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Sensors 2017, 17(4), 812; doi:10.3390/s17040812

Fusing Bluetooth Beacon Data with Wi-Fi Radiomaps for Improved Indoor Localization

1
Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven 5600 MB, The Netherlands
2
Faculty of Pure and Applied Sciences, Open University of Cyprus, Nicosia 2252, Cyprus
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Giancarlo Fortino
Received: 27 February 2017 / Revised: 4 April 2017 / Accepted: 5 April 2017 / Published: 10 April 2017
(This article belongs to the Special Issue Advances in Body Sensor Networks: Sensors, Systems, and Applications)
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Abstract

Indoor user localization and tracking are instrumental to a broad range of services and applications in the Internet of Things (IoT) and particularly in Body Sensor Networks (BSN) and Ambient Assisted Living (AAL) scenarios. Due to the widespread availability of IEEE 802.11, many localization platforms have been proposed, based on the Wi-Fi Received Signal Strength (RSS) indicator, using algorithms such as K-Nearest Neighbour (KNN), Maximum A Posteriori (MAP) and Minimum Mean Square Error (MMSE). In this paper, we introduce a hybrid method that combines the simplicity (and low cost) of Bluetooth Low Energy (BLE) and the popular 802.11 infrastructure, to improve the accuracy of indoor localization platforms. Building on KNN, we propose a new positioning algorithm (dubbed i-KNN) which is able to filter the initial fingerprint dataset (i.e., the radiomap), after considering the proximity of RSS fingerprints with respect to the BLE devices. In this way, i-KNN provides an optimised small subset of possible user locations, based on which it finally estimates the user position. The proposed methodology achieves fast positioning estimation due to the utilization of a fragment of the initial fingerprint dataset, while at the same time improves positioning accuracy by minimizing any calculation errors. View Full-Text
Keywords: indoor positioning; indoor localization; fingerprint; bluetooth low energy (BLE); Internet of Things (IoT); Body Sensor Networks (BSN); positioning algorithms indoor positioning; indoor localization; fingerprint; bluetooth low energy (BLE); Internet of Things (IoT); Body Sensor Networks (BSN); positioning algorithms
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Kanaris, L.; Kokkinis, A.; Liotta, A.; Stavrou, S. Fusing Bluetooth Beacon Data with Wi-Fi Radiomaps for Improved Indoor Localization. Sensors 2017, 17, 812.

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