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Adaptive Neuro-Fuzzy Location Indicator in Wireless Sensor Networks

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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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Acknowledgements

This work of research and innovation is made within a MOBIDOC thesis, financed by the European Union (EU) within the framework of the PASRI program and partially supported by Cynapsys IT Enterprise. Gratitude for all Cynapsys team: Mr Ammar Mr Tebourbi and Mr Kchaou. Also to the students M. Jridi, A. Soula and K. Barouni for their contributions in the experimentations.

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Authors and Affiliations

  1. Innov’COM/ENIT/Cynapsys, University of Tunis El Manar, Tunis, Tunisia

    Noura Baccar

  2. INSAT/Cynapsys, University of Carthage, Tunis, Tunisia

    Mootez Jridi

  3. Innov’COM, University of Carthage, Tunis, Tunisia

    Ridha Bouallegue

Authors
  1. Noura Baccar
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  2. Mootez Jridi
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  3. Ridha Bouallegue
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Correspondence to Noura Baccar.

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Baccar, N., Jridi, M. & Bouallegue, R. Adaptive Neuro-Fuzzy Location Indicator in Wireless Sensor Networks. Wireless Pers Commun 97, 3165–3181 (2017). https://doi.org/10.1007/s11277-017-4668-3

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