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.
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.