Decentralized Kernel-Based Localization in Wireless Sensor Networks Using Belief Functions

• Published in: IEEE sensors journal Vol. 19; no. 11; pp. 4149 - 4159
• Main Authors: Alshamaa, Daniel, Mourad-Chehade, Farah, Honeine, Paul
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Algorithms; Belief functions; Calculators; Data processing; decentralized data fusion; Engineering Sciences; Fingerprinting; fingerprints; Indoor environments; kernel density estimation; Localization; Mathematical analysis; Network topology; Remote sensors; Sensors; Signal and Image processing; Topology; Wireless fidelity; Wireless networks; Wireless sensor networks
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2019.2898106

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, developed in a belief functions framework, using RSS fingerprinting database, to estimate the sensors zones. The fusion of all calculators estimates yields a final zone estimate. Various decentralized architectures are described, then compared with each other, and against the state-of-the-art. The experimental results using WiFi real measurements show the effectiveness of the proposed approach in terms of localization accuracy, processing time, and complexity.