Tracking of Mobile Sensors Using Belief Functions in Indoor Wireless Networks. AlShamaa, D., Chehade, F., & Honeine, P. IEEE Sensors Journal, 18(1):310-319, January, 2018. ![Tracking of Mobile Sensors Using Belief Functions in Indoor Wireless Networks [link]](https://bibbase.org/img/filetypes/link.svg "link")
Link doi abstract bibtex 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.
@ARTICLE{18.tracking,
author = "Daniel AlShamaa and Farah Chehade and Paul Honeine",
title = {Tracking of Mobile Sensors Using Belief Functions in Indoor Wireless Networks},
journal = "IEEE Sensors Journal",
year = "2018",
volume = "18",
number = "1",
pages = "310-319",
month = jan,
url_link = "http://ieeexplore.ieee.org/document/8085100/",
doi = "10.1109/JSEN.2017.2766630",
keywords = "machine learning, wireless sensor networks, belief networks, indoor radio, mobile radio, sensor placement, target tracking, wireless sensor networks, mobile sensor tracking, mobile sensor localization, indoor wireless sensor network, indoor localization scheme, zoning-based tracking technique, indoor environment, belief function framework, Wi-Fi signal strength, hierarchical clustering, access point selection, Sensors, Mobile communication, Wireless fidelity, Indoor environments, Databases, Wireless sensor networks, Target tracking, Access point selection, belief functions, hierarchical clustering, mobility, tracking, WiFi signals",
abstract = "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.",
}
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