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Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues

Internet of Vehicles (IoV) technology has been attracting great interest from both academia and industry due to its huge potential impact on improving driving experiences and enabling better transportation systems. While a large number of interesting IoV applications are expected, it is more challen...

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Autores principales: Danba, Sedeng, Bao, Jingjing, Han, Guorong, Guleng, Siri, Wu, Celimuge
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9503948/
https://www.ncbi.nlm.nih.gov/pubmed/36146341
http://dx.doi.org/10.3390/s22186995
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author Danba, Sedeng
Bao, Jingjing
Han, Guorong
Guleng, Siri
Wu, Celimuge
author_facet Danba, Sedeng
Bao, Jingjing
Han, Guorong
Guleng, Siri
Wu, Celimuge
author_sort Danba, Sedeng
collection PubMed
description Internet of Vehicles (IoV) technology has been attracting great interest from both academia and industry due to its huge potential impact on improving driving experiences and enabling better transportation systems. While a large number of interesting IoV applications are expected, it is more challenging to design an efficient IoV system compared with conventional Internet of Things (IoT) applications due to the mobility of vehicles and complex road conditions. We discuss existing studies about enabling collaborative intelligence in IoV systems by focusing on collaborative communications, collaborative computing, and collaborative machine learning approaches. Based on comparison and discussion about the advantages and disadvantages of recent studies, we point out open research issues and future research directions.
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spelling pubmed-95039482022-09-24 Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues Danba, Sedeng Bao, Jingjing Han, Guorong Guleng, Siri Wu, Celimuge Sensors (Basel) Article Internet of Vehicles (IoV) technology has been attracting great interest from both academia and industry due to its huge potential impact on improving driving experiences and enabling better transportation systems. While a large number of interesting IoV applications are expected, it is more challenging to design an efficient IoV system compared with conventional Internet of Things (IoT) applications due to the mobility of vehicles and complex road conditions. We discuss existing studies about enabling collaborative intelligence in IoV systems by focusing on collaborative communications, collaborative computing, and collaborative machine learning approaches. Based on comparison and discussion about the advantages and disadvantages of recent studies, we point out open research issues and future research directions. MDPI 2022-09-15 /pmc/articles/PMC9503948/ /pubmed/36146341 http://dx.doi.org/10.3390/s22186995 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Danba, Sedeng
Bao, Jingjing
Han, Guorong
Guleng, Siri
Wu, Celimuge
Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues
title Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues
title_full Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues
title_fullStr Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues
title_full_unstemmed Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues
title_short Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues
title_sort toward collaborative intelligence in iov systems: recent advances and open issues
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9503948/
https://www.ncbi.nlm.nih.gov/pubmed/36146341
http://dx.doi.org/10.3390/s22186995
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