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Wireless Personal Communications
Erschienen in: Wireless Personal Communications 2/2017

31.07.2017

Adaptive Neuro-Fuzzy Location Indicator in Wireless Sensor Networks

verfasst von: Noura Baccar, Mootez Jridi, Ridha Bouallegue

Erschienen in: Wireless Personal Communications | Ausgabe 2/2017

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Abstract

Indoor localization is a basic process in Wireless Sensor Networks (WSN) monitoring. This paper presents a new approach for localization of mobile nodes in WSNs. The proposed approach is based on the design of an adaptive fuzzy localization system. First proposed contribution is to consider the rooms of the target environment as a fuzzy sets made by adjacent zones described by a Fuzzy Location Indicator (FLI). FLI provides a fuzzy linearization of the building map hence the creation of a fuzzy linguistic model of the system. Fingerprints of the Radio Signal Strength Indicators (RSSI) are collected from different anchors according to each FLI. A Sugeno type-0 fuzzy inference system is proposed and submitted to a supervised learning through the neuro-fuzzy ANFIS algorithm. Simulation results as well as experimentations in Cynapsys company premises have proved that a good learning process leads to high success rate. Finally, a comparative study with two fuzzy localization systems proved the lower localization error average of the proposed approach.
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Metadaten
Titel
Adaptive Neuro-Fuzzy Location Indicator in Wireless Sensor Networks
verfasst von
Noura Baccar
Mootez Jridi
Ridha Bouallegue
Publikationsdatum
31.07.2017
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 2/2017
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-017-4668-3

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