Abstract
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.
Highlights
Localization is an essential aspect in WSNs, since the knowledge of the sensor’s location is critical to process the information originating from this sensor
The contextual discounting carried an enhancement of 3 to 4% as compared with the classical discounting. This was due to considering the reliability of the APs per area or zone, which is important in the case of localization using received signal strength indicator (RSSI)
Once a new observation was carried for localization, the constructed mass functions were used to assign a mass for each zone with respect to each AP
Summary
Localization is an essential aspect in WSNs, since the knowledge of the sensor’s location is critical to process the information originating from this sensor. A solution is to integrate a Global Positioning System or Global System for Mobile Communications (GPS-GSM) into sensor nodes This is widely used in vehicle tracking systems [1]. It is not always the optimal solution because of the costs of having a GPS receiver at each node, especially when multiple objects are to be localized, as well as for the limited spatial resolution. The objective becomes to determine the position of any MN using collected measurements and information exchanged with the ANs. The remaining issue is to choose the appropriate enabling technology and the measurement technique. We present hereby a brief description of the enabling technologies and the measurement techniques
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