Collaborative Air-Ground Sensing, Communication, Computing, Storage, and Intelligence for Low-Altitude Economy

2026-05-18

AI Summary

This work addresses the stringent safety and compliance demands of low-altitude economic operations, which cannot be adequately supported by traditional communication-centric architectures due to their diverse, task-driven requirements. The paper proposes a novel task-driven, closed-loop air-ground collaborative architecture that, for the first time, unifies application scenarios, a demand-resource coupling matrix, and the collaborative framework into a single model. This enables end-to-end joint orchestration and online decision-making across sensing, communication, computing, storage, and intelligence (SCCSI) resources. By integrating multi-resource coupling modeling, task-oriented optimization, and dynamic scheduling algorithms, the study establishes a comprehensive methodological toolkit, testbed, and evaluation framework. The effectiveness and scalability of the proposed architecture are validated through representative use cases.

Abstract

Low-altitude economy (LAE) is transforming low-altitude airspace into a new cyber-physical infrastructure. Although air-ground communications have been widely studied, LAE is fundamentally different in the sense that it is mission-centric with diverse requirements, such as stringent safety and compliance constraints not be effectively addressed with a communication-centric design alone, which makes air-ground collaboration indispensable: Only through effectively coordinating air-ground infrastructure and resources can LAE missions be fulfilled. Consequently, LAE calls for task-driven, closed-loop, multi-resource orchestration of Sensing, Communication, Computing, Storage, and Intelligence (SCCSI), where key decisions must be co-designed under mobility and uncertainty. In this paper, we first present a novel framework that connects (i) LAE scenarios and a requirement--resource coupling matrix, (ii) an air--ground collaborative architecture, and (iii) methodological toolboxes for SCCSI co-optimization and online decision-making. We then systematically review enabling technologies for collaborative SCCSI resources and capabilities, emphasizing their coupling and end-to-end tradeoffs. Finally, we summarize testbeds, datasets, and evaluation metrics, and provide representative use cases to illustrate how the proposed framework translates application requirements into practical task-driven optimization designs, together with open challenges and a roadmap toward scalable and trustworthy LAE deployment.

Problem

• Low-Altitude Economy
• Air-Ground Collaboration
• SCCSI Orchestration
• Task-Driven Optimization
• Cyber-Physical Infrastructure

Innovation

• Collaborative Air-Ground SCCSI
• Task-Driven Orchestration
• Requirement–Resource Coupling
• Low-Altitude Economy
• Co-Optimization