Carbon-Aware Edge Computing for Internet of Everything Networks: A Digital Twin Approach

The rapid growth of edge computing has enabled low-latency and high-efficiency processing for a wide range of applications; however, it also leads to significant energy consumption and carbon emissions. In this context, this study investigates a CO₂ emission minimization problem in a digital twin-aided edge computing system, aiming to optimize task offloading decisions, transmit power, and processing rates of Internet of Things (IoT) devices. To address the formulated mixed-integer nonlinear programming problem, we propose two solutions: 1) an alternating optimization method based on the successive convex approximation framework and 2) a deep reinforcement learning (DRL) approach. Extensive simulations validate the effectiveness of the proposed solutions, demonstrating significant reductions in CO₂ emissions, robust optimization performance, and superior results compared to benchmark schemes. The findings highlight the feasibility of integrating advanced optimization and artificial intelligence-driven techniques to achieve environmentally sustainable and high-performance edge computing systems, paving the way for greener technological innovation.