Investigating the RSSI-based Distance Classification using Median Confidence Interval in a Multi-Device BLE Environment

Abstract

As the COVID-19 global pandemic occurred in 2020, there has been an increase in interest in using Received Signal Strength Indicator (RSSI) for strict implementations of social distancing. Bluetooth Low Energy (BLE) has been mainly used as an option for it is cost-effective and can be integrated with IoT for a wide range of applications. For a multiple-device BLE environment, the devices were calibrated, and parameters of the distance estimation were estimated using linear regression. For each device, RSSI values were acquired in various directions and distances. A confidence interval of 90% was used to create a prediction range for distance classification using the acquired RSSI. Using the range of median, BLE combinations at various distances and positions yield less than 50% of the data when classifying received RSSI values to distances of 1, 2, 3, and 4m. Irregularities from acquiring the RSSI value for various distances have been observed and can affect the classification of distance with the RSSI values. Further study is needed on other methods for the basis of interval range and minimization of irregularities.