國立虎尾科技大學 |

Deep learning based solutions for vehicular adhoc networks

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Deep learning based solutions for vehicular adhoc networks/ edited by Jitendra Bhatia ... [et al.].
other author:	Bhatia, Jitendra.
Published:	Singapore :Springer Nature Singapore : : 2025.,
Description:	xv, 392 p. :ill., digital ; : 24 cm.;
Contained By:	Springer Nature eBook
Subject:	Vehicular ad hoc networks (Computer networks) -
Online resource:	https://doi.org/10.1007/978-981-96-5190-0
ISBN:	9789819651900

Deep learning based solutions for vehicular adhoc networks
Deep learning based solutions for vehicular adhoc networks[electronic resource] /edited by Jitendra Bhatia ... [et al.]. - Singapore :Springer Nature Singapore :2025. - xv, 392 p. :ill., digital ;24 cm. - Studies in computational intelligence,v. 12071860-9503 ;. - Studies in computational intelligence ;v. 50. .

Overview of Vehicular Ad Hoc Networks -- Architecture and Protocols for data transmission in VANETs -- Applications and Challenges in VANETs -- Deep Learning Architectures for VANET -- Deep Learning for Security in VANET Secure Data Transmission in VANET -- Deep Learning for Resource Allocation in VANET -- Deep Learning for Traffic Prediction in VANET -- Traffic Prediction and modeling in Vehicular Ad Hoc Networks -- Traffic Data Collection and Processing in VANETs -- Deep Learning for Autonomous VANETs -- Implementation and Deployment of Deep Learning in Vehicular Ad Hoc Networks -- Deployment Strategies for Deep Learning in VANETs -- Energy Efficiency Deep Learning techniques in VANETs -- Case Studies and Real-World Deployment Examples -- Future Research Directions in Deep Learning for VANET -- Emerging Trends in VANETs -- Research Challenges and Open Issues in deploying deep learning models in VANETs -- Simulation/Emulation Platforms for Deep Learning in VANETs -- A framework to simulate VANETs.

This book provides a holistic and comprehensive approach to deep learning for vehicular ad hoc networks (VANETs), covering various aspects such as applications, agency involvement, and potential ethical and legal issues. It begins with discussions on how the transportation system has been converted into Intelligent Transportation System (ITS). The use of VANETs is increasing in the development of ITS to enhance road safety, traffic efficiency, and driver comfort. However, the dynamic nature of vehicular environments and the high mobility of vehicles pose significant challenges to designing and implementing VANETs and ensuring reliable and efficient communication. Deep learning, a subset of machine learning, has the potential to revolutionize vehicular ad hoc networks (VANETs) to enable various applications such as traffic management, collision avoidance, and infotainment. DL has demonstrated great potential in addressing various challenges involved in VANETs by leveraging its ability to learn from vast data and make accurate predictions. It reviews the state-of-the-art DL-based approaches for various applications in VANETs, including routing, congestion control, autonomous driving, and security. In addition, this book provides a comprehensive analysis of these approaches' advantages and limitations and discusses their future research directions. The study in this book shows that DL-based techniques can significantly improve the performance and reliability of VANETs. Still, in-depth research is required to address the challenges of deploying these methods in real-world scenarios. Finally, the book discusses the potential of DL-based VANETs in supporting other emerging technologies, such as autonomous driving and smart cities. It explores the simulation/emulation tools for practical exposure to the vehicular ad hoc network.

ISBN: 9789819651900

Standard No.: 10.1007/978-981-96-5190-0doiSubjects--Topical Terms:

716694
Vehicular ad hoc networks (Computer networks)

LC Class. No.: TE228.37

Dewey Class. No.: 388.312

Deep learning based solutions for vehicular adhoc networks
LDR:03978nam a2200349 a 4500 001 1159526
003 DE-He213
005 20250609142457.0
006 m d
007 cr nn 008maaau
008 251029s2025 si s 0 eng d
020 $a 9789819651900 $q (electronic bk.)
020 $a 9789819651894 $q (paper)
024 7 $a 10.1007/978-981-96-5190-0 $2 doi
035 $a 978-981-96-5190-0
040 $a GP $c GP
041 0 $a eng
050 4 $a TE228.37
072 7 $a UT $2 bicssc
072 7 $a COM043000 $2 bisacsh
072 7 $a UT $2 thema
082 0 4 $a 388.312 $2 23
090 $a TE228.37 $b .D311 2025
245 0 0 $a Deep learning based solutions for vehicular adhoc networks $h [electronic resource] / $c edited by Jitendra Bhatia ... [et al.].
260 $a Singapore : $c 2025. $b Springer Nature Singapore : $b Imprint: Springer,
300 $a xv, 392 p. : $b ill., digital ; $c 24 cm.
338 $a online resource $b cr $2 rdacarrier
490 1 $a Studies in computational intelligence, $x 1860-9503 ; $v v. 1207
505 0 $a Overview of Vehicular Ad Hoc Networks -- Architecture and Protocols for data transmission in VANETs -- Applications and Challenges in VANETs -- Deep Learning Architectures for VANET -- Deep Learning for Security in VANET Secure Data Transmission in VANET -- Deep Learning for Resource Allocation in VANET -- Deep Learning for Traffic Prediction in VANET -- Traffic Prediction and modeling in Vehicular Ad Hoc Networks -- Traffic Data Collection and Processing in VANETs -- Deep Learning for Autonomous VANETs -- Implementation and Deployment of Deep Learning in Vehicular Ad Hoc Networks -- Deployment Strategies for Deep Learning in VANETs -- Energy Efficiency Deep Learning techniques in VANETs -- Case Studies and Real-World Deployment Examples -- Future Research Directions in Deep Learning for VANET -- Emerging Trends in VANETs -- Research Challenges and Open Issues in deploying deep learning models in VANETs -- Simulation/Emulation Platforms for Deep Learning in VANETs -- A framework to simulate VANETs.
520 $a This book provides a holistic and comprehensive approach to deep learning for vehicular ad hoc networks (VANETs), covering various aspects such as applications, agency involvement, and potential ethical and legal issues. It begins with discussions on how the transportation system has been converted into Intelligent Transportation System (ITS). The use of VANETs is increasing in the development of ITS to enhance road safety, traffic efficiency, and driver comfort. However, the dynamic nature of vehicular environments and the high mobility of vehicles pose significant challenges to designing and implementing VANETs and ensuring reliable and efficient communication. Deep learning, a subset of machine learning, has the potential to revolutionize vehicular ad hoc networks (VANETs) to enable various applications such as traffic management, collision avoidance, and infotainment. DL has demonstrated great potential in addressing various challenges involved in VANETs by leveraging its ability to learn from vast data and make accurate predictions. It reviews the state-of-the-art DL-based approaches for various applications in VANETs, including routing, congestion control, autonomous driving, and security. In addition, this book provides a comprehensive analysis of these approaches' advantages and limitations and discusses their future research directions. The study in this book shows that DL-based techniques can significantly improve the performance and reliability of VANETs. Still, in-depth research is required to address the challenges of deploying these methods in real-world scenarios. Finally, the book discusses the potential of DL-based VANETs in supporting other emerging technologies, such as autonomous driving and smart cities. It explores the simulation/emulation tools for practical exposure to the vehicular ad hoc network.
650 0 $a Vehicular ad hoc networks (Computer networks) $3 716694
650 0 $a Deep learning (Machine learning) $3 1381171
650 1 4 $a Computer Networks. $3 1365770
650 2 4 $a Machine Learning. $3 1137723
650 2 4 $a Computational Intelligence. $3 768837
700 1 $a Bhatia, Jitendra. $3 1486830
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
830 0 $a Studies in computational intelligence ; $v v. 50. $3 720500 $3 770436
856 4 0 $u https://doi.org/10.1007/978-981-96-5190-0
950 $a Engineering (SpringerNature-11647)