A Carbon-Aware Framework for Energy-Efficient Data Acquisition and Task Offloading in Sustainable AIoT Ecosystems

Abstract

The integration of artificial intelligence (AI) and the Internet of Things (IoT) has given rise to AI of Things (AIoT) ecosystems, which, while revolutionizing various domains, face challenges in energy efficiency and environmental sustainability. This article introduces LSCEA-AIoT, a low-carbon sustainable computing framework for energy-efficient acquisition and allocation in AIoT. The framework addresses two critical aspects: 1) energy-efficient heterogeneous data acquisition and 2) low-carbon task offloading optimization. We develop a multisource model for data acquisition that considers acquisition time, load balancing, and energy consumption, coupled with an adaptive sensor node deployment strategy for optimized channel allocation. The task offloading component formulates a joint optimization problem, balancing local and edge computing models to minimize ecosystem costs and carbon emissions. We propose a carbon-aware multichannel exploration offloading decision algorithm based on a Monte Carlo tree search to obtain near-optimal solutions. Extensive experiments compare LSCEA-AIoT with state-of-the-art methods across various metrics. Results demonstrate that LSCEA-AIoT significantly outperforms existing approaches, achieving lower data acquisition errors, reduced energy consumption, extended network lifetimes, and increased data acquisition volumes. LSCEA-AIoT exhibits superior performance in task offloading scenarios in normalized rewards, system costs, and adaptability to diverse network configurations. These findings validate LSCEA-AIoT’s effectiveness in achieving low-carbon, sustainable computing for AIoT ecosystems.

Topics & Concepts