Bayesian sensor model for indoor localization in ubiquitous sensor network

Abdelmoula Bekkali, Mitsuji Matsumoto

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

11 Citations (Scopus)

Abstract

Ubiquitous Sensor Networks (USN) technology is one of the essential key for driving the Next Generation Network (NGN) to realize secure and easy access from anyone, any thing, anywhere and anytime. The location information is one of the most important and frequently-used contexts in ubiquitous networking. However, a system can use the changes of location to adapt its behavior, such as computation and communication, without the user intervention. In this paper we introduce a Bayesian sensor framework for solving the location estimation errors problem in Radio Frequency Identification (RFID) environments. In our model the physical properties of the signal propagation are not taken into account directly. Instead, the location estimation is regarded as machine learning problem in which the task is to model how the location estimation error is distributed indoors based on a sample of measurements collected at several known locations and stored in RFID tags. Results obtained by simulations demonstrate the suitability of the proposed model to provide high performance level in terms of accuracy and scalability.

Original languageEnglish
Title of host publicationInternational Telecommunication Union - Proceedings of the 1st ITU-T Kaleidoscope Academic Conference, Innovations in NGN, K-INGN
DOIs
Publication statusPublished - 2008
Event1st ITU-T Kaleidoscope Academic Conference, Innovations in NGN, K-INGN - Geneva
Duration: 2008 May 122008 May 13

Other

Other1st ITU-T Kaleidoscope Academic Conference, Innovations in NGN, K-INGN
CityGeneva
Period08/5/1208/5/13

Fingerprint

Sensor networks
Sensors
radio
Radio frequency identification (RFID)
change of location
Error analysis
Next generation networks
networking
Learning systems
Scalability
Physical properties
simulation
communication
Communication
learning
performance

Keywords

  • Bayesian filtering
  • Indoor location estimation
  • NGN
  • RFID
  • USN

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering
  • Communication

Cite this

Bekkali, A., & Matsumoto, M. (2008). Bayesian sensor model for indoor localization in ubiquitous sensor network. In International Telecommunication Union - Proceedings of the 1st ITU-T Kaleidoscope Academic Conference, Innovations in NGN, K-INGN [4542278] https://doi.org/10.1109/KINGN.2008.4542278

Bayesian sensor model for indoor localization in ubiquitous sensor network. / Bekkali, Abdelmoula; Matsumoto, Mitsuji.

International Telecommunication Union - Proceedings of the 1st ITU-T Kaleidoscope Academic Conference, Innovations in NGN, K-INGN. 2008. 4542278.

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

Bekkali, A & Matsumoto, M 2008, Bayesian sensor model for indoor localization in ubiquitous sensor network. in International Telecommunication Union - Proceedings of the 1st ITU-T Kaleidoscope Academic Conference, Innovations in NGN, K-INGN., 4542278, 1st ITU-T Kaleidoscope Academic Conference, Innovations in NGN, K-INGN, Geneva, 08/5/12. https://doi.org/10.1109/KINGN.2008.4542278
Bekkali A, Matsumoto M. Bayesian sensor model for indoor localization in ubiquitous sensor network. In International Telecommunication Union - Proceedings of the 1st ITU-T Kaleidoscope Academic Conference, Innovations in NGN, K-INGN. 2008. 4542278 https://doi.org/10.1109/KINGN.2008.4542278
Bekkali, Abdelmoula ; Matsumoto, Mitsuji. / Bayesian sensor model for indoor localization in ubiquitous sensor network. International Telecommunication Union - Proceedings of the 1st ITU-T Kaleidoscope Academic Conference, Innovations in NGN, K-INGN. 2008.
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