Location Agent: A Study Using Different Wireless Protocols for Indoor Localization

Ana Régia de M. Neves; Humphrey C. Fonseca; Célia G. Ralha

doi:doi:10.11648/j.wcmc.20130101.11

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Location Agent: A Study Using Different Wireless Protocols for Indoor Localization

Ana Régia de M. Neves, Humphrey C. Fonseca, Célia G. Ralha

Published in International Journal of Wireless Communications and Mobile Computing (Volume 1, Issue 1)

Received: 15 March 2013 Accepted: Published: 2 May 2013

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Abstract

Context-aware systems have received greater interest in the computing community. In order to provide relevant services at context-aware applications, the first task is to locate the user, what can be done preferably dynamically and intelligently. However, indoor mobile users localization is not a trivial problem, since it involves checking various devices, transmitting signals simultaneously on the same radio frequency, with possibly the three existing wireless network protocols: Wi-Fi, Bluetooth and ZigBee. In this direction, this paper presents an agent-based architecture with the Location Agent module defined for context-aware applications that uses three artificial neural network algorithms trained for the different protocols: backpropagation, backpropagation with momentum and levenberg–marquardt. Considering the research experimental aspects, a study is presented to compare the neural network algorithms including performance, regression analysis, precision and accuracy. The results indicate that the backpropagation algorithm trained with Bluetooth provides better accuracy (the average error of 0.42 meters) and the backpropagation trained with Wi-Fi provides better precision (73%). We consider our approach promising since the Location Agent has a quality of service component associated with the neural network algorithms that can choose the best received signal strength to locate indoor users.

Published in	International Journal of Wireless Communications and Mobile Computing (Volume 1, Issue 1)
DOI	10.11648/j.wcmc.20130101.11
Page(s)	1-6
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Indoor User Location, Context-Aware Systems, Multiagent System, Quality Of Service, Artificial Neural Network

References

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APA Style

Ana Régia de M. Neves, Humphrey C. Fonseca, Célia G. Ralha. (2013). Location Agent: A Study Using Different Wireless Protocols for Indoor Localization. International Journal of Wireless Communications and Mobile Computing, 1(1), 1-6. https://doi.org/10.11648/j.wcmc.20130101.11

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ACS Style

Ana Régia de M. Neves; Humphrey C. Fonseca; Célia G. Ralha. Location Agent: A Study Using Different Wireless Protocols for Indoor Localization. Int. J. Wirel. Commun. Mobile Comput. 2013, 1(1), 1-6. doi: 10.11648/j.wcmc.20130101.11

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AMA Style

Ana Régia de M. Neves, Humphrey C. Fonseca, Célia G. Ralha. Location Agent: A Study Using Different Wireless Protocols for Indoor Localization. Int J Wirel Commun Mobile Comput. 2013;1(1):1-6. doi: 10.11648/j.wcmc.20130101.11

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@article{10.11648/j.wcmc.20130101.11,
  author = {Ana Régia de M. Neves and Humphrey C. Fonseca and Célia G. Ralha},
  title = {Location Agent: A Study Using Different Wireless Protocols for Indoor Localization},
  journal = {International Journal of Wireless Communications and Mobile Computing},
  volume = {1},
  number = {1},
  pages = {1-6},
  doi = {10.11648/j.wcmc.20130101.11},
  url = {https://doi.org/10.11648/j.wcmc.20130101.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.wcmc.20130101.11},
  abstract = {Context-aware systems have received greater interest in the computing community. In order to provide relevant services at context-aware applications, the first task is to locate the user, what can be done preferably dynamically and intelligently. However, indoor mobile users localization is not a trivial problem, since it involves checking various devices, transmitting signals simultaneously on the same radio frequency, with possibly the three existing wireless network protocols: Wi-Fi, Bluetooth and ZigBee. In this direction, this paper presents an agent-based architecture with the Location Agent module defined for context-aware applications that uses three artificial neural network algorithms trained for the different protocols: backpropagation, backpropagation with momentum and levenberg–marquardt. Considering the research experimental aspects, a study is presented to compare the neural network algorithms including performance, regression analysis, precision and accuracy. The results indicate that the backpropagation algorithm trained with Bluetooth provides better accuracy (the average error of 0.42 meters) and the backpropagation trained with Wi-Fi provides better precision (73%). We consider our approach promising since the Location Agent has a quality of service component associated with the neural network algorithms that can choose the best received signal strength to locate indoor users.},
 year = {2013}
}

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TY  - JOUR
T1  - Location Agent: A Study Using Different Wireless Protocols for Indoor Localization
AU  - Ana Régia de M. Neves
AU  - Humphrey C. Fonseca
AU  - Célia G. Ralha
Y1  - 2013/05/02
PY  - 2013
N1  - https://doi.org/10.11648/j.wcmc.20130101.11
DO  - 10.11648/j.wcmc.20130101.11
T2  - International Journal of Wireless Communications and Mobile Computing
JF  - International Journal of Wireless Communications and Mobile Computing
JO  - International Journal of Wireless Communications and Mobile Computing
SP  - 1
EP  - 6
PB  - Science Publishing Group
SN  - 2330-1015
UR  - https://doi.org/10.11648/j.wcmc.20130101.11
AB  - Context-aware systems have received greater interest in the computing community. In order to provide relevant services at context-aware applications, the first task is to locate the user, what can be done preferably dynamically and intelligently. However, indoor mobile users localization is not a trivial problem, since it involves checking various devices, transmitting signals simultaneously on the same radio frequency, with possibly the three existing wireless network protocols: Wi-Fi, Bluetooth and ZigBee. In this direction, this paper presents an agent-based architecture with the Location Agent module defined for context-aware applications that uses three artificial neural network algorithms trained for the different protocols: backpropagation, backpropagation with momentum and levenberg–marquardt. Considering the research experimental aspects, a study is presented to compare the neural network algorithms including performance, regression analysis, precision and accuracy. The results indicate that the backpropagation algorithm trained with Bluetooth provides better accuracy (the average error of 0.42 meters) and the backpropagation trained with Wi-Fi provides better precision (73%). We consider our approach promising since the Location Agent has a quality of service component associated with the neural network algorithms that can choose the best received signal strength to locate indoor users.
VL  - 1
IS  - 1
ER  -

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Author Information

Ana Régia de M. Neves

Dept. of Electrical Engineering, University of Brasília, Brazil
Humphrey C. Fonseca

Dept. of Computer Science, University of Brasília, Brazil
Célia G. Ralha

Dept. of Computer Science, University of Brasília, Brazil

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Plain Text BibTeX RIS

APA Style

Ana Régia de M. Neves, Humphrey C. Fonseca, Célia G. Ralha. (2013). Location Agent: A Study Using Different Wireless Protocols for Indoor Localization. International Journal of Wireless Communications and Mobile Computing, 1(1), 1-6. https://doi.org/10.11648/j.wcmc.20130101.11

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ACS Style

Ana Régia de M. Neves; Humphrey C. Fonseca; Célia G. Ralha. Location Agent: A Study Using Different Wireless Protocols for Indoor Localization. Int. J. Wirel. Commun. Mobile Comput. 2013, 1(1), 1-6. doi: 10.11648/j.wcmc.20130101.11

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AMA Style

Ana Régia de M. Neves, Humphrey C. Fonseca, Célia G. Ralha. Location Agent: A Study Using Different Wireless Protocols for Indoor Localization. Int J Wirel Commun Mobile Comput. 2013;1(1):1-6. doi: 10.11648/j.wcmc.20130101.11

Copy | Download

@article{10.11648/j.wcmc.20130101.11,
  author = {Ana Régia de M. Neves and Humphrey C. Fonseca and Célia G. Ralha},
  title = {Location Agent: A Study Using Different Wireless Protocols for Indoor Localization},
  journal = {International Journal of Wireless Communications and Mobile Computing},
  volume = {1},
  number = {1},
  pages = {1-6},
  doi = {10.11648/j.wcmc.20130101.11},
  url = {https://doi.org/10.11648/j.wcmc.20130101.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.wcmc.20130101.11},
  abstract = {Context-aware systems have received greater interest in the computing community. In order to provide relevant services at context-aware applications, the first task is to locate the user, what can be done preferably dynamically and intelligently. However, indoor mobile users localization is not a trivial problem, since it involves checking various devices, transmitting signals simultaneously on the same radio frequency, with possibly the three existing wireless network protocols: Wi-Fi, Bluetooth and ZigBee. In this direction, this paper presents an agent-based architecture with the Location Agent module defined for context-aware applications that uses three artificial neural network algorithms trained for the different protocols: backpropagation, backpropagation with momentum and levenberg–marquardt. Considering the research experimental aspects, a study is presented to compare the neural network algorithms including performance, regression analysis, precision and accuracy. The results indicate that the backpropagation algorithm trained with Bluetooth provides better accuracy (the average error of 0.42 meters) and the backpropagation trained with Wi-Fi provides better precision (73%). We consider our approach promising since the Location Agent has a quality of service component associated with the neural network algorithms that can choose the best received signal strength to locate indoor users.},
 year = {2013}
}

Copy | Download

TY  - JOUR
T1  - Location Agent: A Study Using Different Wireless Protocols for Indoor Localization
AU  - Ana Régia de M. Neves
AU  - Humphrey C. Fonseca
AU  - Célia G. Ralha
Y1  - 2013/05/02
PY  - 2013
N1  - https://doi.org/10.11648/j.wcmc.20130101.11
DO  - 10.11648/j.wcmc.20130101.11
T2  - International Journal of Wireless Communications and Mobile Computing
JF  - International Journal of Wireless Communications and Mobile Computing
JO  - International Journal of Wireless Communications and Mobile Computing
SP  - 1
EP  - 6
PB  - Science Publishing Group
SN  - 2330-1015
UR  - https://doi.org/10.11648/j.wcmc.20130101.11
AB  - Context-aware systems have received greater interest in the computing community. In order to provide relevant services at context-aware applications, the first task is to locate the user, what can be done preferably dynamically and intelligently. However, indoor mobile users localization is not a trivial problem, since it involves checking various devices, transmitting signals simultaneously on the same radio frequency, with possibly the three existing wireless network protocols: Wi-Fi, Bluetooth and ZigBee. In this direction, this paper presents an agent-based architecture with the Location Agent module defined for context-aware applications that uses three artificial neural network algorithms trained for the different protocols: backpropagation, backpropagation with momentum and levenberg–marquardt. Considering the research experimental aspects, a study is presented to compare the neural network algorithms including performance, regression analysis, precision and accuracy. The results indicate that the backpropagation algorithm trained with Bluetooth provides better accuracy (the average error of 0.42 meters) and the backpropagation trained with Wi-Fi provides better precision (73%). We consider our approach promising since the Location Agent has a quality of service component associated with the neural network algorithms that can choose the best received signal strength to locate indoor users.
VL  - 1
IS  - 1
ER  -

Copy | Download