Decentralized Sensor Localization by Decision Fusion of RSSI and Mobility in Indoor Environments

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

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

Localization of sensors has become an essential issue in wireless networks. This paper presents a decentralized approach to localize sensors in indoor environments. The targeted area is partitioned into several sectors, each of which having a local calculator capable of emitting, receiving, and processing data. Each calculator runs a local localization algorithm, by investigating the belief functions theory for decision fusion of radio fingerprints, to estimate the sensors zones. The fusion of all calculators estimates, is combined with a mobility model to yield a final zone decision. The decentralized algorithm is described and evaluated against the state-of-the-art. Experimental results show the effectiveness of the proposed method in terms of localization accuracy, processing time, and robustness.

Mots clés: machine learning; wireless sensor networks

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. Decentralized Sensor Localization by Decision Fusion of RSSI and Mobility in Indoor Environments. Proc. 25rd European Conference on Signal Processing (EUSIPCO), 2018, Rome, Italy. pp.2300-2304, ⟨10.23919/EUSIPCO.2018.8553020⟩. ⟨hal-01965909⟩