Xin Fan | Physical Layer Security | Best Researcher Award

Published on by Innovation Technologist

Assist. Prof. Dr. Xin Fan | Physical Layer Security | Best Researcher Award

Assistant Professor at Beijing Forestry University, China

Dr. Xin Fan is an Assistant Professor at the School of Information Science and Technology, Beijing Forestry University, Beijing, China. He has a strong academic background, having completed his B.E., M.E., and Ph.D. in Electronic and Information Engineering at Beijing Jiaotong University. His expertise spans wireless communications, machine learning, security and privacy, optimization, statistical signal processing, and blockchain technologies. With a visiting research experience at George Mason University, Dr. Fan has made significant contributions to edge intelligence through innovative joint optimization methods for wireless communication and airborne federated learning.

Publication Profile 

Scopus

Educational Background 🎓

  • Bachelor’s Degree (B.E.): Electronic and Information Engineering, Beijing Jiaotong University, China (2016)
  • Master’s Degree (M.E.): Electronic and Information Engineering, Beijing Jiaotong University, China (2018)
  • Doctoral Degree (Ph.D.): Electronic and Information Engineering, Beijing Jiaotong University, China (2023)
  • Visiting Ph.D. Student: Electrical and Computer Engineering, George Mason University, USA (2020-2022)

Professional Experience 💼

  • Assistant Professor: School of Information Science and Technology, Beijing Forestry University, Beijing, China.
  • Guest Editor: Electronics Journal.
  • IEEE MILCOM Technical Program Committee (TPC) Member.
  • Participated in five consultancy/industry projects and completed nine research projects.

Research Interests 🔬

  • Wireless communications
  • Machine learning
  • Security and privacy
  • Optimization
  • Statistical signal processing
  • Blockchain

Awards and Honors🏆✨

  • Nominee for the “Best Researcher Award” at the Global Innovation Technologist Awards.
  • IEEE Member and IEEE Communication Society Member.
  • CIC (China Institute of Communications) Member and CIE (Chinese Institute of Electronics) Member.

Contributions

Dr. Xin Fan has significantly advanced the field of wireless communications and machine learning. His work focuses on proposing joint optimization methods for wireless communication and airborne federated learning, promoting edge intelligence. He has authored 19 SCI-indexed journal papers and numerous conference publications in prestigious platforms such as IEEE IoT-J, IEEE TWC, IEEE TCCN, IEEE ICC, and IEEE Globecom. He has also contributed to nine patents, demonstrating his commitment to innovation.

Conclusion🌟

Dr. Xin Fan is a dedicated researcher and academic with a robust background in wireless communications and machine learning. His innovations in joint optimization and edge intelligence highlight his contributions to advancing technology in wireless communication systems. With 445 citations, editorial appointments, and memberships in prestigious professional organizations, Dr. Fan continues to make a lasting impact in his field.

Publications 📚

📄 Article in Press
Self-Learning Based Dependable Offloading Optimization in Semi-Trusted Vehicular Edge Computing and Networks
Li, X., Jing, T., Li, R., … Huo, Y., Yu, F.R.
📕 IEEE Transactions on Vehicular Technology, 2025
🔗 Abstract: [Unavailable]
🔗 Related Documents: [Unavailable]
🔢 Citations: 0

📘 Conference Paper
3D Physical Layer Secure Transmission for UAV-Assisted Mobile Communications Without Locations of Eavesdroppers
Yu, W., Li, J., Fan, X., … Hong, Y., Chen, T.
📕 Lecture Notes in Computer Science (LNCS), 2025, 14998 LNCS, pp. 355–366
🔗 Abstract: [Unavailable]
🔗 Related Documents: [Unavailable]
🔢 Citations: 0

🌐 Open Access Article
CB-DSL: Communication-Efficient and Byzantine-Robust Distributed Swarm Learning on Non-i.i.d. Data
Fan, X., Wang, Y., Huo, Y., Tian, Z.
📕 IEEE Transactions on Cognitive Communications and Networking, 2024, 10(1), pp. 322–334
🔗 Abstract: [Unavailable]
🔗 Related Documents: [Unavailable]
🔢 Citations: 4

📄 Article
UAV-Assisted Multi-Object Computing Offloading for Blockchain-Enabled Vehicle-to-Everything Systems
Chen, T., Wang, S., Fan, X., … Luo, C., Hong, Y.
📕 Computers, Materials and Continua, 2024, 81(3), pp. 3927–3950
🔗 Abstract: [Unavailable]
🔗 Related Documents: [Unavailable]
🔢 Citations: 0

📄 Article in Press
DT-Driven Computation Offloading for Edge Computing in IIoT with RIS-Assisted Multi-UAVs
Luo, C., Zhao, S., Sun, Q., … Sun, G., Zhang, L.
📕 IEEE Internet of Things Journal, 2024
🔗 Abstract: [Unavailable]
🔗 Related Documents: [Unavailable]
🔢 Citations: 0

🔒 Secure Transmission Article
Secure Transmission Scheme for Blocks in Blockchain-Based Unmanned Aerial Vehicle Communication Systems
Chen, T., Jiang, S., Fan, X., … Luo, C., Hong, Y.
📕 Computers, Materials and Continua, 2024, 81(2), pp. 2195–2217
🔗 Abstract: [Unavailable]
🔗 Related Documents: [Unavailable]
🔢 Citations: 0

📑 Article
Two-Stage Offloading for Enhancing Distributed Vehicular Edge Computing and Networks: Model and Algorithm
Li, X., Jing, T., Wang, X., … Li, X., Richard Yu, F.
📕 IEEE Transactions on Intelligent Transportation Systems, 2024, 25(11), pp. 17744–17761
🔗 Abstract: [Unavailable]
🔗 Related Documents: [Unavailable]
🔢 Citations: 2

🎓 Conference Paper
GANFed: GAN-Based Federated Learning with Non-IID Datasets in Edge IoTs
Fan, X., Wang, Y., Zhang, W., … Cai, Z., Tian, Z.
📕 IEEE International Conference on Communications, 2024, pp. 5443–5448
🔗 Abstract: [Unavailable]
🔗 Related Documents: [Unavailable]
🔢 Citations: 0

🌟 Open Access Article
Distributed Swarm Learning for Edge Internet of Things
Wang, Y., Tian, Z., Fan, X., … Nowzari, C., Zeng, K.
📕 IEEE Communications Magazine, 2024, 62(11), pp. 160–166
🔗 Abstract: [Unavailable]
🔗 Related Documents: [Unavailable]
🔢 Citations: 2

📚 Conference Paper
1-Bit Compressive Sensing for Efficient Federated Learning Over the Air
Fan, X., Wang, Y., Huo, Y., Tian, Z.
📕 IEEE Transactions on Wireless Communications, 2023, 22(3), pp. 2139–2155
🔗 Abstract: [Unavailable]
🔗 Related Documents: [Unavailable]
🔢 Citations: 16

 

 

 

Qasem Abu Al-Haija | Defensive Security | Best Researcher Award

Published on by Innovation Technologist

Dr. Qasem Abu Al-Haija | Defensive Security | Best Researcher Award

Department of Cybersecurity at Jordan University of Science and Technology, Jordan

🔍 Dr. Qasem S. Abu Al-Haija is an accomplished educator and researcher in the field of cybersecurity and IoT. With a passion for developing intelligent detection systems and efficient cryptographic methods, he strives to advance the landscape of technology. 🌐✨ His commitment to teaching and mentoring the next generation of engineers is matched only by his dedication to impactful research. 📚🔒

Publication Profile : 

Scopus

🎓 Educational Background :

Dr. Qasem S. Abu Al-Haija holds a PhD in Computer and Information Systems Engineering from Tennessee State University (2020) with a perfect GPA of 4.00. His dissertation focused on intelligent IoT attack detection using non-traditional machine learning methods. He also earned a Master’s degree in Computer Engineering from Jordan University of Science and Technology (2009), specializing in efficient algorithms for ECC cryptography, and a Bachelor’s degree in Electrical and Computer Engineering from Mu’tah University (2005).

💼 Professional Experience :

Dr. Abu Al-Haija is currently an Assistant Professor in the Department of Cybersecurity at Jordan University of Science and Technology, where he conducts research in AI and cybersecurity. Previously, he held similar positions at Princess Sumaya University for Technology and the University of Petra, teaching various cybersecurity and data science courses. His extensive experience includes postdoctoral research at Tennessee State University and a lecturer role at King Faisal University, where he taught numerous engineering courses and supervised many capstone projects. Throughout his career, he has contributed to various funded research projects and has actively engaged in academic committees and training programs.

📚 Research Interests : 

His research interests encompass machine learning, cybersecurity, IoT/CPS modeling, and embedded systems, aiming to enhance the security and efficiency of interconnected systems.

📝 Publication Top Notes :

  • Q. Abu Al-Haija, M. Al Fayoumi, “An intelligent identification and classification system for malicious uniform resource locators (URLs),” Neural Computing and Applications (NCAA), Springer, 2023.
  • Q. Abu Al-Haija, M. AlOhaly, M. Odeh, “A Lightweight Double-Stage Scheme to Identify Malicious DNS over HTTPS Traffic Using a Hybrid Learning Approach,” Sensors, MDPI, 2023.
  • Q. Abu Al-Haija, A. Al Badawi, “High-performance intrusion detection system for networked UAVs via deep learning,” Neural Computing and Applications (NCAA), Springer, 2022.
  • Q. Abu Al-Haija, A. Al Badawi, “Boost-Defense for Resilient IoT Networks: A Head-to-Toe Approach,” Expert Systems, Wiley, 2022.
  • Q. Abu Al-Haija, “Top-Down Machine Learning Based Architecture for Cyberattacks Identification and Classification in IoT Communication Networks,” Frontiers in Big Data: Cybersecurity and Privacy, Frontiers, 2022.
  • Q. Abu Al-Haija, M. Krichen, W. Abu Elhaija, “Machine-Learning-Based Darknet Traffic Detection System for IoT Applications,” Electronics, MDPI, Vol. 11(4), 2022.
  • S. Zidi, A. Mihoub, S. Qaisar, M. Krichen, Q. Abu Al-Haija, “Theft detection dataset for benchmarking and machine learning based classification in a smart grid environment,” Journal of King Saud University-Computer and Information Sciences, Elsevier, In Press, 2022.
  • Altamimi, S., Abu Al-Haija, Q. “Maximizing intrusion detection efficiency for IoT networks using extreme learning machine,” Discover Internet of Things, 2024, 4(1), 5. [Open access]
  • Alsulami, A.A., Abu Al-Haija, Q., Alturki, B., Alghamdi, B., Alsemmeari, R.A. “Exploring the efficacy of GRU model in classifying the signal to noise ratio of microgrid model,” Scientific Reports, 2024, 14(1), 15591. [Open access]
  • Abu Al-Haija, Q., Altamimi, S., AlWadi, M. “Analysis of Extreme Learning Machines (ELMs) for intelligent intrusion detection systems: A survey,” Expert Systems with Applications, 2024, 253, 124317. [Review]
  • Al-Fayoumi, M., Alhijawi, B., Al-Haija, Q.A., Armoush, R. “XAI-PhD: Fortifying Trust of Phishing URL Detection Empowered by Shapley Additive Explanations,” International Journal of Online and Biomedical Engineering, 2024, 20(11), pp. 80–101.
  • Khalil, M., Al-Haija, Q.A. “Ethical machine learning for internet of things network,” in Ethical Artificial Intelligence in Power Electronics, 2024, pp. 12–20. [Book Chapter]
  • Al-Haija, Q.A. “Preface,” in Smart and Agile Cybersecurity for IoT and IIoT Environments, 2024, pp. viii–xii. [Editorial]
  • Ayyad, W.R., Al-Haija, Q.A., Al-Masri, H.M.K. “Human factors in cybersecurity,” in Smart and Agile Cybersecurity for IoT and IIoT Environments, 2024, pp. 235–256. [Book Chapter]
  • Al-Haija, Q.A. Smart and Agile Cybersecurity for IoT and IIoT Environments, 2024, pp. 1–397. [Book]
  • Al-Tamimi, S.A., Al-Haija, Q.A. “Supply chain security, technological advancements, and future trends,” in Smart and Agile Cybersecurity for IoT and IIoT Environments, 2024, pp. 211–234. [Book Chapter]
  • Saif, A., Al-Haija, Q.A. “Artificial Intelligence (AI)-powered internet of things (IoT): Smartening Up IoT,” in Future Communication Systems Using Artificial Intelligence, Internet of Things and Data Science, 2024, pp. 18–29. [Book Chapter]