Enhanced fingerprinting and trajectory prediction for IoT localization in smart buildings
Abstract
Location service is one of the primary services in smart automated systems of Internet of Things (IoT). For various location-based services, accurate localization has become a key issue. Recently, research on IoT localization systems for smart buildings has been attracting increasing attention. In this paper, we propose a novel localization approach that utilizes the neighbor relative received signal strength to build the fingerprint database and adopts a Markov-chain prediction model to assist positioning. The approach is called the novel localization method (LNM) in short. In the proposed LNM scheme, the history data of the pedestrian's locations are analyzed to further lower the unpredictable signal fluctuations in a smart building environment, meanwhile enabling calibration-free positioning for various devices. The performance evaluation conducted in a realistic environment shows that the presented method demonstrates superior localization performance compared with well-known existing schemes, especially when the problems of device heterogeneity and WiFi signals fluctuation exist.
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Indoor localization is challenging due to radio propagation environments such as shadowing, multipath effect, delay distortion and fading.
“Indoor localization is challenging due to radio propagation environments such as shadowing, multipath effect, delay distortion and fading [67].”
From Embedded Intelligence on FPGA: Survey, Applications and Challenges (2021) · cites this paper as [67]
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- Authors
- Lin, K., Chen, M., Deng, J., Hassan, M. M., & Fortino, G.
- Affiliations
- Dalian University of Technology (CN)
- Huazhong University of Science and Technology (CN)
- University of North Carolina at Greensboro (US)
- King Saud University (SA)
- University of Calabria (IT)
- Venue
- IEEE Transactions on Automation Science and Engineering
- Published
- 2016-07
- Volume
- 13
- Issue
- 3
- Pages
- 1294-1307
- ISSN
- 1545-5955 (ISSN-L)
- 1558-3783
- OpenAlex
- W2313528934
- Cited by
- 246
- References
- 35
- Citations by year
- 2026: 6 · 2025: 10 · 2024: 13 · 2023: 24 · 2022: 20 · 2021: 32 · 2020: 27 · 2019: 43 · 2018: 31 · 2017: 27 · 2016: 13
- FWCI
- 17.50
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Automation Science and Engineering (publishedVersion)
- Archive of Doctoral Theses and Digital Collections (University of Calabria) (submittedVersion)
- Funders
- Deanship of Scientific Research from King Saud University, Riyadh, Saudi Arabia, through the International Research Group (IRG14-17)
- National Natural Science Foundation of China (61103234)
- China Scholarship Council
- 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 › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Keywords
- smart buildings, fingerprinting, received signal strength, Markov chain prediction, trajectory prediction, heterogeneous devices, indoor positioning
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