A Model-based Approach for Indoor Localization Leveraging Single Mobile Sensor

Yaoxin Duan; Rongbin Hu; Zexing Liu; Yuanyi Zhang; Wendi Nie; Kam-Yiu Lam; Chun Jason Xue; Yongli Song; Guan Gui

doi:10.1109/MSN63567.2024.00111

A Model-based Approach for Indoor Localization Leveraging Single Mobile Sensor

Yaoxin Duan, Rongbin Hu, Zexing Liu, Yuanyi Zhang, Wendi Nie^*, Kam-Yiu Lam, Chun Jason Xue, Yongli Song, Guan Gui

^*Corresponding author for this work

Department of Computer Science

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

Abstract

As widely deployed WiFi sensors in indoor scenarios, e.g., WiFi access points and WiFi monitors, WiFi signalbased indoor localization has attracted increasing attention from research communities in the past decade. Among various WiFibased localization techniques, received signal strength (RSS) fingerprinting based on multiple sensors reveals its superiority and effectiveness in complex indoor environments. Existing multi-sensor-based techniques mainly focus on designing efficient algorithms to improve localization performance. However, the limitations of 1) densely pre-deployed WiFi sensors and, 2) sensitivity to changing sensors are not considered appropriately. In this paper, we propose a novel technique called Single Mobile Sensor (SMS) localization, which leverages a single mobile sensor for indoor localization. The SMS localization technique employs a fingerprinting technique with a custom-designed model, named SMS Fingerprint Matching (SMSFM) model, which is responsible for matching fingerprints constructed by a single mobile sensor to estimate targets' location. Numerous experiments conducted on a practical testbed have revealed that the SMSFM model surpasses conventional models, leading to SMS localization delivering competitive localization accuracy compared to previous multi-sensor-based technologies, despite relying solely on a single sensor.

© 2024 IEEE

Original language	English
Title of host publication	Proceedings - 2024 20th International Conference on Mobility, Sensing and Networking (MSN 2024)
Publisher	IEEE
Number of pages	8
ISBN (Electronic)	979-8-3315-1602-4
DOIs	https://doi.org/10.1109/MSN63567.2024.00111
Publication status	Published - Dec 2024
Event	20th International Conference on Mobility, Sensing and Networking (MSN 2024) - Harbin, China Duration: 20 Dec 2024 → 22 Dec 2024 https://ieee-msn.org/2024/

Conference

Conference	20th International Conference on Mobility, Sensing and Networking (MSN 2024)
Place	China
City	Harbin
Period	20/12/24 → 22/12/24
Internet address	https://ieee-msn.org/2024/

Funding

The work described in this paper was supported in part by the National Key Research and Development Program of China under Grant 2022YFE0101000; the National Science Foundation of China under Grant No. 62102055; the National Science Foundation of China under Grant No. 61902043; the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No. KJQN202100621).

Research Keywords

Indoor Localization
Single Mobile Sensor
Fingerprint Matching Model

Access Output

10.1109/MSN63567.2024.00111

Other Access Options

Check CityUHK Library

Permanent Link

Right click to copy link

Cite this

@inproceedings{63beeb74abee4bf1857ecc9edd642357,

title = "A Model-based Approach for Indoor Localization Leveraging Single Mobile Sensor",

abstract = "As widely deployed WiFi sensors in indoor scenarios, e.g., WiFi access points and WiFi monitors, WiFi signalbased indoor localization has attracted increasing attention from research communities in the past decade. Among various WiFibased localization techniques, received signal strength (RSS) fingerprinting based on multiple sensors reveals its superiority and effectiveness in complex indoor environments. Existing multi-sensor-based techniques mainly focus on designing efficient algorithms to improve localization performance. However, the limitations of 1) densely pre-deployed WiFi sensors and, 2) sensitivity to changing sensors are not considered appropriately. In this paper, we propose a novel technique called Single Mobile Sensor (SMS) localization, which leverages a single mobile sensor for indoor localization. The SMS localization technique employs a fingerprinting technique with a custom-designed model, named SMS Fingerprint Matching (SMSFM) model, which is responsible for matching fingerprints constructed by a single mobile sensor to estimate targets' location. Numerous experiments conducted on a practical testbed have revealed that the SMSFM model surpasses conventional models, leading to SMS localization delivering competitive localization accuracy compared to previous multi-sensor-based technologies, despite relying solely on a single sensor.{\textcopyright} 2024 IEEE",

keywords = "Indoor Localization, Single Mobile Sensor, Fingerprint Matching Model",

author = "Yaoxin Duan and Rongbin Hu and Zexing Liu and Yuanyi Zhang and Wendi Nie and Kam-Yiu Lam and Xue, {Chun Jason} and Yongli Song and Guan Gui",

year = "2024",

month = dec,

doi = "10.1109/MSN63567.2024.00111",

language = "English",

booktitle = "Proceedings - 2024 20th International Conference on Mobility, Sensing and Networking (MSN 2024)",

publisher = "IEEE",

address = "United States",

note = "20th International Conference on Mobility, Sensing and Networking (MSN 2024) ; Conference date: 20-12-2024 Through 22-12-2024",

url = "https://ieee-msn.org/2024/",

}

Duan, Y, Hu, R, Liu, Z, Zhang, Y, Nie, W, Lam, K-Y , Xue, CJ, Song, Y & Gui, G 2024, A Model-based Approach for Indoor Localization Leveraging Single Mobile Sensor. in Proceedings - 2024 20th International Conference on Mobility, Sensing and Networking (MSN 2024). IEEE, 20th International Conference on Mobility, Sensing and Networking (MSN 2024), Harbin, China, 20/12/24. https://doi.org/10.1109/MSN63567.2024.00111

A Model-based Approach for Indoor Localization Leveraging Single Mobile Sensor. / Duan, Yaoxin; Hu, Rongbin; Liu, Zexing et al.
Proceedings - 2024 20th International Conference on Mobility, Sensing and Networking (MSN 2024). IEEE, 2024.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - A Model-based Approach for Indoor Localization Leveraging Single Mobile Sensor

AU - Duan, Yaoxin

AU - Hu, Rongbin

AU - Liu, Zexing

AU - Zhang, Yuanyi

AU - Nie, Wendi

AU - Lam, Kam-Yiu

AU - Xue, Chun Jason

AU - Song, Yongli

AU - Gui, Guan

PY - 2024/12

Y1 - 2024/12

N2 - As widely deployed WiFi sensors in indoor scenarios, e.g., WiFi access points and WiFi monitors, WiFi signalbased indoor localization has attracted increasing attention from research communities in the past decade. Among various WiFibased localization techniques, received signal strength (RSS) fingerprinting based on multiple sensors reveals its superiority and effectiveness in complex indoor environments. Existing multi-sensor-based techniques mainly focus on designing efficient algorithms to improve localization performance. However, the limitations of 1) densely pre-deployed WiFi sensors and, 2) sensitivity to changing sensors are not considered appropriately. In this paper, we propose a novel technique called Single Mobile Sensor (SMS) localization, which leverages a single mobile sensor for indoor localization. The SMS localization technique employs a fingerprinting technique with a custom-designed model, named SMS Fingerprint Matching (SMSFM) model, which is responsible for matching fingerprints constructed by a single mobile sensor to estimate targets' location. Numerous experiments conducted on a practical testbed have revealed that the SMSFM model surpasses conventional models, leading to SMS localization delivering competitive localization accuracy compared to previous multi-sensor-based technologies, despite relying solely on a single sensor.© 2024 IEEE

AB - As widely deployed WiFi sensors in indoor scenarios, e.g., WiFi access points and WiFi monitors, WiFi signalbased indoor localization has attracted increasing attention from research communities in the past decade. Among various WiFibased localization techniques, received signal strength (RSS) fingerprinting based on multiple sensors reveals its superiority and effectiveness in complex indoor environments. Existing multi-sensor-based techniques mainly focus on designing efficient algorithms to improve localization performance. However, the limitations of 1) densely pre-deployed WiFi sensors and, 2) sensitivity to changing sensors are not considered appropriately. In this paper, we propose a novel technique called Single Mobile Sensor (SMS) localization, which leverages a single mobile sensor for indoor localization. The SMS localization technique employs a fingerprinting technique with a custom-designed model, named SMS Fingerprint Matching (SMSFM) model, which is responsible for matching fingerprints constructed by a single mobile sensor to estimate targets' location. Numerous experiments conducted on a practical testbed have revealed that the SMSFM model surpasses conventional models, leading to SMS localization delivering competitive localization accuracy compared to previous multi-sensor-based technologies, despite relying solely on a single sensor.© 2024 IEEE

KW - Indoor Localization

KW - Single Mobile Sensor

KW - Fingerprint Matching Model

UR - https://www.webofscience.com/wos/woscc/full-record/WOS:001540152200100

U2 - 10.1109/MSN63567.2024.00111

DO - 10.1109/MSN63567.2024.00111

M3 - RGC 32 - Refereed conference paper (with host publication)

BT - Proceedings - 2024 20th International Conference on Mobility, Sensing and Networking (MSN 2024)

PB - IEEE

T2 - 20th International Conference on Mobility, Sensing and Networking (MSN 2024)

Y2 - 20 December 2024 through 22 December 2024

ER -

A Model-based Approach for Indoor Localization Leveraging Single Mobile Sensor

Abstract

Conference

Funding

Research Keywords

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

Cite this