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.