Fusing Bluetooth Beacon Data with Wi-Fi Radiomaps for Improved Indoor LocalizationReportar como inadecuado


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


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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

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





Autor: Loizos Kanaris 1,†,* , Akis Kokkinis 1,†, Antonio Liotta 1,† and Stavros Stavrou 2,†

Fuente: http://mdpi.com/



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