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Article Open Access October 19, 2021

A Ligthweight Wayfinding Assistance System for IoT Applications

Mouna Afif 1,*, Riadh Ayachi 1 and Mohamed Atri 2
1
Laboratory of Electronics and Microelectronics (EμE), Faculty of Sciences of Monastir, University of Monastir, Tunisia
2
College of Computer Science, King Khalid University, Abha, Saudi Arabia
Publihed in: Current Research in Public Health (Volume 1, Issue 1, 2021)
Page(s): 39-47
DOI: 10.31586/jaibd.2021.147
Received
September 16, 2021
Revised
October 15, 2021
Accepted
October 18, 2021
Published
October 19, 2021
Keywords
Indoor Wayfinding; Indoor Navigation; Blind and Sighted Persons; Internet of Things (IoT); Industry 4.0; Deep Convolutional Neural Network.
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), 2021. Published by Scientific Publications
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Cite This Article

APA Style
Afif, M. , Ayachi, R. , & Atri, M. (2021). A Ligthweight Wayfinding Assistance System for IoT Applications. Current Research in Public Health, 1(1), 39-47. https://doi.org/10.31586/jaibd.2021.147
ACS Style
Afif, M. ; Ayachi, R. ; Atri, M. A Ligthweight Wayfinding Assistance System for IoT Applications. Current Research in Public Health 2021 1(1), 39-47. https://doi.org/10.31586/jaibd.2021.147
Chicago/Turabian Style
Afif, Mouna, Riadh Ayachi, and Mohamed Atri. 2021. "A Ligthweight Wayfinding Assistance System for IoT Applications". Current Research in Public Health 1, no. 1: 39-47. https://doi.org/10.31586/jaibd.2021.147
AMA Style
Afif M, Ayachi R, Atri M. A Ligthweight Wayfinding Assistance System for IoT Applications. Current Research in Public Health. 2021; 1(1):39-47. https://doi.org/10.31586/jaibd.2021.147 
@Article{crph147,
AUTHOR = {Afif, Mouna and Ayachi, Riadh and Atri, Mohamed},
TITLE = {A Ligthweight Wayfinding Assistance System for IoT Applications},
JOURNAL = {Current Research in Public Health},
VOLUME = {1},
YEAR = {2021},
NUMBER = {1},
PAGES = {39-47},
URL = {/10.31586/jaibd-1-1-410.31586/jaibd/1/1/4},
ISSN = {2831-5162},
DOI = {10.31586/jaibd.2021.147},
ABSTRACT = {In this paper, we propose to design an indoor sign detection system for industry 4.0. In order to implement the proposed system, we proposed a lightweight deep learning-based architecture based on MobileNet which can be run on embedded devices used to detect and recognize indoor landmarks signs in order to assist blind and sighted during indoor navigation. We apply various operations in order to minimize the network size as well as computation complexity. Internet of things (IoT) presents a connection between internet and the surroundings objects. IoT is characterized to connect physical objects with their numerical identities and enables them to connect with each other. This technique creates a kind of bridge between the physical world and the virtual world. The paper provides a comprehensive overview of a new method for a set of landmark indoor sign objects based on deep convolutional neural network (DCNN) for internet of things applications.},
}
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AB  - In this paper, we propose to design an indoor sign detection system for industry 4.0. In order to implement the proposed system, we proposed a lightweight deep learning-based architecture based on MobileNet which can be run on embedded devices used to detect and recognize indoor landmarks signs in order to assist blind and sighted during indoor navigation. We apply various operations in order to minimize the network size as well as computation complexity. Internet of things (IoT) presents a connection between internet and the surroundings objects. IoT is characterized to connect physical objects with their numerical identities and enables them to connect with each other. This technique creates a kind of bridge between the physical world and the virtual world. The paper provides a comprehensive overview of a new method for a set of landmark indoor sign objects based on deep convolutional neural network (DCNN) for internet of things applications.
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