Guest Editors
Prof. Peiying Zhang
Email: zhangpeiying@upc.edu.cn
Affiliation: Qingdao Institue of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, China
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Research Interests: mobile edge computing, deep reinforcement learning, space-air-ground integrated networks, resource allocation algorithm, artificial intelligence for networking
Summary
By integrating satellites, airborne platforms, and ground networks, the space-air-ground integrated network (SAGIN) overcomes the limitations of traditional ground infrastructure in remote and maritime areas. The paradigm shift toward 6G has shifted edge computing (EC) from ground-based infrastructure to SAGIN. Unlike traditional edge environments, EC in SAGIN faces unique challenges such as extreme node mobility, heterogeneous computing capabilities, and intermittent link stability. To achieve ubiquitous connectivity and seamless global coverage, intelligent strategies must be developed to manage distributed edge resources in these highly unstable environments.
Deep reinforcement learning (DRL) provides a powerful framework for solving complex, high-dimensional problems. Through real-time learning and dynamic optimization, DRL can significantly enhance task offloading, resource orchestration, trajectory control, and network security within the SAGIN architecture. It is crucial to explore how DRL can autonomously optimize key edge operations in SAGIN.
Both original research and reviews will be considered. The following subtopics are the particular interests of this special issue, including but not limited to:
· DRL for joint task offloading and resource allocation in SAGIN
· Network virtualization and slicing using DRL in SAGIN
· Multi-agent deep reinforcement learning for satellite-edge collaborative computing
· Dynamic trajectory control for a drone-assisted edge platform based on DRL
· Energy efficiency and green computing strategies for intelligent SAGIN
· DRL-based privacy protection solutions and secure edge computing in SAGIN
· Real-time traffic prediction and intelligent routing at the SAGIN edge
· Distributed DRL and federated frameworks in SAGIN
· Intelligent network telemetry and resource monitoring for SAGIN edge environments
Keywords
mobile edge computing, space-air-ground integrated networks, deep reinforcement learning, task offloading, edge intelligence, resource orchestration, distributed computing, traffic scheduling
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