SatAIOps: Revamping the Full Life-Cycle Satellite Network Operations
- URL: http://arxiv.org/abs/2305.08722v2
- Date: Wed, 17 May 2023 17:59:50 GMT
- Title: SatAIOps: Revamping the Full Life-Cycle Satellite Network Operations
- Authors: Peng Hu
- Abstract summary: Non-geostationary (NGSO) satellite networks provide high-quality Internet connectivity to any place on Earth.
Traditional approach to satellite operations cannot address the new challenges in the NGSO satellite networks.
This paper proposes a novel approach called "SatAIOps" as an overall solution.
- Score: 9.368986073388813
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recently advanced non-geostationary (NGSO) satellite networks represented by
large constellations and advanced payloads provide great promises for enabling
high-quality Internet connectivity to any place on Earth. However, the
traditional approach to satellite operations cannot address the new challenges
in the NGSO satellite networks imposed by the significant increase in
complexity, security, resilience, and environmental concerns. Therefore, a
reliable, sustainable, and efficient approach is required for the entire
life-cycle of satellite network operations. This paper provides a timely
response to the new challenges and proposes a novel approach called "SatAIOps"
as an overall solution. Through our discussion on the current challenges of the
advanced satellite networks, SatAIOps and its functional modules in the entire
life-cycle of satellites are proposed, with some example technologies given.
SatAIOps provides a new perspective for addressing operational challenges with
trustworthy and responsible AI technologies. It enables a new framework for
evolving and collaborative efforts from research and industry communities.
Related papers
- A Sharded Blockchain-Based Secure Federated Learning Framework for LEO Satellite Networks [4.034610694515541]
Low Earth Orbit (LEO) satellite networks are increasingly essential for space-based artificial intelligence (AI) applications.
As commercial use expands, LEO satellite networks face heightened cyberattack risks.
We propose a sharded blockchain-based federated learning framework for LEO networks, called SBFL-LEO.
arXiv Detail & Related papers (2024-11-09T10:22:52Z) - SatFed: A Resource-Efficient LEO Satellite-Assisted Heterogeneous Federated Learning Framework [19.59862482196897]
We propose SatFed, a resource-efficient satellite-assisted heterogeneous FL framework.
SatFed implements freshness-based model prioritization queues to optimize the use of highly constrained satellite-ground bandwidth.
Experiments with real-world LEO satellite networks demonstrate that SatFed achieves superior performance and robustness compared to state-of-the-art benchmarks.
arXiv Detail & Related papers (2024-09-20T13:44:00Z) - A Distance Similarity-based Genetic Optimization Algorithm for Satellite Ground Network Planning Considering Feeding Mode [53.71516191515285]
The low transmission efficiency of the satellite data relay back mission has become a problem that is currently constraining the construction of the system.
We propose a distance similarity-based genetic optimization algorithm (DSGA), which considers the state characteristics between the tasks and introduces a weighted Euclidean distance method to determine the similarity between the tasks.
arXiv Detail & Related papers (2024-08-29T06:57:45Z) - Leveraging Large Language Models for Integrated Satellite-Aerial-Terrestrial Networks: Recent Advances and Future Directions [47.791246017237]
Integrated satellite, aerial, and terrestrial networks (ISATNs) represent a sophisticated convergence of diverse communication technologies.
This paper explores the transformative potential of integrating Large Language Models (LLMs) into ISATNs.
arXiv Detail & Related papers (2024-07-05T15:23:43Z) - Cyber Threat Landscape Analysis for Starlink Assessing Risks and Mitigation Strategies in the Global Satellite Internet Infrastructure [0.0]
This study aims to provide valuable insights into the cybersecurity challenges inherent in the operation of global satellite internet infrastructure.
By prioritizing risks and proposing effective mitigation strategies, this research seeks to contribute to the ongoing efforts to safeguard the integrity and accessibility of satellite-based internet connectivity.
arXiv Detail & Related papers (2024-05-11T23:03:31Z) - Security-Sensitive Task Offloading in Integrated Satellite-Terrestrial Networks [15.916368067018169]
We propose the deployment of LEO satellite edge in an integrated satellite-terrestrial networks (ISTN) structure to support textitsecurity-sensitive computing task offloading.
We model the task allocation and offloading order problem as a joint optimization problem to minimize task offloading delay, energy consumption, and the number of attacks while satisfying reliability constraints.
arXiv Detail & Related papers (2024-01-20T07:29:55Z) - Learning Emergent Random Access Protocol for LEO Satellite Networks [51.575090080749554]
We propose a novel grant-free random access solution for LEO SAT networks, dubbed emergent random access channel protocol (eRACH)
eRACH is a model-free approach that emerges through interaction with the non-stationary network environment.
Compared to RACH, we show from various simulations that our proposed eRACH yields 54.6% higher average network throughput.
arXiv Detail & Related papers (2021-12-03T07:44:45Z) - Deep Learning Aided Routing for Space-Air-Ground Integrated Networks
Relying on Real Satellite, Flight, and Shipping Data [79.96177511319713]
Current maritime communications mainly rely on satellites having meager transmission resources, hence suffering from poorer performance than modern terrestrial wireless networks.
With the growth of transcontinental air traffic, the promising concept of aeronautical ad hoc networking relying on commercial passenger airplanes is potentially capable of enhancing satellite-based maritime communications via air-to-ground and multi-hop air-to-air links.
We propose space-air-ground integrated networks (SAGINs) for supporting ubiquitous maritime communications, where the low-earth-orbit satellite constellations, passenger airplanes, terrestrial base stations, ships, respectively, serve as the space-, air-,
arXiv Detail & Related papers (2021-10-28T14:12:10Z) - Artificial Intelligence for Satellite Communication: A Review [91.3755431537592]
This work provides a general overview of AI, its diverse sub-fields, and its state-of-the-art algorithms.
The application of AI to a wide variety of satellite communication aspects have demonstrated excellent potential, including beam-hopping, anti-jamming, network traffic forecasting, channel modeling, telemetry mining, ionospheric scintillation detecting, interference managing, remote sensing, behavior modeling, space-air-ground integrating, and energy managing.
arXiv Detail & Related papers (2021-01-25T13:01:16Z) - Reconfigurable Intelligent Surfaces in Action for Non-Terrestrial
Networks [22.345609845425493]
Next-generation communication technology will be fueled on the cooperation of terrestrial networks with nonterrestrial networks (NTNs)
We propose the use of reconfigurable intelligent surfaces (RISs) to improve and escalate this collaboration owing to the fact that they perfectly match with the size, weight, and power requirements.
A comprehensive framework of RIS-assisted non-terrestrial and interplanetary communications is presented by pinpointing challenges, use cases, and open issues.
arXiv Detail & Related papers (2020-12-02T05:11:51Z) - Integrating LEO Satellite and UAV Relaying via Reinforcement Learning
for Non-Terrestrial Networks [51.05735925326235]
A mega-constellation of low-earth orbit (LEO) satellites has the potential to enable long-range communication with low latency.
We study the problem of forwarding packets between two faraway ground terminals, through an LEO satellite selected from an orbiting constellation.
To maximize the end-to-end data rate, the satellite association and HAP location should be optimized.
We tackle this problem using deep reinforcement learning (DRL) with a novel action dimension reduction technique.
arXiv Detail & Related papers (2020-05-26T05:39:27Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.