Tracking of Mobile Sensors Using Belief Functions in Indoor Wireless Networks

• Published in: IEEE sensors journal Vol. 18; no. 1; pp. 310 - 319
• Main Authors: Alshamaa, Daniel, Mourad-Chehade, Farah, Honeine, Paul
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Access point selection; belief functions; Clustering; Computer Science; Engineering Sciences; hierarchical clustering; Indoor environments; Localization; Machine Learning; Mathematical models; Mobile communication; mobility; Position sensing; Remote sensors; Sensors; Signal and Image processing; Target tracking; Three dimensional models; tracking; WiFi signals; Wireless fidelity; Wireless networks; Wireless sensor networks; mobility; belief function framework; Mobile communication; indoor environment; indoor wireless sensor network; tracking; Wi-Fi signal strength; Databases; sensor placement; indoor radio; Sensors; belief networks; Wireless fidelity; Access point selection; mobile radio; Target tracking; belief functions; Indoor environments; mobile sensor localization; mobile sensor tracking; machine learning; WiFi signals; Wireless sensor networks; indoor localization scheme; hierarchical clustering; zoning-based tracking technique
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2017.2766630

Localization of mobile sensors is an important research issue in wireless sensor networks. Most indoor localization schemes focus on determining the exact position of these sensors. This paper presents a zoning-based tracking technique that works efficiently in indoor environments. The targeted area is composed of several zones, the objective being to determine the zone of the mobile sensor in a real time tracking process. The proposed method creates a belief functions framework that combines evidence using the sensors mobility and observations. To do this, a mobility model is proposed by using the previous state of the sensor and its assumed maximum speed. Also, an observation model is constructed based on fingerprints collected as Wi-Fi signals strengths received from surrounding access points. This model can be extended via hierarchical clustering and access point selection. Real experiments demonstrate the effectiveness of this approach and its competence compared with state-of-the-art methods.