Evaluating the implications of varying bluetooth low energy (BLE) transmission power levels on wireless indoor localization accuracy and precision
Qureshi, Umair Mujtaba, Umair, Zuneera, and Hancke, Gerhard Petrus (2019) Evaluating the implications of varying bluetooth low energy (BLE) transmission power levels on wireless indoor localization accuracy and precision. Sensors, 19 (15). 3282.
|
PDF (Published Version)
- Published Version
Available under License Creative Commons Attribution. Download (3MB) | Preview |
Abstract
Bluetooth Low Energy (BLE) based Wireless Indoor Localization System (WILS) with high localization accuracy and high localization precision is a key requirement in enabling the Internet of Things (IoT) in today’s applications. In this paper, we investigated the effect of BLE signal variations on indoor localization caused by the change in BLE transmission power levels. This issue is not often discussed as most of the works on localization algorithms use the highest power levels but has important practical implications for energy efficiency, e.g., if a designer would like to trade-off localization performance and node lifetime. To analyze the impact, we used the established trilateration based localization model with two methods i.e., Centroid Approximation (CA) and Minimum Mean Square Error (MMSE). We observed that trilateration based localization with MMSE method outperforms the CA method. We further investigated the use of two filters i.e., Low Pass Filter (LPF) and Kalman Filter (KF) and evaluated their effects in terms of mitigating the random variations from BLE signal. In comparison to non-filter based approach, we observed a great improvement in localization accuracy and localization precision with a filter-based approach. Furthermore, in comparison to LPF based trilateration localization with CA, the performance of a KF based trilateration localization with MMSE is far better. An average of 1 m improvement in localization accuracy and approximately 50% improvement in localization precision is observed by using KF in trilateration based localization model with the MMSE method. In conclusion, with KF in trilateration based localization model with MMSE method effectively eliminates random variations in BLE RSS with multiple transmission power levels and thus results in a BLE based WILS with high accuracy and high precision.
| Item ID: | 89324 |
|---|---|
| Item Type: | Article (Refereed Research - C1) |
| ISSN: | 1424-8220 |
| Keywords: | Bluetooth Low Energy (BLE), Internet of Things (IoT) applications, Kalman Filter, Localization accuracy and precision, Low Pass Filter, Position estimation, Wireless Indoor Localization |
| Copyright Information: | © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
| Date Deposited: | 28 Aug 2026 05:45 |
| FoR Codes: | 40 ENGINEERING > 4006 Communications engineering > 400608 Wireless communication systems and technologies (incl. microwave and millimetrewave) @ 100% |
| SEO Codes: | 22 INFORMATION AND COMMUNICATION SERVICES > 2299 Other information and communication services > 229999 Other information and communication services not elsewhere classified @ 100% |
| More Statistics |
Actions (Repository Staff Only)
 |
Item Control Page |