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. Simulation results demonstrate the effectiveness of the proposed
framework. Finally, we insightfully provide open research directions for
low-carbon AIoT.

通过结合人工智能和物联网，人工智能物联网（AIoT）已经在许多领域引起革命。本文旨在探讨生成式人工智能（GAI）降低 AIoT 碳排放的潜力，并提出了一种新颖的 GAI 解决方案用于低碳 AIoT。我们首先研究引起 AIoT 碳排放的主要影响，然后介绍 GAI 技术及其与碳排放之间的关系。接下来，我们探索了 GAI 在低碳 AIoT 中的应用前景，重点讨论了 GAI 如何减少网络组件的碳排放。随后，我们提出了一个基于大型语言模型（LLM）的碳排放优化框架，框架中设计可插拔的 LLM 和检索增强生成（RAG）模块，以生成更准确可靠的优化问题。此外，我们利用生成扩散模型（GDM）识别碳排放减少的最佳策略。模拟结果证明了该框架的有效性。最后，我们提供低碳 AIoT 的未来研究方向。