CFP on “Superior Signal Processing Methods for VANET Communication”
Vehicle-to-vehicle and vehicle-to-infrastructure communication can be improved with the use of vehicle ad hoc networks (VANETs), which can also increase passenger comfort, traffic efficiency, and road safety. Reliable and efficient communication in VANETs depends on the use of advanced signal processing techniques. High mobility, variable channel conditions, and sporadic connectivity are among the particular difficulties that VANETs manage. To properly handle these issues, legacy communication techniques might not be sufficient. Addressing channel impairments, fighting interference, and improving dependability in dynamic vehicle situations are all made possible by advanced signal processing techniques. To ensure dependable and efficient communication in dynamic vehicle circumstances, advanced algorithms for signal processing are necessary. Enhancing signal quality, decreasing interference, increasing data throughput, and fortifying communication security are all made possible by these techniques, which include beam formation, MIMO infrastructure, channel estimation and adjustment, biodiversity techniques, and cognitive radio.
Automobiles cooperate to transmit signals in cooperative communication, a recent breakthrough in VANET signal processing, to increase coverage and reliability. Machine learning methods are applied to optimize signal processing parameters in a flexible manner based on network dynamics and ambient variables. Software-defined radio (SDR) technologies enable flexible radio solutions through dynamic prototyping and algorithmic implementation of signal processing. VANETs also use powerful signal processing techniques to lower security risks and protect user privacy. In the future, VANET signal processing will probably facilitate the integration of 5G to enable ultra-reliable low-latency communication and enormous machine-type communication. It will also prioritize high reliability and low latency communication by utilizing signal processing techniques designed specifically for autonomous vehicles, as well as blockchain-based security to increase trust and vehicular edge computing to reduce latency and enable real-time decision-making. However, sophisticated signal processing methods are necessary to address the unique issues with VANET communication in order to deliver dependable and fruitful interactions in dynamic vehicular environments. Continuous research and innovation in this field will lead to the development of VANET signal processing and make transportation systems safer and more interconnected.
This special issue associated with VANET signal processing includes the need for scalable algorithms that can handle large-scale deployments, co-channel interference from neighboring cars and malicious blocking assaults, and quickly changing channel conditions brought on by vehicle movement. Adaptive algorithms, which adjust parameters in response to current conditions; collaborative sensing, in which vehicles exchange data to minimize interference; and cross-layer optimization, which integrates signal processing with network and application layers for all-encompassing performance improvement, are potential solutions to these problems.
The topics of interest include:
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A smarter hybrid machine learning approach in VANET for reliable communication
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Using diversity approaches to increase communication dependability in vehicular networks
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Vehicle-to-vehicle Internet of Things (VANET): intelligent and safe connectivity
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Strategies, Guidelines, and Standards for Communication Relaying Mechanisms in VANET
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Assessment and evaluation of the VANET's distance-to-mean broadcast technique
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An overview of VANETs, including new architectures, uses, security concern
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An Assessment of a Cluster-Based Connectivity Protocol’s for VANETs
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An analysis of the broadcasting routing protocols' specifications in the VANET
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A cooperative VANET system for emergency message transfer based on blockchain
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Automotive collision warning algorithms installed in VANET communication devices
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Understanding the mobility speed affects VANETs' end-to-end communication performance
LIST OF IMPORTANT DATES
Manuscript Submission Deadline: 15th September, 2025
Authors Notification: 05th November, 2025
Revised Papers Due: 15th February, 2026
Final notification: 30th April, 2026
GUEST EDITORS DETAIL
Lead Guest Editor:
Dr. A. S. M. Sanwar Hosen | Assistant Professor
Department of Artificial Intelligence and Big Data
Woosong University, South Korea
Official Email: sanwarhosen53@gmail.com, sanwar@wsu.ac.kr
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Biography: Dr. A. S. M. Sanwar Hosen is an accomplished academic and researcher currently serving as an Assistant Professor in the Department of Artificial Intelligence and Big Data at Woosong University, Daejeon, South Korea. He earned his M.S. and Ph.D. in Computer Science and Engineering from Jeonbuk National University (JBNU), where he also held postdoctoral positions at Kunsan National University and JBNU. His research interests are broad and include wireless sensor networks, Internet of Things (IoT), cybersecurity, edge-cloud computing, blockchain technology, and artificial intelligence. Dr. Hosen has published extensively in peer-reviewed journals and conferences, contributing valuable insights to these fields. He has also been an expert reviewer for high-impact journals such as IEEE Transactions, Elsevier, and MDPI, and has served as a Technical Program Committee Member for several international conferences, including those hosted by IEEE and ACM. His contributions extend beyond research, with a focus on the integration of emerging technologies in practical applications, particularly in the areas of network security and green IT. Through his work, Dr. Hosen continues to make significant strides in advancing AI, blockchain, and IoT, contributing to innovative, real-world solutions.
Co-Guest Editors:
Dr. Pradip Kumar Sharma | Associate Professor
SMIEEE
Department of Computing Science
University of Aberdeen, U.K.
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Biography: Dr. Pradip Sharma, SMIEEE, is a highly esteemed academician and researcher known for his expertise in Cybersecurity and Artificial Intelligence. Currently serving as an Associate Professor of Cybersecurity & AI at the Department of Computing Science, University of Aberdeen, UK. His academic journey is characterised by a steadfast pursuit of excellence. He holds a PhD in Secure Computing (2019), along with a Master of Technology in Computer Science (2014) and a Bachelor's in Information Technology (2009). Dr. Sharma's professional background encompasses roles such as Research Fellow and Software Engineer, reflecting a blend of academic and industry experience. Throughout his career, he has shown a strong interest in advancing research in areas such as Privacy-aware AI, Blockchain, Edge Computing, IoT Security, and Critical Infrastructure Security, earning recognition through various research projects and grants, including awards from EPSRC and Innovate UK. In addition to his research pursuits, Dr. Sharma actively engages in professional activities within the academic community. He holds editorial roles in esteemed journals and contributes to organizing conferences and workshops on cybersecurity and AI-related topics. Dr. Sharma's impact is also evident in his extensive publication record, featuring research articles in reputable journals and conferences. Dr. Sharma's academic contributions are further highlighted by his inclusion in the world's Top 2% Scientists list by Stanford University and his receipt of accolades such as the IEEE Outstanding Leadership Award and Publons Peer Review Awards. His commitment to education is evident through his teaching experiences, where he has developed and delivered courses on cybersecurity, distributed systems, and digital forensics. Dr. Sharma's dedication extends to mentoring the next generation of researchers, as demonstrated by his supervision of doctoral students and mentorship roles in academic and professional settings.
Dr. Tao (Kevin) Huang |Professor
SMIEEE
College of Science and Engineering
James Cook University, Australia
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Biography: Dr. Tao Huang (Senior Member, IEEE) received his Ph.D. in Electrical Engineering from The University of New South Wales, Sydney, Australia. He is an Electronic Systems and IoT Engineering lecturer at James Cook University, Cairns, Australia. He was an Endeavour Australia Cheung Kong Research Fellow, a visiting scholar at The Chinese University of Hong Kong, a research associate at the University of New South Wales, and a postdoctoral research fellow at James Cook University. Dr. Huang received the Australian Postgraduate Award and Engineering Research Award at The University of New South Wales. He received the Best Paper Award from the IEEE Wireless Communications and Networking Conference, Cancun, Mexico, in 2011. He received the IEEE Outstanding Leadership Award in 2022. He received the Citation for Outstanding Contribution to Student Learning at James Cook University in 2022. Dr. Huang serves as the MTT-S/COM Chapter Chair and Young Professionals Affinity Group Chair for the IEEE Northern Australia Section. Dr. Huang has served in several international conferences as TPC chair, program vice chair, local chair, and TPC member. Dr. Huang is an Associate Editor of the IEEE Open Journal of Communications Society, IEEE Access, and IET Communications. He is also a Topical Advisory Panel Member of MDPI Electronics. His research interests include deep learning, smart sensing, computer vision, pattern recognition, wireless communications, and IoT security.