Zoning-based Localization in Indoor Sensor Networks Using Belief Functions Theory

Daniel Alshamaa (1) , Farah Mourad-Chehade (1) , Paul Honeine (2)

1 LM2S - Laboratoire Modélisation et Sûreté des Systèmes
2 DocApp - LITIS - Equipe Apprentissage

Localization is an essential issue in wireless sensor networks to process the information retrieved by sensor nodes. This paper presents an indoor zoning-based localization technique that works efficiently in real environments. The targeted area is composed of several zones, the objective being to find the zone where the mobile node is instantly located. The proposed approach collects first strengths of received WiFi signals from neighboring access points and builds a fingerprints database. It then uses belief functions theory to combine all measured data and define an evidence framework, to be used afterwards for estimating the most probable node's zone. Real experiments demonstrate the effectiveness of this approach and its competence compared to state-of-the-art methods.

Mots clés: wireless LAN; zoning-based localization; indoor sensor networks; belief functions theory; sensor nodes; mobile node; WiFi signals; evidence framework; IEEE 802.11 Standard; Mobile nodes; Databases; Indoor environments; Buildings; Wireless sensor networks; Belief functions; indoor environment; radio fingerprints; zoning

Domaines: Apprentissage [cs.LG]; Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]; Vision par ordinateur et reconnaissance de formes [cs.CV]

Daniel Alshamaa, Farah Mourad-Chehade, Paul Honeine. Zoning-based Localization in Indoor Sensor Networks Using Belief Functions Theory. Proc. 17th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2016, Edinburgh, UK, Unknown Region. ⟨10.1109/SPAWC.2016.7536787⟩. ⟨hal-01965915⟩