Evaluations of Fingerprint-based Indoor Positioning System Using IEEE 802.15.4

Puspitaningayu, Pradini; Funabiki, Nobuo; Hamazaki, Kazushi; Kuribayashi, Minoru; Kao, Wen-Chung

doi:10.1109/icce-tw52618.2021.9603037

Cited by 2 publications

(3 citation statements)

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“…(6) For each receiver, calculate the average LQI using the forwarded data from it after the last average LQI calculation. (7) Make the fingerprint at the server, and store them in the SQLite database.…”

Section: Calibration Phasementioning

confidence: 99%

“…In the detection phase, the server detects the current room of the user by applying steps (1)-( 6) in the procedure for the calibration phase periodically. Then, in step (7), after the vector of the average LQI values from all the receivers are obtained, the Euclidean distance is calculated against every pre-stored fingerprint by Equation (1), and the room whose fingerprint has the smallest distance is appointed as the detected room.…”

Section: Detection Phasementioning

confidence: 99%

“…Previously, we developed a fingerprint-based indoor localization system using the IEEE802.15.4 protocol, called FILS15. 4 for convenience [7,8]. FILS15.4 adopts IEEE802.15.4 devices from Mono Wireless [9], because the transmitter is inexpensive (30US), tiny (13.97 × 13.97 × 2.5 mm, 0.93 g), has long-life, no user software to download, and no user setup; it is suitable to be always be worn by a user during location detection.…”

Section: Introductionmentioning

confidence: 99%

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A Proposal of the Fingerprint Optimization Method for the Fingerprint-Based Indoor Localization System with IEEE 802.15.4 Devices

et al. 2022

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Nowadays, human indoor localization services inside buildings or on underground streets are in strong demand for various location-based services. Since conventional GPS cannot be used, indoor localization systems using wireless technologies have been extensively studied. Previously, we studied a fingerprint-based indoor localization system using IEEE802.15.4 devices, called FILS15.4, to allow use of inexpensive, tiny, and long-life transmitters. However, due to the narrow channel band and the low transmission power, the link quality indicator (LQI) used for fingerprints easily fluctuates by human movements and other uncontrollable factors. To improve the localization accuracy, FILS15.4 restricts the detection granularity to one room in the field, and adopts multiple fingerprints for one room, considering fluctuated signals, where their values must be properly adjusted. In this paper, we present a fingerprint optimization method for finding the proper fingerprint parameters in FILS15.4 by extending the existing one. As the training phase using the measurement LQI, it iteratively changes fingerprint values to maximize the newly defined score function for the room detecting accuracy. Moreover, it automatically increases the number of fingerprints for a room if the accuracy is not sufficient. For evaluations, we applied the proposed method to the measured LQI data using the FILS15.4 testbed system in the no. 2 Engineering Building at Okayama University. The validation results show that it improves the average detection accuracy (at higher than 97%) by automatically increasing the number of fingerprints and optimizing the values.

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Section: Calibration Phasementioning

confidence: 99%

Section: Detection Phasementioning

confidence: 99%