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Future Internet
Volume 17
Issue 10
10.3390/fi17100440
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Enhanced Position Estimation via RSSI Offset Correction in BLE Fingerprinting-Based Indoor Positioning †

by
Jingshi Qian
1,
Nobuyoshi Komuro
2,*,
Won-Suk Kim
3 and
Younghwan Yoo
3
1
Graduate School of Science and Engineering, Chiba University, 1-33, Yayoi-cho, Inage-ku, Chiba 263-8522, Japan
2
Chiba University Digital Transformation Enhancement Council, Chiba University, 1-33, Yayoi-cho, Inage-ku, Chiba 263-8522, Japan
3
Division of Computer Science and Engineering, Pusan National University, 2 Busandaehak-ro 63 beon-gil, Geumjeong-gu, Busan 43241, Republic of Korea
*
Author to whom correspondence should be addressed.
†
This paper is an extended version of our paper published in IEEE GCCE 2023, Nara, Japan, 10–13 October 2023
Future Internet 2025, 17(10), 440; https://doi.org/10.3390/fi17100440
Submission received: 31 July 2025 / Revised: 10 September 2025 / Accepted: 23 September 2025 / Published: 26 September 2025
(This article belongs to the Special Issue Wireless Sensor Networks and Internet of Things)
Versions Notes

Abstract

Since GPS (Global Positioning System) cannot meet accuracy requirements indoors, indoor Location-Based Services (LBSs) have become increasingly important. BLE (Bluetooth Low Energy) offers cost and accuracy advantages. Typically, the position fingerprinting method is used for indoor positioning. However, due to irregular reflection and absorption, the indoor environment introduces various offsets in Bluetooth RSSI (Received Signal Strength Indicator). This study analyzed the RSSI space and proposed a pre-processing workflow to improve position estimation accuracy by correcting offsets in RSSI space for BLE fingerprinting methods using machine learning. Experiments performed using different position estimation methods showed that the corrected data achieved a 6% improvement over the filter-only result. This study also evaluated the effects of different pre-processing and post-processing filters on positioning accuracy. Experiments were also conducted using a published dataset and showed similar results.
Keywords: indoor positioning; RSSI; Bluetooth low energy; position fingerprinting method indoor positioning; RSSI; Bluetooth low energy; position fingerprinting method

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MDPI and ACS Style

Qian, J.; Komuro, N.; Kim, W.-S.; Yoo, Y. Enhanced Position Estimation via RSSI Offset Correction in BLE Fingerprinting-Based Indoor Positioning. Future Internet 2025, 17, 440. https://doi.org/10.3390/fi17100440

AMA Style

Qian J, Komuro N, Kim W-S, Yoo Y. Enhanced Position Estimation via RSSI Offset Correction in BLE Fingerprinting-Based Indoor Positioning. Future Internet. 2025; 17(10):440. https://doi.org/10.3390/fi17100440

Chicago/Turabian Style

Qian, Jingshi, Nobuyoshi Komuro, Won-Suk Kim, and Younghwan Yoo. 2025. "Enhanced Position Estimation via RSSI Offset Correction in BLE Fingerprinting-Based Indoor Positioning" Future Internet 17, no. 10: 440. https://doi.org/10.3390/fi17100440

APA Style

Qian, J., Komuro, N., Kim, W.-S., & Yoo, Y. (2025). Enhanced Position Estimation via RSSI Offset Correction in BLE Fingerprinting-Based Indoor Positioning. Future Internet, 17(10), 440. https://doi.org/10.3390/fi17100440

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