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

Kanaris, Loizos; Kokkinis, Akis; Liotta, Antonio; Stavrou, Stavros


Loizos Kanaris

Akis Kokkinis

Antonio Liotta

Stavros Stavrou


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.

Journal Article Type Article
Acceptance Date Apr 5, 2017
Online Publication Date Apr 10, 2017
Publication Date Apr 10, 2017
Deposit Date Aug 23, 2019
Publicly Available Date Aug 23, 2019
Journal Sensors
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 17
Issue 4
Pages 812
Keywords indoor positioning; indoor localization; fingerprint; bluetooth low energy (BLE); Internet of Things (IoT); Body Sensor Networks (BSN); positioning algorithms
Public URL
Publisher URL
Contract Date Aug 23, 2019


Fusing Bluetooth Beacon Data with Wi-Fi Radiomaps for Improved Indoor Localization (1.6 Mb)

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Copyright Statement
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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