Carbon-Aware and Energy-Efficient Management Framework for Edge Intelligence in 6G Networks

Abstract

Large-scale Internet of Things (IoT) ecosystems and next-generation communication networks, such as 5G and 6G, are rapidly expanding. This has led to a huge increase in energy consumption and carbon emissions across dispersed computing infrastructures. While edge computing reduces latency and bandwidth use by processing data closer to end devices, large-scale edge installations raise new sustainability challenges. This paper proposes a Green Edge Computing system that combines carbon-aware resource management with sustainable edge intelligence to boost energy efficiency in 6G-enabled networks. The proposed system incorporates energy adaptive task offloading, carbon-intensity-aware scheduling, and dynamic workload orchestration to minimize environmental impact while maintaining Quality of Service (QoS) standards. Network load circumstances and real-time carbon intensity statistics inform intelligent resource allocation decisions across edge-cloud layers. Additionally, renewable energy awareness is entwined with policies that prioritize low-carbon energy sources when they are available. Simulation-based performance evaluation demonstrates that the proposed framework significantly reduces overall energy use and carbon emissions while preserving latency and service dependability when compared to conventional edge deployment strategies. By incorporating sustainability criteria into edge intelligence and orchestration approaches, this study encourages ecologically conscientious network design. By assisting in the development of resilient, energy-efficient, and carbon-conscious digital infrastructures, the proposed approach promotes global sustainability goals and enables more ecologically friendly next-generation communication networks.