Collaborative Orbital Edge Intelligence: A Decentralized Paradigm for Energy-Efficient Computing in Space

2026-08-01

AI Summary

This study addresses critical challenges in existing low Earth orbit (LEO) satellite edge computing—namely limited connectivity, high latency, and excessive onboard battery consumption—stemming from the lack of decentralized collaboration among multiple providers. To overcome these limitations, this work proposes the Collaborative Orbital Edge Intelligence (COEI) paradigm, which establishes the first decentralized coordination framework supporting heterogeneous LEO constellations. By jointly integrating networking, computation, and power management, COEI enables globally resilient connectivity, real-time intelligence, and energy-efficient services. Key innovations include decentralized task offloading, energy-aware scheduling, multi-orbit collaborative computing, and deep battery discharge optimization. The proposed approach significantly improves task success rates while effectively reducing the maximum depth of discharge across the satellite swarm, thereby achieving substantial gains in both energy efficiency and quality of service.

Abstract

In recent years, Low Earth Orbit (LEO) satellites have been increasingly deployed to enable connectivity in remote and disaster-prone areas. Researchers have proposed Orbital Edge Computing, which adds computational intelligence to LEO satellites to process data on orbit, providing edge intelligence close to space data sources. Existing work on Orbital Edge Computing typically assumes centralized control without collaboration among satellites from different providers, leading to limited connectivity, higher latency, and increased satellite battery depletion. They have not fully explored decentralized inter-satellite collaboration for energy-efficient intelligence under heterogeneous LEO constellations. This paper introduces a novel paradigm, Collaborative Orbital Edge Intelligence (COEI), that leverages decentralized collaboration among LEO satellites to enable energy-efficient on-orbit processing of space data. We describe the overall system architecture of COEI, including the issues related to networking, computing, and power management. COEI can help create a multi-party, multi-orbit megaconstellation of satellites that delivers better service quality by providing benefits, including global resilient connectivity, real-time intelligence, and energy-efficient services. To demonstrate the COEI benefits, we conduct a case study on decentralized energy-aware satellite task offloading to maximize the task success rate while minimizing the sum of the maximum battery depth-of-discharge across all satellites. Finally, we outline future directions for COEI that offer opportunities for further investigation.

Problem

• Orbital Edge Computing
• Decentralized Collaboration
• Energy Efficiency
• LEO Constellations
• Satellite Intelligence

Innovation

• Collaborative Orbital Edge Intelligence
• decentralized satellite collaboration
• energy-efficient computing
• LEO megaconstellation
• on-orbit task offloading