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112 records · Page 7

All sources NASA NTRS DOE OSTI

NASA NTRSDate not supplied

Zero-Trust Architecture for Autonomous Edge Computing

We are at the apex of an aviation revolution where autonomy will play a central role in enabling complex, multi-agent systems to communicate, interact, and collaborate on a myriad of applications spanning autonomous swarms to wild-fire management. Autonomy is not an absolute but rather a spectrum ranging from a system requiring significant human intervention to one requiring little to none [1]. For example, the extreme, in the case of an autonomous aircraft, is one that operates independently in the airspace interacting with all other elements (air traffic controllers, other pilots) as if it were a human pilot. Critical to this vision is an architecture that enables autonomous agents to interact with minimal latency. Edge computing is an emerging architecture where compute and storage is pushed to the ‘edge’ of the network in order to minimize the round-trip time from agent to resource thereby mitigating the latency associated with cloud-only based approaches. Additionally, services can generate massive amounts of data (e.g., video feeds), which may require analysis in near real-time. Moving this data to the cloud for further processing may not be feasible due to latency, bandwidth, and cost. Privacy, security, and reliability can also be improved by edge computing architectures. However, this geo-distributed and dynamic* architecture complicates the establishment of unambiguous network security boundaries and can lead to vulnerabilities including man in the middle attacks, replay attacks, physical security breaches of edge nodes, signal interception, etc. This motivates the need for zero-trust architectures [2–4] which de-emphasize the notion of static network perimeters and, as the name implies, do not instill any innate trust in any particular agent. It is required that all agents must be authorized and approved in every transaction. In this paper, we present a zero-trust architecture suitable for edge-computing applications that demand significant low-latency, security, privacy, and reliability.

zero trust↗

NASA NTRS2023

An Overview of Distributed Spacecraft Autonomy at NASA Ames

Autonomous decision-making significantly increases mission effectiveness by mitigating the effects of communication constraints, like latency and bandwidth, and mission complexity on multi-spacecraft operations. To advance the state of the art in autonomous Distributed Space Systems (DSS), the Distributed Spacecraft Autonomy (DSA) team at NASA's Ames Research Center is developing within five relevant technical areas: distributed resource and task management, reactive operations, system modeling and simulation, human-swarm interaction, and ad hoc network communications. DSA is maturing these technologies - critical for future large autonomous DSS - from concept to launch via simulation studies and orbital deployments. A 100-node heterogenous Processor-in-the-Loop (PiL) testbed aids distributed autonomy capability development and verification of multi-spacecraft missions. The DSA software payload deployed to the D-Orbit SCV-004 spacecraft demonstrates multi-agent reconfigurability and reliability as part of an ESA-sponsored in-orbit technology demonstration. Finally, DSA's primary flight mission showcases collaborative resource allocation for multipoint science data collection with four small spacecraft as a payload on NASA's Starling 1.0 satellites.

Caleb Ashmore Adams↗

DOE OSTI2019

MAVNet: Design of a Reliable Beyond Visual Line of Sight Communication System for Unmanned Vehicles

This paper details the design and system components of the Multi-Modal Autonomous Vehicle Network system (MAVNet). MAVNet was designed to provide reliable and redundant command, control and payload data distribution for unmanned vehicles operating beyond visual line of sight. Current industry solutions do not adequately solve key networking challenges, do not support network diversity and fall short of providing desirable next-generation requirements including data fusion from national air space data sources, multi-pilot and multi-agent control. The MAVNet system and the approach taken to solve these issues will be detailed in this paper.

Stinson, Brad J↗

DOE OSTI2024

A Distributed Model Identification Algorithm for Multi-Agent Systems: Preprint

In this study, we investigate agent-based approach for system model identification with emphasis on power distribution system applications. Departing from conventional practices of relying on historical data for offline model identification, we adopt online update approach utilizing real-time data by employing the latest data points for gradient computation. This methodology offers advantages including a large reduction in the communication network's bandwidth requirements by minimizing the data exchanged at each iteration and enabling the model to adapt in real-time to disturbances. Furthermore, we extend our model identification process from linear frameworks to more complex non-linear convex models. This extension is validated through numerical studies demonstrating improved control performance for a synthetic IEEE test case.

data-driven control↗