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

Daniel Alshamaa 1 Farah Mourad-Chehade 1 Paul Honeine 2
2 DocApp - LITIS - Equipe Apprentissage
LITIS - Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes
Abstract : 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.
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Journal articles
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https://hal-utt.archives-ouvertes.fr/hal-02307466
Contributor : Jean-Baptiste Vu Van <>
Submitted on : Monday, October 7, 2019 - 4:11:52 PM
Last modification on : Wednesday, October 9, 2019 - 1:38:17 AM

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Daniel Alshamaa, Farah Mourad-Chehade, Paul Honeine. Decentralized Kernel-Based Localization in Wireless Sensor Networks Using Belief Functions. IEEE Sensors Journal, Institute of Electrical and Electronics Engineers, 2019, 19 (11), pp.4149-4159. ⟨10.1109/JSEN.2019.2898106⟩. ⟨hal-02307466⟩

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