# Analysis of Bluetooth RSSI for Proximity Detection of Ship Passengers
## 4 Impact of Quantization of Bluetooth RSSI Values  
### Statistical Analysis  
Due to the limited space of the ship, most of the time, passengers are in the sitting pose instead of the standing pose. Therefore, it is necessary to study the influence on proximity detection in the case of two different poses, sitting and standing, in order to provide a basis for future research related to the theoretical method of distance identification. Experimental data are obtained from the PACT consortium. RSSI values are reported by smartphones. These quantization bins are represented by all possible values reported by the RSSI of each smartphone [20]. The larger the quantization of RSS, the better RSS represents a Bluetooth signal. Bluetooth with more quantization steps should provide a better method for proximity detection [23]. If only the integer number is used for proximity detection, the chance of any two locations having exact locations would increase and degrade the performance of the proximity detection technique [24]. Typically, most of the research studies involving indoor positioning systems calculate the average values of RSSI and record them as real numbers [25]. This reduces the problem of the quantization effect in development [26]. A comparison of RSSI from the different  
Figure 3: An illustration of how developing technologies can be used to find close contacts [21]. distances and poses is shown in Table 1. The quantization impact can also be mitigated by using the mode and median. Table 1 shows how RSSI values of the mean, median, and mode change over time for the same distance. Each distance and pose collected 3240 RSSI samples over three minutes (18 samples/second) in order to calculate the summary statistics in the table. The means are different by about 0.6 dBm to 10.9 dBm in various poses at the same distance. Range, standard deviation, and skewness are also diverse. A particular point of interest is that the mean of RSSI (Sit and Stand) is the most similar at 15 ft. RSSI will rapidly deteriorate as the distance between two points increases. At 8 ft, the RSSI value becomes more vital in the sitting pose. Mean and median readings, \(-67.5\) dBm and \(-67\) dBm, are nearly identical. The mode value, however, is \(-61\) dBm. The mode value at 6 ft and the mode value at 8 ft is 7 dBm. The mean and median readings are only 2.5 and 1 dBm, respectively. If proximity is calculated by using mode as a representative value of RSSI, the result may be around 4 ft; however, if proximity is calculated using mean and median, the result will be between 5 ft and 6 ft. To put it another way, using mode as an RSSI proxy for a sitting pose at an 8 ft will result in more significant inaccuracy. Mean and median values in the standing pose show a declining tendency with increasing distance. The 4 ft to 6 ft mode values are more significant than the 3 ft mode values. This contradicts the RSSI rule, which states that RSSI decreases with distance. The mode value at 4 ft distance is 7 dBm higher than at 3 ft, resulting in a substantial mistake in distance computation. Finally, we find that mean and median RSSI values are recommended for calculating proximity detection, whereas the mode value is not. Standard deviation is a measure of a set of value variability or dispersion. A low standard deviation shows that these values tend to be close to the average value of the group, whereas a high standard deviation indicates that these values are spread across a broader range. As shown in Table 1, standard deviation is the lowest at 15 ft and largest at 6 ft for the sitting pose. However, the most significant distance error occurs at 8 ft. In standing poses, the median values are the same for 5 ft and 6 ft and 8 ft and 10 ft. That is to