NASA SBIR 2022-I Solicitation

Proposal Summary

Proposal Information

Proposal Number:
22-1- H9.07-1564
Subtopic Title:
Cognitive Communication
Proposal Title:
Configuration, Optimization and Management of DTN via Artificially Intelligent Decentralized Systems (COMMANDS)

Small Business Concern

EpiSys Science, Inc.
13025 Danielson Street, Suite 106, Poway, CA 92064
(858) 805-5608                                                                                                                                                                                

Principal Investigator:

Dr. Saeed Kaviani Kaviani
3086 Starry Night Dr, CA 92029 -
(858) 900-8857                                                                                                                                                                                

Business Official:

Epiphany Ryu
12234 Boulder View Drive, CA 92064 - 5337
(858) 513-1503                                                                                                                                                                                

Summary Details:

Estimated Technology Readiness Level (TRL) :                                                                                                                                                          
Begin: 4
End: 6
Technical Abstract (Limit 2000 characters, approximately 200 words):

We propose Configuration, Optimization and Management of DTN via Artificially Intelligent Decentralized Systems (COMMANDS). COMMANDS offers a decentralized AI-based solution to configure, optimize and manage communications for NASA’s lunar networks. It will provide significant improvements in throughput, end-to-end latency, overhead and resource consumption in delay/disruption tolerant networks (DTN) backed by our previous NASA’s SBIR program, LEARN. COMMANDS is based on our tactical AI design and builds a multi-agent deep reinforcement learning (MADRL) framework that enables the lunar network to sense, adapt, act and learn from its experiences and unknown environment autonomously. This automates and optimizes mission operation and resource efficiencies without necessitating involvement from a mission operations team. We propose to deploy graph-based machine learning that enables neural comprehension of all available network parameters and conditions into a node embedding. Next, our tactical AI uses a hybrid approach to combine expert systems and AI to bring robustness and intuition designs.

COMMANDS extends our Python-based simulation of DTN Lunar communication for the training, testing, verification. We further use the CORE network emulation tool to demonstrate the validation and performance gains of the COMMANDS as a novel network traffic management software service.

We propose an alternative approach for training graph neural network (GNN)-based design. To efficiently train GNN, first, we find the centralized solution to the network routing, resource allocation and control, bundle scheduling and fragmentation. The centralized solution will be formulated as mixed-integer linear programming (MILP). We propose to solve neural combinatorial optimization (NCO) to solve this centralized problem. The outcome is a centralized solution, which will help us in the design of a decentralized GNN-based solution.

Potential NASA Applications (Limit 1500 characters, approximately 150 words):

COMMANDS technologies, when fully developed and demonstrated, will offer commercially viable, cost-effective delay-tolerant networking solutions to deploy intelligent overlay network solutions over commercial communication services for future space missions, such as Artemis. In addition, they will substantially reduce the operational complexity for mission engineers by automating, with high confidence, various tasks of planning, scheduling and managing communication resources across multiple heterogeneous networking assets.

Potential Non-NASA Applications (Limit 1500 characters, approximately 150 words):

Space-based broadband internet services such as Starlink and Amazon Kuiper demand terrestrial-grade service quality despite many spacecraft participating in data forwarding. We expect our AI-based DTN technology to further advance such systems by seamlessly allowing the addition of new communication assets, e.g. unmanned aerial systems, to quickly adapt to varying user demands with little planning

Duration:     6

Form Generated on 05/25/2022 15:49:13