Fusing bluetooth beacon data with Wi-Fi radiomaps for improved indoor localization

Loizos Kanaris, Akis Kokkinis, Antonio Liotta, Stavros Stavrou

Research output: Contribution to journalArticleAcademicpeer-review

52 Citations (Scopus)
99 Downloads (Pure)


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.

Original languageEnglish
Article number812
Issue number4
Publication statusPublished - 10 Apr 2017

Bibliographical note

This article belongs to the Special Issue Advances in Body Sensor Networks: Sensors, Systems, and Applications.


  • Bluetooth low energy (BLE)
  • Body Sensor Networks (BSN)
  • Fingerprint
  • Indoor localization
  • Indoor positioning
  • Internet of Things (IoT)
  • Positioning algorithms


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