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Article

An Efficient Indoor Positioning Method Based on Wi-Fi RSS Fingerprint and Classification Algorithm

1
Department of Information and Communication Engineering, Wonkwang University, Iksan 570-749, Korea
2
Samsung Electronics, Suwon 497-001, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Guenther Retscher
Sensors 2021, 21(10), 3418; https://doi.org/10.3390/s21103418
Received: 25 March 2021 / Revised: 22 April 2021 / Accepted: 13 May 2021 / Published: 14 May 2021
(This article belongs to the Special Issue Challenges in Energy Perspective on Mobile Sensor Networks)
Wi-Fi received signal strength (RSS) fingerprint-based indoor positioning has been widely used because of its low cost and universality advantages. However, the Wi-Fi RSS is greatly affected by multipath interference in indoor environments, which can cause significant errors in RSS observations. Many methods have been proposed to overcome this issue, including the average method and the error handling method, but these existing methods do not consider the ever-changing dynamics of RSS in indoor environments. In addition, traditional RSS-based clustering algorithms have been proposed in the literature, but they make clusters without considering the nonlinear similarity between reference points (RPs) and the signal distribution in ever-changing indoor environments. Therefore, to improve the positioning accuracy, this paper presents an improved RSS measurement technique (IRSSMT) to minimize the error of RSS observation by using the number of selected RSS and its median values, and the strongest access point (SAP) information-based clustering technique, which groups the RPs using their SAP similarity. The performance of this proposed method is tested by experiments conducted in two different experimental environments. The results reveal that our proposed method can greatly outperform the existing algorithms and improve the positioning accuracy by 89.06% and 67.48%, respectively. View Full-Text
Keywords: indoor positioning; received signal strength (RSS); Wi-Fi fingerprint; SAP similarity; fingerprint clustering; RSS extraction indoor positioning; received signal strength (RSS); Wi-Fi fingerprint; SAP similarity; fingerprint clustering; RSS extraction
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MDPI and ACS Style

Ezhumalai, B.; Song, M.; Park, K. An Efficient Indoor Positioning Method Based on Wi-Fi RSS Fingerprint and Classification Algorithm. Sensors 2021, 21, 3418. https://doi.org/10.3390/s21103418

AMA Style

Ezhumalai B, Song M, Park K. An Efficient Indoor Positioning Method Based on Wi-Fi RSS Fingerprint and Classification Algorithm. Sensors. 2021; 21(10):3418. https://doi.org/10.3390/s21103418

Chicago/Turabian Style

Ezhumalai, Balaji, Moonbae Song, and Kwangjin Park. 2021. "An Efficient Indoor Positioning Method Based on Wi-Fi RSS Fingerprint and Classification Algorithm" Sensors 21, no. 10: 3418. https://doi.org/10.3390/s21103418

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