Carbon-Aware Orchestration of Integrated Satellite Aerial Terrestrial Networks via Digital Twin
- URL: http://arxiv.org/abs/2510.17825v1
- Date: Wed, 01 Oct 2025 06:49:42 GMT
- Title: Carbon-Aware Orchestration of Integrated Satellite Aerial Terrestrial Networks via Digital Twin
- Authors: Shumaila Javaid, Nasir Saeed,
- Abstract summary: Integrated Satellite Aerial Terrestrial Networks (ISATNs) are envisioned as key enablers of 6G.<n>This work advances prior energy-aware studies by proposing a carbon-aware orchestration framework for ISATNs.
- Score: 6.097337826307388
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Integrated Satellite Aerial Terrestrial Networks (ISATNs) are envisioned as key enablers of 6G, providing global connectivity for applications such as autonomous transportation, Industrial IoT, and disaster response. Their large-scale deployment, however, risks unsustainable energy use and carbon emissions. This work advances prior energy-aware studies by proposing a carbon-aware orchestration framework for ISATNs that leverages Digital Twin (DT) technology. The framework adopts grams of CO$_2$-equivalent per bit (gCO$_2$/bit) as a primary sustainability metric and implements a multi timescale Plan Do Check Act (PDCA) loop that combines day-ahead forecasting with real-time adaptive optimization. ISATN-specific control knobs, including carbon-aware handovers, UAV duty cycling, and renewable-aware edge placement, are exploited to reduce emissions. Simulation results with real carbon intensity data show up to 29\% lower gCO$_2$/bit than QoS-only orchestration, while improving renewable utilization and resilience under adverse events.
Related papers
- Towards eco friendly cybersecurity: machine learning based anomaly detection with carbon and energy metrics [0.17476892297485447]
This study introduces an eco aware anomaly detection framework that unifies machine learning based network monitoring with real time carbon and energy tracking.<n>We benchmark Logistic Regression, Random Forest, Support Vector Machine, Isolation Forest, and XGBoost models across energy, carbon, and performance dimensions.<n>Results reveal that optimized Random Forest and lightweight Logistic Regression models achieve the highest eco efficiency, reducing energy consumption by more than forty percent compared to XGBoost.
arXiv Detail & Related papers (2025-12-31T14:36:57Z) - ASTREA: Introducing Agentic Intelligence for Orbital Thermal Autonomy [51.56484100374058]
ASTREA is the first agentic system executed on flight-heritage hardware for autonomous spacecraft operations.<n>We integrate a resource-constrained Large Language Model (LLM) agent with a reinforcement learning controller in an asynchronous architecture tailored for space-qualified platforms.
arXiv Detail & Related papers (2025-09-16T08:52:13Z) - Dirty Bits in Low-Earth Orbit: The Carbon Footprint of Launching Computers [1.8990839669542956]
Low-Earth Orbit (LEO) satellites are increasingly proposed for communication and in-orbit computing.<n>This paper investigates the carbon footprint of computing in space, focusing on lifecycle emissions from launch over orbital operation to re-entry.
arXiv Detail & Related papers (2025-08-08T12:14:20Z) - Towards net-zero manufacturing: carbon-aware scheduling for GHG emissions reduction [0.0]
Scope 2 emissions are the indirect emissions related to the production and consumption of grid electricity.<n>This study introduces a carbon-aware permutation flow-shop scheduling model designed to reduce scope 2 emissions.
arXiv Detail & Related papers (2025-03-03T09:06:54Z) - Improving Power Plant CO2 Emission Estimation with Deep Learning and Satellite/Simulated Data [0.0]
CO2 emissions from power plants, as significant super emitters, substantially contribute to global warming.<n>This study addresses challenges by expanding the available dataset through the integration of NO2 data from Sentinel-5P, generating continuous XCO2 maps, and incorporating real satellite observations from OCO-2/3 for over 71 power plants in data-scarce regions.
arXiv Detail & Related papers (2025-02-04T08:05:15Z) - SCoTT: Strategic Chain-of-Thought Tasking for Wireless-Aware Robot Navigation in Digital Twins [78.53885607559958]
We propose SCoTT, a wireless-aware path planning framework.<n>We show that SCoTT achieves path gains within 2% of DP-WA* while consistently generating shorter trajectories.<n>We also show the practical viability of our approach by deploying SCoTT as a ROS node within Gazebo simulations.
arXiv Detail & Related papers (2024-11-27T10:45:49Z) - Generative AI for Low-Carbon Artificial Intelligence of Things with Large Language Models [67.0243099823109]
Generative AI (GAI) holds immense potential to reduce carbon emissions of Artificial Intelligence of Things (AIoT)
In this article, we explore the potential of GAI for carbon emissions reduction and propose a novel GAI-enabled solution for low-carbon AIoT.
We propose a Large Language Model (LLM)-enabled carbon emission optimization framework, in which we design pluggable LLM and Retrieval Augmented Generation (RAG) modules.
arXiv Detail & Related papers (2024-04-28T05:46:28Z) - Real-time high-resolution CO$_2$ geological storage prediction using
nested Fourier neural operators [58.728312684306545]
Carbon capture and storage (CCS) plays an essential role in global decarbonization.
Scaling up CCS deployment requires accurate and high-resolution modeling of the storage reservoir pressure buildup and the gaseous plume migration.
We introduce Nested Fourier Neural Operator (FNO), a machine-learning framework for high-resolution dynamic 3D CO2 storage modeling at a basin scale.
arXiv Detail & Related papers (2022-10-31T04:04:03Z) - Near Real-time CO$_2$ Emissions Based on Carbon Satellite And Artificial
Intelligence [20.727982405167758]
We propose an integral AI based pipeline that contains both a data retrieval algorithm and a two-step data-driven solution.
First, the data retrieval algorithm can generate effective datasets from multi-modal data including carbon satellite, the information of carbon sources, and several environmental factors.
Second, the two-step data-driven solution that applies the powerful representation of deep learning techniques to learn to quantify anthropogenic CO$$ emissions.
arXiv Detail & Related papers (2022-10-11T12:01:32Z) - Integrating LEO Satellites and Multi-UAV Reinforcement Learning for
Hybrid FSO/RF Non-Terrestrial Networks [55.776497048509185]
A mega-constellation of low-altitude earth orbit satellites (SATs) and burgeoning unmanned aerial vehicles (UAVs) are promising enablers for high-speed and long-distance communications in beyond fifth-generation (5G) systems.
We investigate the problem of forwarding packets between two faraway ground terminals through SAT and UAV relays using either millimeter-wave (mmWave) radio-frequency (RF) or free-space optical (FSO) link.
arXiv Detail & Related papers (2020-10-20T09:07:10Z) - 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.