Construction and evaluation of a Wi-Fi RSSI indoor positioning system in a waste incineration facility

REON Sato; YUTARO Atarashi; KENICHI Nakano; YOSHINORI Fujimoto; SHINJI Motoyama; SACHIKO Shigemasa

doi:10.13205/j.hjgc.202510004

Volume 43 Issue 10

Oct. 2025

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Article Navigation > ENVIRONMENTAL ENGINEERING > 2025 > 43(10): 30-35

REON Sato, YUTARO Atarashi, KENICHI Nakano, YOSHINORI Fujimoto, SHINJI Motoyama, SACHIKO Shigemasa. Construction and evaluation of a Wi-Fi RSSI indoor positioning system in a waste incineration facility[J]. ENVIRONMENTAL ENGINEERING , 2025, 43(10): 30-35. doi: 10.13205/j.hjgc.202510004

Citation:

REON Sato, YUTARO Atarashi, KENICHI Nakano, YOSHINORI Fujimoto, SHINJI Motoyama, SACHIKO Shigemasa. Construction and evaluation of a Wi-Fi RSSI indoor positioning system in a waste incineration facility[J]. ENVIRONMENTAL ENGINEERING , 2025, 43(10): 30-35. doi: 10.13205/j.hjgc.202510004

Citation:

REON Sato, YUTARO Atarashi, KENICHI Nakano, YOSHINORI Fujimoto, SHINJI Motoyama, SACHIKO Shigemasa. Construction and evaluation of a Wi-Fi RSSI indoor positioning system in a waste incineration facility[J]. ENVIRONMENTAL ENGINEERING , 2025, 43(10): 30-35. doi: 10.13205/j.hjgc.202510004

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Construction and evaluation of a Wi-Fi RSSI indoor positioning system in a waste incineration facility

doi: 10.13205/j.hjgc.202510004

1. Kanadevia Corporation, Osaka 559-8559, Japan

Received Date: 2025-09-04
Accepted Date: 2025-10-10
Rev Recd Date: 2025-09-15

Available Online: 2025-12-03

Publish Date: 2025-10-01

Abstract

Abstract

To enhance worker safety management in waste incineration facilities， we examined indoor positioning methods to track worker locations. We focused on fingerprint （FP） using Wi-Fi， which allows us to utilize existing access points and thus reduce infrastructure deployment costs. We developed a system to track worker positions in an operational waste incineration facility. For system construction， we employed a high-speed fingerprint collection method developed by us to create a data map of the facility. By using a machine learning model， we built a positioning model with an average error of 2.78 meters. Applying the constructed positioning model to an actual waste incineration facility for real-time location display verification， we confirmed that it could provide position displays with an average error of approximately 4 meters at a frequency of once every 30 seconds. This achieved real-time location display of workers with a practical level of accuracy.
- waste incineration facility,
- safety management,
- indoor positioning,
- fingerprinting,
- Wi-Fi FP,
- machine learning

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References(13)

References

[1]	ANJASMARA I M，PRATOMO D G，RISTANTO W，et al. Accuracy analysis of GNSS（GPS，GLONASS，and Beidou）observation for positioning［C］// E3S Web of Conferences，EDP Sciences，2019:01019.
[2]	NDZUKULA S，RAMOTSOELA T，SILVA B，et al. A Bluetooth low energy based system for personnel tracking［C］// IECON 2017-43rd Annual Conference of the IEEE Industrial Electronics Society，IEEE，2017:8435-8440.
[3]	SAKAMOTO Y，EBINUMA T，FUJII K. Doppler positioning with a movable receiver antenna for high-accuracy IMES localization［J］. SICE Journal of Control，Measurement，and System Integration，2012，5（5）:259-267.
[4]	LI Z，LIU J，WANG Z，et al. A novel fingerprinting method of WiFi indoor positioning based on Weibull Signal Model［C］// China Satellite Navigation Conference（CSNC）2018 Proceedings，Springer，Singapore，2018.
[5]	TAO Y，ZHAO L. AIPS:An accurate indoor positioning system with fingerprint map adaptation［J］. IEEE Internet Things J，2022，9（4）:3062-3073.
[6]	YANG J，WANG Y，CHENG W，et al. A fingerprint dictionary processing approach in indoor localization system based on Wi-Fi［J］. Sci Rep，2024（14）:24944.
[7]	https://www.city.otsu.lg.jp/soshiki/030/1708/g/gaiyo/21046.html
[8]	TAKUYA M，ISMAIL A，YUTARO A，et al. Developing dense wireless signal and magnetic field mapping tool［C］// 2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events（PerCom Workshops），Biarritz，France，2024:370-372.
[9]	BAILEY T，DURRANT-WHYTE H. Simultaneous localization and mapping（SLAM）:Part II［J］. IEEE Robotics & Automation Magazine，2006（13）:108-117.
[10]	LABBÉ M，MICHAUD F. RTAB-Map as an open-source lidar and visual simultaneous localization and mapping library for large-scale and long-term online operation［J］. J Field Robot，2018（36）:416-446.
[11]	VINCENT P，LAROCHELLE H，LAJOIE I，et al.. Stacked denoising autoencoders:Learning useful representations in a deep network with a local denoising criterion［J］. Journal of Machine Learning Research，2010，11（12）:3371– 3408.
[12]	YOUSSEF M，AGRAWALA A. Small-scale compensation for WLAN location determination systems［C］// Proc IEEE WCNC，2003:1974-1978.
[13]	ABBAS M，ELHAMSHARY M，RIZK H，et al. WiDeep:WiFi-based accurate and robust indoor localization system using deep learning［C］// IEEE PerCom，2019.