different poses have on RSSI and to consider this influence for proximity detection in the future to obtain accurate proximity detection. We first describe the MITRE range angle structured (MRAS) PACT dataset. Next, RSSI values are statistically analyzed in order to compare differences in mean, median, mode, standard deviation, and skewness at different distances and compare different poses. In addition, the differences in RSSI distributions at different poses are compared at exact distances. This paper's main contribution is that using the mean value of RSSI brings more minor proximity detection errors than by using the median, mode, minimum, and maximum. Ifonly RSSI dispersion is considered, it is difficult to reduce the error. Moreover, in most cases, RSSI values almost belong to the left-skewed distribution. There is a combination of mean and skewness of RSSI at different poses.