Artificial Intelligence in Visible Light Positioning for Indoor IoT: A Methodological Review
Indoor communication and positioning are significant fields of applications for indoor Internet of Things (IoT) given the rapid growth of IoT in smart cities, smart grids, and smart industries. Visible light positioning (VLP) has become more and more attractive for researchers to provide indoor loca...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Open Journal of the Communications Society |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10293149/ |
| _version_ | 1797631052467929088 |
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| author | Vasileios P. Rekkas Lazaros Alexios Iliadis Sotirios P. Sotiroudis Achilles D. Boursianis Panagiotis Sarigiannidis David Plets Wout Joseph Shaohua Wan Christos G. Christodoulou George K. Karagiannidis Sotirios K. Goudos |
| author_facet | Vasileios P. Rekkas Lazaros Alexios Iliadis Sotirios P. Sotiroudis Achilles D. Boursianis Panagiotis Sarigiannidis David Plets Wout Joseph Shaohua Wan Christos G. Christodoulou George K. Karagiannidis Sotirios K. Goudos |
| author_sort | Vasileios P. Rekkas |
| collection | DOAJ |
| description | Indoor communication and positioning are significant fields of applications for indoor Internet of Things (IoT) given the rapid growth of IoT in smart cities, smart grids, and smart industries. Visible light positioning (VLP) has become more and more attractive for researchers to provide indoor location-aware IoT services. Additionally, artificial intelligence (AI) has attracted considerable research effort to address the challenges in visible-light communication (VLC) systems. This is an emerging technology in next-generation wireless networks. However, despite the rapid progress, the use of AI in localization, navigation, and position estimation is still underexplored in VLC systems, and various research challenges are still open. This methodological review summarizes the research efforts regarding the use of AI in the field of VLP, to improve the position estimation accuracy in both two-dimensional (2D) and three-dimensional (3D) indoor IoT applications. This treatise also presents open issues and potential future directions for motivating further research in the field. Various databases were utilized in this paper: Scopus, Google Scholar, and IEEE Xplore; obtained 88 papers from 2017 to early 2023. Most (68%) of the AI articles in VLP systems are machine learning (ML) methods applied for localization and position estimation in VLC systems, while the other 32% of the research articles focussed on evolutionary algorithms. ML and evolutionary models may present limitations in terms of complexity and time-consuming nature but offer highly accurate, robust, reliable, and cost-effective results in terms of position estimation over conventional approaches. |
| first_indexed | 2024-03-11T11:16:33Z |
| format | Article |
| id | doaj.art-807adfa565b14afe89a9871863685d28 |
| institution | Directory Open Access Journal |
| issn | 2644-125X |
| language | English |
| last_indexed | 2024-03-11T11:16:33Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Communications Society |
| spelling | doaj.art-807adfa565b14afe89a9871863685d282023-11-11T00:01:47ZengIEEEIEEE Open Journal of the Communications Society2644-125X2023-01-0142838286910.1109/OJCOMS.2023.332721110293149Artificial Intelligence in Visible Light Positioning for Indoor IoT: A Methodological ReviewVasileios P. Rekkas0https://orcid.org/0000-0001-9171-8023Lazaros Alexios Iliadis1https://orcid.org/0000-0001-8090-1519Sotirios P. Sotiroudis2https://orcid.org/0000-0003-3557-9211Achilles D. Boursianis3https://orcid.org/0000-0001-5614-9056Panagiotis Sarigiannidis4https://orcid.org/0000-0001-6042-0355David Plets5https://orcid.org/0000-0002-8879-5076Wout Joseph6https://orcid.org/0000-0002-8807-0673Shaohua Wan7https://orcid.org/0000-0001-7013-9081Christos G. Christodoulou8https://orcid.org/0000-0002-9306-8666George K. Karagiannidis9https://orcid.org/0000-0001-8810-0345Sotirios K. Goudos10https://orcid.org/0000-0001-5981-5683ELEDIA@AUTH, School of Physics, Aristotle University of Thessaloniki, Thessaloniki, GreeceELEDIA@AUTH, School of Physics, Aristotle University of Thessaloniki, Thessaloniki, GreeceELEDIA@AUTH, School of Physics, Aristotle University of Thessaloniki, Thessaloniki, GreeceELEDIA@AUTH, School of Physics, Aristotle University of Thessaloniki, Thessaloniki, GreeceDepartment of Electrical and Computer Engineering, University of Western Macedonia, Kozani, GreeceDepartment of Information Technology, imec-WAVES Group, Ghent University, Ghent, BelgiumDepartment of Information Technology, imec-WAVES Group, Ghent University, Ghent, BelgiumShenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen, ChinaDepartment of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, NM, USADepartment of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, GreeceELEDIA@AUTH, School of Physics, Aristotle University of Thessaloniki, Thessaloniki, GreeceIndoor communication and positioning are significant fields of applications for indoor Internet of Things (IoT) given the rapid growth of IoT in smart cities, smart grids, and smart industries. Visible light positioning (VLP) has become more and more attractive for researchers to provide indoor location-aware IoT services. Additionally, artificial intelligence (AI) has attracted considerable research effort to address the challenges in visible-light communication (VLC) systems. This is an emerging technology in next-generation wireless networks. However, despite the rapid progress, the use of AI in localization, navigation, and position estimation is still underexplored in VLC systems, and various research challenges are still open. This methodological review summarizes the research efforts regarding the use of AI in the field of VLP, to improve the position estimation accuracy in both two-dimensional (2D) and three-dimensional (3D) indoor IoT applications. This treatise also presents open issues and potential future directions for motivating further research in the field. Various databases were utilized in this paper: Scopus, Google Scholar, and IEEE Xplore; obtained 88 papers from 2017 to early 2023. Most (68%) of the AI articles in VLP systems are machine learning (ML) methods applied for localization and position estimation in VLC systems, while the other 32% of the research articles focussed on evolutionary algorithms. ML and evolutionary models may present limitations in terms of complexity and time-consuming nature but offer highly accurate, robust, reliable, and cost-effective results in terms of position estimation over conventional approaches.https://ieeexplore.ieee.org/document/10293149/Artificial intelligenceindoor localizationmachine learningevolutionary algorithmsvisible light communicationvisible light positioning |
| spellingShingle | Vasileios P. Rekkas Lazaros Alexios Iliadis Sotirios P. Sotiroudis Achilles D. Boursianis Panagiotis Sarigiannidis David Plets Wout Joseph Shaohua Wan Christos G. Christodoulou George K. Karagiannidis Sotirios K. Goudos Artificial Intelligence in Visible Light Positioning for Indoor IoT: A Methodological Review IEEE Open Journal of the Communications Society Artificial intelligence indoor localization machine learning evolutionary algorithms visible light communication visible light positioning |
| title | Artificial Intelligence in Visible Light Positioning for Indoor IoT: A Methodological Review |
| title_full | Artificial Intelligence in Visible Light Positioning for Indoor IoT: A Methodological Review |
| title_fullStr | Artificial Intelligence in Visible Light Positioning for Indoor IoT: A Methodological Review |
| title_full_unstemmed | Artificial Intelligence in Visible Light Positioning for Indoor IoT: A Methodological Review |
| title_short | Artificial Intelligence in Visible Light Positioning for Indoor IoT: A Methodological Review |
| title_sort | artificial intelligence in visible light positioning for indoor iot a methodological review |
| topic | Artificial intelligence indoor localization machine learning evolutionary algorithms visible light communication visible light positioning |
| url | https://ieeexplore.ieee.org/document/10293149/ |
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