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Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues.
Danba, Sedeng; Bao, Jingjing; Han, Guorong; Guleng, Siri; Wu, Celimuge.
Afiliação
  • Danba S; School of Computer Science and Information Engineering, Hohhot Minzu College, Hohhot 010051, China.
  • Bao J; Graduate School of Informatics and Engineering, University of Electro-Communications, Tokyo 182-8585, Japan.
  • Han G; B&S Tech Co., Ltd., Arakawa-ku, Tokyo 116-0014, Japan.
  • Guleng S; School of Computer Science and Information Engineering, Hohhot Minzu College, Hohhot 010051, China.
  • Wu C; Graduate School of Informatics and Engineering, University of Electro-Communications, Tokyo 182-8585, Japan.
Sensors (Basel) ; 22(18)2022 Sep 15.
Article em En | MEDLINE | ID: mdl-36146341
ABSTRACT
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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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Condução de Veículo / Internet das Coisas Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Condução de Veículo / Internet das Coisas Idioma: En Ano de publicação: 2022 Tipo de documento: Article