# Generative AI for Low-Carbon Artificial Intelligence of Things with Large Language Models

arXiv: https://arxiv.org/abs/2404.18077 · HTML: https://mlsanity.com/paper/2404.18077/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 21 references that are in the map.
- First posted on arXiv 28 Apr 2024.

## Builds on (most cited first, 9 of 9)

- [Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization](https://mlsanity.com/paper/2308.05384/) (2023)
- [Generative AI Agents with Large Language Model for Satellite Networks via a Mixture of Experts Transmission](https://mlsanity.com/paper/2404.09134/) (2024)
- [Interactive AI with Retrieval-Augmented Generation for Next Generation Networking](https://mlsanity.com/paper/2401.11391/) (2024)
- [Green Edge AI: A Contemporary Survey](https://mlsanity.com/paper/2312.00333/) (2023)
- [From Generative AI to Generative Internet of Things: Fundamentals, Framework, and Outlooks](https://mlsanity.com/paper/2310.18382/) (2023)
- [Enabling Resource-efficient AIoT System with Cross-level Optimization: A survey](https://mlsanity.com/paper/2309.15467/) (2023)
- [Towards a Systematic Survey for Carbon Neutral Data Centers](https://mlsanity.com/paper/2110.09284/) (2021)
- [Resource-efficient Generative Mobile Edge Networks in 6G Era: Fundamentals, Framework and Case Study](https://mlsanity.com/paper/2312.12063/) (2023)
- [Towards Carbon-Neutral Edge Computing: Greening Edge AI by Harnessing Spot and Future Carbon Markets](https://mlsanity.com/paper/2304.11374/) (2023)

## Built on by (most cited first, 3 of 3)

- [When Graph Meets Retrieval Augmented Generation for Wireless Networks: A Tutorial and Case Study](https://mlsanity.com/paper/2412.07189/) (2024)
- [Advancing Generative Artificial Intelligence and Large Language Models for Demand Side Management with Internet of Electric Vehicles](https://mlsanity.com/paper/2501.15544/) (2025)
- [A Trustworthy Multi-LLM Network: Challenges,Solutions, and A Use Case](https://mlsanity.com/paper/2505.03196/) (2025)

## Abstract (arXiv)

By integrating Artificial Intelligence (AI) with the Internet of Things (IoT), Artificial Intelligence of Things (AIoT) has revolutionized many fields. However, AIoT is facing the challenges of energy consumption and carbon emissions due to the continuous advancement of mobile technology. Fortunately, Generative AI (GAI) holds immense potential to reduce carbon emissions of AIoT due to its excellent reasoning and generation capabilities. In this article, we explore the potential of GAI for carbon emissions reduction and propose a novel GAI-enabled solution for low-carbon AIoT. Specifically, we first study the main impacts that cause carbon emissions in AIoT, and then introduce GAI techniques and their relations to carbon emissions. We then explore the application prospects of GAI in low-carbon AIoT, focusing on how GAI can reduce carbon emissions of network components. Subsequently, we propose a Large Language Model (LLM)-enabled carbon emission optimization framework, in which we design pluggable LLM and Retrieval Augmented Generation (RAG) modules to generate more accurate and reliable optimization problems. Furthermore, we utilize Generative Diffusion Models (GDMs) to identify optimal strategies for carbon emission reduction. Numerical results demonstrate the effectiveness of the proposed framework. Finally, we insightfully provide open research directions for low-carbon AIoT.