Peng Yingsheng | Edge Computing | Best Researcher Award

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Dr. Peng Yingsheng | Edge Computing | Best Researcher Award

Dr. Peng Yingsheng | Sun Yat-sen University | China

Dr. Peng Yingsheng is an emerging researcher whose work significantly advances the fields of wireless communications, UAV-assisted networks, and artificial intelligence-enabled edge computing. His research primarily focuses on developing intelligent optimization frameworks that enhance network performance, data delivery efficiency, and resource allocation in next-generation communication systems. A key element of his research is the application of deep reinforcement learning for real-time decision-making, particularly in UAV path planning and freshness-aware communication, enabling efficient mobile edge computing support for dynamic and heterogeneous IoT environments. His publication record demonstrates high scientific relevance, featuring strong contributions such as deep reinforcement learning-based path planning approaches that improve task execution and communication reliability, as well as innovative solutions for Age of Information (AoI) aware networking that ensure timely data transmission in mobile systems. Dr. Peng has also contributed to energy optimization strategies for digital twin-assisted edge networks, highlighting his insights into enabling sustainable and resource-efficient wireless ecosystems. His recent work extends into NOMA-based wireless powered cognitive radio networks through multi-agent learning strategies, showcasing his commitment to intelligent resource control in challenging spectral environments. Additionally, he has explored hybrid non-orthogonal multiple access techniques to improve the performance of wirelessly powered Internet of Things networks, reinforcing his expertise in emerging IoT communication strategies. Overall, Dr. Peng’s research delivers practical and forward-thinking solutions aligned with global technological transitions toward 6G, smart computing environments, and autonomous networking systems. His contributions reflect strong innovation, growing scholarly influence, and clear potential for leadership in advanced wireless communication research.

Profile: Google Scholar

Featured Publications

Peng, Y., Liu, Y., & Zhang, H. (2021). Deep reinforcement learning based path planning for UAV-assisted edge computing networks. Proceedings of the 2021 IEEE Wireless Communications and Networking Conference (WCNC), 1–6.

Peng, Y., Liu, Y., Li, D., & Zhang, H. (2022). Deep reinforcement learning based freshness-aware path planning for UAV-assisted edge computing networks with device mobility. Remote Sensing, 14(16), 4016.

He, T., Peng, Y., Liu, Y., & Song, H. (2024). AoI-oriented resource allocation for NOMA-based wireless powered cognitive radio networks based on multi-agent deep reinforcement learning. IEEE Access, 12, 69738–69752.

Peng, Y., Duan, J., Zhang, J., Li, W., Liu, Y., & Jiang, F. (2024). Stochastic long-term energy optimization in digital twin-assisted heterogeneous edge networks. IEEE Journal on Selected Areas in Communications.

Qi, H., Peng, Y., & Zhang, H. (2022). Performance analysis for wireless-powered IoT networks with hybrid non-orthogonal multiple access. Journal of Smart Environments and Green Computing, 2(3), 105–125.

Dhulfiqar Zoltán Alwahab | Edge Computing | Best Researcher Award

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Assoc. Prof. Dr. Dhulfiqar Zoltán Alwahab | Edge Computing | Best Researcher Award

Assoc. Prof. Dr. Dhulfiqar Zoltán Alwahab | Obuda University | Hungary

Assoc. Prof. Dr. Dhulfiqar Zoltán Alwahab is an active researcher with 134 citations across 100 documents, producing 28 scholarly publications and holding an h-index of 8. His research spans computer networks, cybersecurity, distributed systems, big data, IoT, cloud computing, and advanced networking technologies, with notable contributions to network automation and programmable network environments. He has participated in multiple research and development initiatives involving P4Edge, UNKP-ELTE-IK, and EFOP-ELTE-IK, contributing to innovative solutions in software-defined networking, Linux systems, and Dev Net-based architectures. His work includes journal and conference publications addressing emerging Internet technologies, intelligent network management, and data-driven optimization methods. He has been involved in several academic and applied research projects, including training-oriented technology programs in Python, AWS, big data, and IoT systems, enriching both industry and academic communities. His research outputs demonstrate meaningful advancements in network security, digital infrastructure design, and scalable cloud services. He has also contributed through instructional roles supporting research capacity building in networking technologies and open-source systems. His achievements reflect significant research engagement, impactful citations, and ongoing contributions to contemporary computing and networking innovations.

Profile: Scopus | ORCID | Google Scholar 

Featured Publications:

Ali, T. E., Ali, F. I., Dakić, P., & Zoltan, A. D. (n.d.). Trends, prospects, challenges, and security in the healthcare internet of things. Computing, 107(1), 28.

Alwahab, D. A., & Laki, S. (n.d.). A simulation-based survey of active queue management algorithms. Proceedings of the 6th International Conference on Communications and Networking.

Biró, C., & Kusper, G. (n.d.). Equivalence of strongly connected graphs and black-and-white 2-SAT problems. Miskolc Mathematical Notes, 19(2), 755–768.

Zaghar, D. (n.d.). Simplified the QoS factor for the ad-hoc network using fuzzy technique. International Journal of Communications, Network and System Sciences.

AlWahab, D. A., Gombos, G., & Laki, S. (n.d.). On a deep q-network-based approach for active queue management. Joint European Conference on Networks and Communications & 6G Summit.