Properties of indoor received signal strength for WLAN location fingerprinting
Abstract
Indoor positioning systems that make use of received signal strength based location fingerprints and existing wireless local area network infrastructure have recently been the focus for supporting location-based services in indoor and campus areas. A knowledge and understanding of the properties of the location fingerprint can assist in improving design of algorithms and deployment of position location systems. However, most existing research work ignores the radio signal properties. This paper investigates the properties of the received signal strength reported by IEEE 802.11b wireless network interface cards. Analyses of the data are performed to understand the underlying features of location fingerprints. The performance of an indoor positioning system in terms of its precision is compared using measured data and a Gaussian model to see how closely a Gaussian model may fit the measured data.
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Indoor positioning in public spaces on transport networks is challenging because radio signals are subject to reflection, diffraction, scattering, attenuation by people, and ambient humidity.
“Indoor positioning in public spaces on the transport network is a challenge for several reasons: during propagation, the radio signal is subject to reflection, diffraction and scattering due to the architectural elements that exist in that space. To these effects we also have to add signal attenuation due to the presence of people, which may be a mass presence of people in public spaces such as”
From Study on an Indoor Positioning System for Harsh Environments Based on Wi-Fi and Bluetooth Low Energy (2017) · cites this paper as [8]
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Au, A. W. S., Feng, C., Valaee, S., Reyes, S., Sorour, S., Markowitz, S. N., Gold, D., Gordon, K., & Eizenman, M. (2013) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2012.175 · Cited by 183 · see citing works
Unobtrusive assessment of activity patterns associated with mild cognitive impairment
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Study on an Indoor Positioning System for Harsh Environments Based on Wi-Fi and Bluetooth Low Energy
De Blasio, G., Quesada-Arencibia, A., García, C., Molina-Gil, J., & Caballero-Gil, C. (2017) · Sensors · doi:10.3390/s17061299 · Cited by 59 · see citing works
SWiBluX: Multi-Sensor Deep Learning Fingerprint for Precise Real-Time Indoor Tracking
Belmonte-Hernandez, A., Hernandez-Penaloza, G., Martin Gutierrez, D., & Alvarez, F. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2019.2892590 · Cited by 53 · see citing works
- Authors
- Kaemarungsi, K., & Krishnamurthy, P.
- Affiliations
- University of Pittsburgh (US)
- Venue
- The First Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services, 2004. MOBIQUITOUS 2004.
- Publisher
- IEEE
- Published
- 2004-11-08
- Pages
- 14-23
- OpenAlex
- W2118099695
- Cited by
- 498
- References
- 0
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 7 · 2023: 2 · 2022: 10 · 2021: 25 · 2020: 22 · 2019: 37 · 2018: 34 · 2017: 34 · 2016: 35 · 2015: 44 · 2014: 43 · 2013: 35 · 2012: 39
- FWCI
- 8.68
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous) (publishedVersion)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Indoor and Outdoor Localization Technologies
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Optical Wireless Communication Technologies
- Physical Sciences › Computer Science › Computer Networks and Communications › Wireless Networks and Protocols
- Keywords
- received signal strength, location fingerprinting, indoor positioning, wireless LAN, IEEE 802.11b, wireless local area networks, Gaussian model, positioning accuracy, location-based services
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