Experimental analysis of received signals strength in Bluetooth Low Energy (BLE) and its effect on distance and position estimation. Issue 2 (22nd November 2019)

• Record Type: Journal Article
• Authors: Subhan, Fazli; Khan, Asfandyar; Saleem, Sajid; Ahmed, Shakeel; Imran, Muhammad; Asghar, Zubair; Bangash, Javed Iqbal
• Is Part Of: Transactions on emerging telecommunications technologies. Volume 33:Issue 2(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-11-22
• Subjects: Telecommunication -- Periodicals; 384.05
• DOI: 10.1002/ett.3793
• Languages: English
• ISSNs: 2161-5748

Abstract

Abstract: Received Signal Strength Indicator (RSSI) is the measurement of the power in the radio signal and a parameter for distance‐based measurements. Bluetooth Low Energy (BLE) is an advance implementation for Internet of Things (IoT). BLE beacons‐based indoor positioning systems provide an easy and energy‐efficient, low deployment cost solution for wide variety of applications including smart phones. This paper presents an in‐depth experimental study of BLE RSSI in a dense indoor environment. Due to noise, fluctuations in RSSI occur, which produces distance estimation error, which ultimately affect position estimation accuracy. The main objective of this paper is to know the variations in RSSI and develop a radio propagation model in order to minimize the distance estimation and position estimation error. Based on our real‐time experimental analysis using BLE modules, there is an average 1.32‐m position estimation error in the presence of 10‐dBm variation in RSSI. Moreover, we also observed that environmental specific radio propagation constants greatly affect distance and position estimation accuracy in BLE modules. Abstract : This paper presents an in‐depth experimental analysis of Received Signal Strength Indicator (RSSI) in Bluetooth Low Energy Modules. Based on our experimental analysis, we found 10 dBm variation in RSSI in a typical dense indoor environment. Due to this variation, distance and position estimation error occur. To minimize this error, we used environmental specific radio propagational constants and achieved an average 1.3 meters mean error in the presence of 10 dBm variation using a traditional Multilateration approach.