Enhanced Fingerprinting and Trajectory Prediction for IoT Localization in Smart Buildings
Top Cited Papers
- 8 April 2016
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Automation Science and Engineering
- Vol. 13 (3), 1294-1307
- https://doi.org/10.1109/tase.2016.2543242
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.Keywords
Funding Information
- 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
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