Athanasios Kanavos | Vehicular Communication Systems | Best Researcher Award

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Dr. Athanasios Kanavos | Vehicular Communication Systems | Best Researcher Award

Postdoctoral Researcher  |  Department of Informatics and Telecommunications, Tripoli  |  Greece

Dr. Athanasios Kanavos is a distinguished postdoctoral researcher at the Department of Informatics and Telecommunications, University of Peloponnese, where he contributes extensively to the Wireless and Mobile Communications Lab. His professional expertise lies in advanced V2X communications, cellular networks, and communication protocols, focusing on optimizing network performance for next-generation intelligent transportation systems. His ongoing research emphasizes reinforcement learning-based scheduling techniques for autonomous driving applications in emerging 6G cellular networks, addressing challenges in resource allocation, latency, and reliability. Dr. Kanavos’s research interests span cellular communications, network performance evaluation, handover and scheduling algorithms, and the integration of AI/ML techniques for intelligent network management. His technical proficiency and research skills include simulation-based protocol design, performance analysis, and algorithmic optimization for vehicular and wireless communication systems. With several impactful publications in respected international journals such as Telecom and ACM conferences, his scholarly contributions have advanced the understanding of adaptive scheduling mechanisms and their application in vehicular communication environments. His innovative approaches have significantly improved throughput, connectivity stability, and communication reliability in dynamic vehicular networks, showcasing both academic excellence and industrial relevance. Recognized for his scientific rigor and commitment to the advancement of wireless communication technologies, Dr. Kanavos continues to influence future developments in connected mobility and smart transportation. His dedication to bridging the gap between theoretical research and practical solutions underscores his position as a leading figure in the field of next-generation communication systems. He has achieved 29 Citations,  4 Documents, 2 h-index.

Profiles:  ORCID  |  Scopus

Featured Publications 

1. Kanavos, A., & Kaloxylos, A. (2025, February 19). V2X Communications in Highway Environments: Scheduling Challenges and Solutions for 6G Networks. Telecom.

2. Kanavos, A., Barmpounakis, S., & Kaloxylos, A. (2023, July 6). An Adaptive Scheduling Mechanism Optimized for V2N Communications over Future Cellular Networks. Telecom.

3. Kanavos, A., Fragkos, D., & Kaloxylos, A. (2021, January). V2X Communication over Cellular Networks: Capabilities and Challenges. Telecom.

4. Kanavos, A., Fragkos, D., & Kaloxylos, A. (2020, November 20). Delay and Spectrum Analysis for V2X Communication over 5G Networks. 24th Pan-Hellenic Conference on Informatics.

Dr. Athanasios Kanavos’s research advances the evolution of intelligent transportation systems by enhancing the efficiency and reliability of V2X communications within 5G and 6G networks. His innovative scheduling and resource allocation mechanisms contribute to safer, low-latency, and more connected vehicular ecosystems—driving global progress toward autonomous mobility and smarter urban infrastructures.

Jiayin Tang | Automotive Artificial Intelligence | Excellence in Research Award

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Dr. Jiayin Tang | Automotive Artificial Intelligence  |  Excellence in Research Award

Associate professor  |  Southwest Jiaotong university  |  China

Prof. Jiayin Tang is a distinguished academic and researcher whose work bridges the fields of manufacturing, reliability engineering, and intelligent systems, with a strong focus on mechanical, electrical, and automation technologies. His scholarly pursuits emphasize reliability assessment, degradation modeling, fault diagnosis, and intelligent prediction within industrial systems, contributing to both theoretical innovation and practical applications in smart manufacturing and system health management. His research encompasses areas such as accelerated life testing, reliability inference under multiple stress factors, and fault detection using deep learning and advanced signal processing. Prof. Tang’s notable publications in leading international journals including IEEE Transactions on Instrumentation and Measurement, Quality and Reliability Engineering International, PLOS ONE, and IEEE Sensors Journal demonstrate his mastery of reliability modeling and intelligent diagnostic algorithms. He has developed advanced methodologies such as Wiener process-based models, complex attention transformers, and graph attention networks to enhance predictive maintenance and system dependability in modern industrial environments. With expertise in automation, transportation systems, and electronic reliability, he continues to contribute significantly to the advancement of smart industrial solutions and sustainable engineering practices. Prof. Tang’s research skills include data-driven modeling, machine learning, statistical analysis, and sensor-based fault detection, reflecting his interdisciplinary strength and innovative vision. His dedication to academic excellence and impactful research has earned him recognition within the international reliability and automation research communities. He has acheived 250 Citations, 39 Documents, 8 h-index.

Profiles:  ORCID  |  Scopus

Featured Publication

  1. Gan, W., & Tang, J. (2024). Multi-Performance Degradation System Reliability Analysis with Varying Failure Threshold Based on Copulas. Symmetry, 16(1), 57.

 

Feng Wang | EV Charging Infrastructure | Best Researcher Award

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Dr. Feng Wang | EV Charging Infrastructure | Best Researcher Award

Associate Professor  |  Fujian University of Technology |  China

Dr. Feng Wang is a distinguished academic and researcher recognized for his significant contributions to the fields of computer cryptography, network security, and applied mathematics. Currently serving as an Associate Professor at the College of Computer Science and Mathematics, Fujian University of Technology, he has established himself as a prominent figure in the development of secure computational frameworks and innovative cryptographic algorithms. His extensive professional experience spans teaching, research, and collaborative projects that integrate theoretical mathematics with practical applications in cybersecurity. Dr. Wang’s research focuses on computer cryptography, data privacy, and secure communication protocols within distributed and cloud-based computing environments. His scholarly work demonstrates a strong command of mathematical modeling, algorithm design, and encryption mechanisms, enabling the advancement of secure data transmission and protection techniques. He is particularly skilled in areas such as network information assurance, data encryption standards, and privacy-preserving computation, which are essential for modern information systems. Over the years, he has guided numerous students and contributed to academic excellence through publications, peer reviews, and conference participation. Dr. Wang’s dedication to advancing research in computer and network security has earned him recognition within the academic community. His research output continues to influence emerging developments in cybersecurity and applied cryptography, providing a foundation for next-generation secure computing technologies. His work reflects a balance of theoretical insight and practical relevance, aligning with the evolving challenges of global information security. Feng Wang remains committed to fostering academic innovation and interdisciplinary collaboration that bridges mathematics, computer science, and information technology. He has achieved 222 Citations , 32 Documents ,9 h-index.

Profile:  Scopus

Featured Publications

  1. Huang, Z., Wang, F., Chen, X., & Chang, C.-C. (2025). Revisiting “online/offline provable data possession” schemes. Computer Standards & Interfaces.
    Citations: 2

  2. Huang, Z., Wang, F., Chen, X., & Chang, C.-C. (2025). Reversible data hiding with secret encrypted image sharing and adaptive coding. IEEE Internet of Things Journal.
    Citations: 1

  3. Huang, Z., Wang, F., Chen, X., & Chang, C.-C. (2024). Efficient blockchain-based data aggregation scheme with privacy-preserving on the smart grid. IEEE Transactions on Smart Grid.