Ihab Nassra | 5G-IoT | Best Researcher Award

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Dr. Ihab Nassra | 5G-IoT | Best Researcher Award

Dr. Ihab Nassra | Universitat Politècnica de València | Spain

Dr. Ihab Nassra is an accomplished researcher with a strong focus on Internet of Things (IoT), wireless body sensor networks, data compression techniques, and smart healthcare systems. His work on data compression for IoT-enabled wireless body sensor networks has systematically reviewed current methodologies and proposed research trends aimed at improving quality of service (QoS), demonstrating a deep understanding of network optimization and resource management. He has also contributed to the advancement of information retrieval systems, particularly in Arabic language processing, where his studies on Arabic stemmers have enhanced recall and retrieval efficiency. In addition, Dr. Nassra has explored software security through innovative approaches such as Dynamic Multi-Level Java Code Obfuscation Techniques, reflecting his commitment to protecting digital systems against threats. His research on sensor networks for smart hospitals and smart body area networks emphasizes the practical application of technology to improve healthcare monitoring, patient care, and data management. Through a combination of high-quality publications, interdisciplinary approaches, and focus on applied research, his work bridges the gap between theoretical development and real-world implementation. Dr. Nassra’s contributions have not only advanced knowledge in his primary fields but also addressed critical societal challenges, including healthcare efficiency, secure information management, and optimized network performance, establishing him as a leading figure in contemporary IoT and smart systems research.

Profile: Scopus | ORCID | Google Scholar

Featured Publications

Nassra, I., & Capella, J. V. (2023). Data compression techniques in IoT-enabled wireless body sensor networks: A systematic literature review and research trends for QoS improvement. Internet of Things, 23, 100806.

Nasra, I., & Maree, M. (2017). On the use of Arabic stemmers to increase the recall of information retrieval systems. In 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD).

Adwan, Y., Yasin, A., & Nassra, I. (2016). Dynamic Multi Levels Java Code Obfuscation Technique (DMLJCOT). International Journal of Computer Science and Information Security, 5

Nguyen, T. A., & Nassra, I. (2025). Sensor networks for smart hospitals. Elsevier.

Nassra, I., & Capella, J. V. (2025). Smart body area networks. In Sensor Networks for Smart Hospitals (pp. 41–75).

Amin Nazari | Internet of Things | Best Researcher Award

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Mr. Amin Nazari | Internet of Things | Best Researcher Award

Ph.D. Candidate, Bu-Ali Sina University, Iran

Mr. Amin Nazari is a highly accomplished researcher and final-year Ph.D. candidate in Artificial Intelligence at Bu-Ali Sina University, specializing in the Internet of Things (IoT), intelligent networks, graph neural networks and recommender systems. He holds an M.Sc. in Computer Engineering from Arak University (2014) and a B.Sc. in Computer Engineering from Islamic Azad University of Hamedan (2012), with theses focused on energy-aware routing and wireless sensor networks. With over five years of academic and research experience, Mr. Amin Nazari has authored 15 peer-reviewed publications in reputed Q1/Q2 journals and conferences (Elsevier, Springer, IEEE, Wiley), achieving 238 citations, 15 Scopus-indexed documents and an h-index of 8. His professional engagements include teaching courses in data mining, software engineering, programming and database design at Bu-Ali Sina University and Technical and Vocational University. His research interests span IoT and SDN-based intelligent networks, multimodal deep learning for financial forecasting and natural language processing for information retrieval, with practical projects such as decentralized orchestration systems, cryptocurrency forecasting models and AI-driven recommender platforms in collaboration with industry partners. He possesses strong technical skills in Python, MATLAB, Java, C++ and R, with expertise in advanced AI/ML frameworks including PyTorch, TensorFlow and Scikit-learn. Mr. Amin Nazari has received multiple recognitions, notably Top Researcher of Hamadan Province (2023, 2024) and Top Student of Bu-Ali Sina University (2023, 2024), alongside professional certifications from Coursera and Stanford. With a proven record of impactful publications, academic leadership and industry collaboration, he is strongly positioned to make significant future contributions to AI and IoT research at both national and international levels.

Profile: Scopus | ORCID | Google Scholar

Featured Publications

Javanmardi, S., Shojafar, M., Mohammadi, R., Nazari, A., Persico, V., & … (2021). FUPE: A security driven task scheduling approach for SDN-based IoT–Fog networks. Journal of Information Security and Applications, 60, 102853.

Samadi, R., Nazari, A., & Seitz, J. (2023). Intelligent energy-aware routing protocol in mobile IoT networks based on SDN. IEEE Transactions on Green Communications and Networking, 7(4), 2093–2103.

Khaledian, N., Nazari, A., Khamforoosh, K., Abualigah, L., & Javaheri, D. (2023). TrustDL: Use of trust-based dictionary learning to facilitate recommendation in social networks. Expert Systems with Applications, 228, 120487.

Mohammadi, R., Nazari, A., Nassiri, M., & Conti, M. (2021). An SDN-based framework for QoS routing in internet of underwater things. Telecommunication Systems, 78(2), 253–266.

Akraminejad, R., Khaledian, N., Nazari, A., & Voelp, M. (2024). A multi-objective crow search algorithm for optimizing makespan and costs in scientific cloud workflows (CSAMOMC). Computing, 106(6), 1777–1793.