Mohammed Seid Abegaz
Université du Québec à Montréal, Hamad bin Khalifa University
Research areas: Computer Science · Computer Networks and Communications · IoT and Edge/Fog Computing · UAV Applications and Optimization · Satellite Communication Systems
0000-0002-3672-6132
About
Dr Abegaz Mohammed Seid received his BSc and MSC in Computer Science from Ambo University, Ethiopia in 2010 and Addis Ababa University, Ethiopia in 2015, respectively. He received a Ph.D. degree in Computer Science and Technology from the University of Electronic Science and Technology of China (UESTC) in 2021. He is currently a post-doctoral fellow with the School of Mathematics and Computer Science, Department of Computer Science and Engineering at Zhejiang Normal University, China.
Dr. Abegaz has published seven scientific conferences and journal papers. His research interests include a wireless network, mobile edge computing, blockchain, machine learning, Vehicular network, IoT, Fog computing, UAV Network, IoT, and 5G/6G wireless network.
Employment
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University of Quebec in Montreal Researcher on AI-native 6G2026 - Present
Education
Education history is unavailable.
Projects & Funding
Projects & funding information is unavailable.
Publications (40)
- A Hierarchical MAFDRL-Based Resource Allocation and Incentive Mechanism for TN-NTN in 6G Networks Save
- Multi-Agent DRL for Queue-Aware Task Offloading in Hierarchical MEC-Enabled Air-Ground Networks Save
- A Multi-Agent DRL-Based Dynamic Resource Allocation in O-RAN-Enabled TN-NTN Metaverse Services Save
- Blockchain-Empowered Resource Allocation in HAPS-Assisted IoV Digital Twin Networks: A Federated DRL Approach Save
- Multiagent DRL-Based Demand Response Optimization for IoT-Based Smart Home Energy Management Systems Save
- Blockchain-Empowered Multi-Domain Resource Trading in TN-NTN 6G Networks: A Hierarchical Multi-Agent DRL Approach Save
- PACE: Reputation-Driven Hierarchical Incentive Mechanism for Trusted Healthcare Transactions in Model Marketplace Save
- Dynamic Charging and Path Planning for UAV-Powered Rechargeable WSNs Using Multi-Agent Deep Reinforcement Learning Save
- Lyapunov-Guided Long-Term Fairness-Aware Federated Learning for Collaborative TinyML on Edge Devices Save
- Task Offloading Optimization in Digital Twin Assisted MEC-Enabled Air–Ground IIoT 6G Networks Save
- Multi-Agent Deep Reinforcement Learning-Based Task Scheduling and Resource Sharing for O-RAN-Empowered Multi-UAV-Assisted Wireless Sensor Networks Save
- ASMCC+: A Secure Authentication Scheme for Mobile Cloud Computing Environment Based on Zero Trust Architecture Save
- Meta Reinforcement Learning for UAV-Assisted Energy Harvesting IoT Devices in Disaster-Affected Areas Save
- Multi-Agent DRL-Based Energy Harvesting for Freshness of Data in UAV-Assisted Wireless Sensor Networks Save
- Novel Approach for Curbing Unfair Energy Consumption and Biased Model in Federated Edge Learning Save
- Truthful Incentive Mechanism Design via Internalizing Externalities and LP Relaxation for Vertical Federated Learning Save
- Multiagent Federated Reinforcement Learning for Resource Allocation in UAV-Enabled Internet of Medical Things Networks Save
- Actor-Aware Self-Supervised Learning for Semi-Supervised Video Representation Learning Save
- Blockchain-Empowered Resource Allocation in Multi-UAV-Enabled 5G-RAN: A Multi-Agent Deep Reinforcement Learning Approach Save
- Fair Selection of Edge Nodes to Participate in Clustered Federated Multitask Learning Save