title: Fusing Bluetooth Beacon Data with Wi-Fi Radiomaps for Improved Indoor Localization.
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 asK-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.
authors.affiliation: Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven 5600 MB, The Netherlands. l.kanaris@tue.nl.
authors.forename: Loizos
authors.initials: L
authors.lastname: Kanaris
authors.affiliation: Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven 5600 MB, The Netherlands. a.kokkinis@tue.nl.
authors.forename: Akis
authors.initials: A
authors.lastname: Kokkinis
authors.affiliation: Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven 5600 MB, The Netherlands. a.liotta@tue.nl.
authors.forename: Antonio
authors.initials: A
authors.lastname: Liotta
authors.affiliation: Faculty of Pure and Applied Sciences, Open University of Cyprus, Nicosia 2252, Cyprus. stavros.stavrou@ouc.ac.cy.
authors.forename: Stavros
authors.initials: S
authors.lastname: Stavrou
keywords: Body Sensor Networks (BSN); Internet of Things (IoT); bluetooth low energy (BLE); fingerprint; indoor localization; indoor positioning; positioning algorithms