Bluetooth low energy microlocation asset tracking (blemat) in a context-aware fog computing system

Saša Pešić, Miloš Radovanović, Milenko Tošić, Mirjana Ivanović, Ognjen Iković, Dragan Boscovic

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper we present a Bluetooth Low Energy Microlocation Asset Tracking system (BLEMAT) that performs real-time position estimation and asset tracking based on BLE beacons and scanners. It is built on a context-aware fog computing system comprising Internet of Things controllers, sensors and a cloud platform, helped by machine-learning models and techniques. The BLEMAT system offers detecting signal propagation obstacles, performing signal perturbation correction and beacon paths exploration as well as auto discovery and onboarding of fog controller devices. These are the key characteristics of semi-supervised indoor position estimation services. In this paper we have shown there are solid basis that a fog computing system can efficiently carry out semi-supervised machine learning procedures for high-precision indoor position estimation and space modeling without the need for detailed input information (i.e. floor plan, signal propagation map, scanner position). In addition, the fog computing system inherently brings high level of system robustness, integrity, privacy and trust.

Original languageEnglish (US)
Title of host publicationWIMS 2018 - 8th International Conference on Web Intelligence, Mining and Semantics
EditorsCostin Badica, Rajendra Akerkar, Mirjana Ivanovic, Milos Savic, Milos Radovanovic, Sang-Wook Kim, Riccardo Rosati, Yannis Manolopoulos
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450354899
DOIs
StatePublished - Jun 25 2018
Externally publishedYes
Event8th International Conference on Web Intelligence, Mining and Semantics, WIMS 2018 - Novi Sad, Serbia
Duration: Jun 25 2018Jun 27 2018

Other

Other8th International Conference on Web Intelligence, Mining and Semantics, WIMS 2018
CountrySerbia
CityNovi Sad
Period6/25/186/27/18

Keywords

  • Fog computing
  • Indoor positioning
  • Machine learning
  • Space modeling

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

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  • Cite this

    Pešić, S., Radovanović, M., Tošić, M., Ivanović, M., Iković, O., & Boscovic, D. (2018). Bluetooth low energy microlocation asset tracking (blemat) in a context-aware fog computing system. In C. Badica, R. Akerkar, M. Ivanovic, M. Savic, M. Radovanovic, S-W. Kim, R. Rosati, & Y. Manolopoulos (Eds.), WIMS 2018 - 8th International Conference on Web Intelligence, Mining and Semantics Association for Computing Machinery. https://doi.org/10.1145/3227609.3227652