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Bomin Mao

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Review 2026

Space Computing Power Networks: A Survey

Space Computing Power Networks (SCPNs), also termed as Satellite Comptuting Power Networks, as an integration of satellite networks, orbital computing, and terrestrial infrastructure, have been becoming an emerging architecture and attracting growing research attention during the past few years. Beyond meeting the differentiated intelligent communication, computing, and caching service requirements from users and terminals across space, air, ground, and sea, SCPNs hold significant importance for space exploration, earth observation, environment monitoring, remote user activities, and so on. There is no doubt that SCPNs will be the critical part of 6G to realize the ubiquitous and seamless intelligence. However, compared to traditional Terrestrial Computing Power Networks (TCPNs) and Satellite Computing Networks (SCNs), SCPNs holds the uniqueness, such as the cycled node movements, hierarchical network topology, extremely large network scalability, ubiquitous resource heterogeneity and constraints, and particular space computing environment. The system integration, protocol optimization, service orchestration, and sustainable operation of SCPN have inspired many meaningful research and projects. Considering existing survey papers mainly focus on scenarios of TCPNs carrying large-scale and complex computing tasks, this paper presents a comprehensive survey of state-of-the-art research on SCPN, covering various aspects ranging from system architecture, applications and challenges, and diversified Quality of Service (QoS) metric analysis and optimization. Finally, a number of potential future research directions have also been discussed to enlighten more innovative works.

Shi Chen, Y. Wu, Bomin Mao et al. · 0 citations
2026

Cooperative Covert Communication and Task Offloading for AI-Empowered Multi-UAV Networks

Multi-UAV edge networks, as an effective supplement to ground sensor systems, can significantly improve perception coverage and data processing efficiency. However, as UAV networks scale up, effective multi-UAV cooperation becomes increasingly critical and challenging, especially for coupled deployment, task allocation, and resource management. Meanwhile, due to the openness and broadcast nature of wireless channels, UAV transmissions are vulnerable to eavesdropping, making cooperative security protection essential for reliable UAV edge computing. To address these issues, this paper investigates a multi-UAV secure edge computing scenario in which UAVs cooperate both for self-jamming to thwart the aerial eavesdropper and for distributed edge computing to assist task processing. We establish the digital models of the UAV secure edge computing workflow and formulate the latency minimization problem under covert communication constraints. Then, a particle swarm optimization (PSO) + block coordinate descent (BCD) method for discrete state spaces and a multi-agent deep deterministic policy gradient (MADDPG)-based scheme suitable for continuous real-world environments are proposed, respectively. Extensive analysis and simulations demonstrate the effectiveness of our methods, achieving covert task offloading while significantly reducing task processing latency.

Xiaoyi Zhou, Hongzhi Guo, Bomin Mao et al. · 0 citations