C3Meta: A Context-Aware Cloud-Edge-End Collaboration Framework Toward Green Metaverse

Abstract

The Metaverse aims to provide an immersive and interactive experience for people by digitizing the physical world to the virtual world using sensing, communication, and computation technology. However, the seamless transmission of massive data in the communication network and the intensive computation brought by large models in the Metaverse have raised concerns over energy consumption and carbon emissions. To address this issue, this article designs a Context-aware Cloud-edge-end Collaboration framework to establish an energy-efficient green Metaverse system (C3Me-ta) from the six aspects via the collaboration of end, edge, and cloud based on the system condition and information flow. We take the smart city as the case study of C3Meta and focus on a typical crowd counting task in the field of intelligent monitoring to demonstrate the effectiveness of the proposed framework. A series of key technologies, including key frame detection, adaptive partition, offloading location decision, and virtual scene reconstruction, are illustrated to achieve the trade-off between detection performance and energy consumption. An experimental platform is established to verify the performance of the proposed framework, and the results show C3Meta is an energy-saving method while maintaining low delay and high detection performance in the real-time system. The C3Meta framework has the potential to contribute to a sustainable and environmentally friendly Metaverse.