LEADER 00849nam0-22003131i-450- 001 990002039260403321 005 20150708152214.0 035 $a000203926 035 $aFED01000203926 035 $a(Aleph)000203926FED01 035 $a000203926 100 $a20030910d1956----km-y0itay50------ba 101 0 $ahun 200 1 $aPokolodarazs Alkatuak$d= Pompiloidea$fMoczar Laszlo 210 $aBudapest$cAkademiai Kiado$d1956 215 $a76 p.$d24 cm 225 1 $aFauna Hungariae$v11 610 0 $aImenotteri 610 0 $aPompilidae 610 0 $aHymenoptera, Pompilidae 676 $a595.79 700 1$aMoczar,$bLaszlo$085946 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990002039260403321 952 $a61 V D.4/08.3.05$b4731$fDAGEN 959 $aDAGEN 996 $aPokolodarazs Alkatuak$91498852 997 $aUNINA LEADER 04868nam 22006255 450 001 9911009335503321 005 20250609142457.0 010 $a981-9651-90-5 024 7 $a10.1007/978-981-96-5190-0 035 $a(CKB)39239643600041 035 $a(MiAaPQ)EBC32150305 035 $a(Au-PeEL)EBL32150305 035 $a(DE-He213)978-981-96-5190-0 035 $a(OCoLC)1523376344 035 $a(EXLCZ)9939239643600041 100 $a20250609d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDeep Learning Based Solutions for Vehicular Adhoc Networks /$fedited by Jitendra Bhatia, Sudeep Tanwar, Joel J. P. C. Rodrigues, Malaram Kumhar 205 $a1st ed. 2025. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2025. 215 $a1 online resource (470 pages) 225 1 $aStudies in Computational Intelligence,$x1860-9503 ;$v1207 311 08$a981-9651-89-1 327 $aOverview 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. 330 $aThis book provides a holistic and comprehensive approach to deep learning for vehicular ad hoc networks (VANETs), covering various aspects such as applications, agency involvement, and potential ethical and legal issues. It begins with discussions on how the transportation system has been converted into Intelligent Transportation System (ITS). The use of VANETs is increasing in the development of ITS to enhance road safety, traffic efficiency, and driver comfort. However, the dynamic nature of vehicular environments and the high mobility of vehicles pose significant challenges to designing and implementing VANETs and ensuring reliable and efficient communication. Deep learning, a subset of machine learning, has the potential to revolutionize vehicular ad hoc networks (VANETs) to enable various applications such as traffic management, collision avoidance, and infotainment. DL has demonstrated great potential in addressing various challenges involved in VANETs by leveraging its ability to learn from vast data and make accurate predictions. It reviews the state-of-the-art DL-based approaches for various applications in VANETs, including routing, congestion control, autonomous driving, and security. In addition, this book provides a comprehensive analysis of these approaches' advantages and limitations and discusses their future research directions. The study in this book shows that DL-based techniques can significantly improve the performance and reliability of VANETs. Still, in-depth research is required to address the challenges of deploying these methods in real-world scenarios. Finally, the book discusses the potential of DL-based VANETs in supporting other emerging technologies, such as autonomous driving and smart cities. It explores the simulation/emulation tools for practical exposure to the vehicular ad hoc network. 410 0$aStudies in Computational Intelligence,$x1860-9503 ;$v1207 606 $aComputer networks 606 $aMachine learning 606 $aComputational intelligence 606 $aComputer Networks 606 $aMachine Learning 606 $aComputational Intelligence 615 0$aComputer networks. 615 0$aMachine learning. 615 0$aComputational intelligence. 615 14$aComputer Networks. 615 24$aMachine Learning. 615 24$aComputational Intelligence. 676 $a621.3821 676 $a004.6 700 $aBhatia$b Jitendra$01827844 701 $aTanwar$b Sudeep$0942571 701 $aRodrigues$b Joel J. P. C$01373632 701 $aKumhar$b Malaram$01827845 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911009335503321 996 $aDeep Learning Based Solutions for Vehicular Adhoc Networks$94395977 997 $aUNINA