[PDF] Orbital Edge Computing: Nanosatellite Constellations as a New Class of Computer System | Semantic Scholar (2024)

This article comprehensively surveys the significant areas of focus in orbital edge computing, which include protocol optimization, mobility management, and resource allocation and provides the first comprehensive survey of OEC.

The vision and challenges for satellite computing are presented based on a brief survey of the very recent literature in the “NewSpace” era and a case study of an open research platform on real satellites named Tiansuan constellation is given.

Kodan is presented: an OEC system that maximizes the utility of saturated satellite downlinks while mitigating the computational bottleneck and increases the data value density between 89 and 97 percent.

This paper designs a domain-specific method to characterize key components in Starlink's autonomous driving from various public space situational awareness datasets, including continuous orbit maintenance, collision avoidance, and maneuvers between orbital shells.

This work seeks to fully exploit the scarce satellite computation and communication resources to achieve satellite-ground collaboration and presents a satellite-ground collaborative system named TargetFuse for onboard object detection, which incorporates a combination of techniques to minimize detection errors under energy and bandwidth constraints.

The first measurement study on the thermal control, power management, and performance of COTS computing devices on satellites is presented, revealing that the satellite platform and COTS computing devices significantly interplay in terms of the temperature and energy, forming the main constraints on satellite computing.

This paper explores the feasibility and performance of a novel approach that leverages emerging low Earth orbit (LEO) constellations to enable low-latency and scalable EO data delivery from space, and presents OrbitCast, a hybrid EOData delivery architecture upon LEOconstellations and geo-distributed ground stations to forward EO Data from the source remote sensing satellite to a collection of end users.

This work develops an online energy scheduling algorithm within an online convex optimization framework that achieves sub-linear regret and the constraint violation asymptotically approaches zero and can reduce the depth of discharge significantly.

A new end-to-end scheduler system called Umbra is presented, which plans transfers from large satellite constellations through ground stations to the cloud, by accounting for both spatial and temporal factors, i.e., orbital dynamics, bandwidth constraints, and queue sizes.

This paper proposes an OEC Task Allocation algorithm based on the greedy strategy in LEO satellite networks for the Walker Delta satellite constellation, which fully utilizes satellite computing resources to provide services to ground users and analyzes the performance in terms of computational cost.

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This work defines and characterize Orbital Edge Computing, and describes power and software optimizations for the orbital edge, and uses formation flying to parallelize computation in space.

Techniques, insights and lessons learned from calibrating and monitoring ADCS subsystems at scale are shared and designed to enable agile decision making.

Missions with multiple small satellites can deliver a comparable or greater mission capability than a monolithic satellite, but with significantly enhanced flexibility, adaptability, scalability, evolvability, and maintainability and robustness.

Throughout this paper, what Spire has undertaken to get a commercial satellite constellation up and running is walked through, to enable both scale and rapid technology iteration, the design processes and systems have evolved to be lean but reliable.

An overview of micro-propulsion technologies that have been developed or are currently being developed for CubeSats is provided and operating principles and key design considerations for each class of propulsion system are outlined.

A new set of nonvolatility-exploiting optimizations are proposed and embody them in the NEOFog system architecture, showing how nonvolatile processing and non-volatile RF support alter the benefits of computation and communication-centric approaches.

An overview of the EO-1 mission, including the several operational phases which have characterized its lifetime is provided, and the spin-off technologies, including disaster monitoring and new Web-based tools which can be adapted for use in future missions are given.

Neurosurgeon, a lightweight scheduler to automatically partition DNN computation between mobile devices and datacenters at the granularity of neural network layers is designed, finding that a fine-grained, layer-level computation partitioning strategy based on the data and computation variations of each layer within a DNN has significant latency and energy advantages over the status quo approach.

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