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Open AccessArticle

An Evidential Framework for Localization of Sensors in Indoor Environments

1
Charles Delaunay Institute, University of Technology of Troyes, 10300 Troyes, France
2
LITIS Lab, University of Rouen Normandie, 76130 Rouen, France
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(1), 318; https://doi.org/10.3390/s20010318
Received: 12 November 2019 / Revised: 25 December 2019 / Accepted: 4 January 2020 / Published: 6 January 2020
(This article belongs to the Special Issue Sensors Localization in Indoor Wireless Networks)
Indoor localization has several applications ranging from people tracking and indoor navigation, to autonomous robot navigation and asset tracking. We tackle the problem as a zoning localization where the objective is to determine the zone where the mobile sensor resides at any instant. The decision-making process in localization systems relies on data coming from multiple sensors. The data retrieved from these sensors require robust fusion approaches to be processed. One of these approaches is the belief functions theory (BFT), also called the Dempster–Shafer theory. This theory deals with uncertainty and imprecision with a theoretically attractive evidential reasoning framework. This paper investigates the usage of the BFT to define an evidence framework for estimating the most probable sensor’s zone. Real experiments demonstrate the effectiveness of this approach and its competence compared to state-of-the-art methods. View Full-Text
Keywords: decision-making; evidence fusion; localization; WiFi RSSI decision-making; evidence fusion; localization; WiFi RSSI
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MDPI and ACS Style

Alshamaa, D.; Mourad-Chehade, F.; Honeine, P.; Chkeir, A. An Evidential Framework for Localization of Sensors in Indoor Environments. Sensors 2020, 20, 318.

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