Visual E2C: AI-Driven Visual End-Edge-Cloud Architecture for 6G in Low-Carbon Smart Cities

Abstract

With the rapid development of 6G wireless communication technology, the emergence of rich multimedia data for massive devices will lead to greater intensive computations and energy consumption. However, the requirements from both green communication and international low-carbon strategy can be challenging. In this article, we first systematically analyze the key challenges from the perspective of 6G networks for low-carbon smart city development. Then we propose an AI-driven visual end-edge-cloud architecture (E2C), which extends upon the conventional design from the perspective of human-machine fusion and carbon emission optimization. We provide systematical analysis and intelligent computing methods for carbon emission in visual end-edge-cloud architecture. This architecture can enable the provision of E2C AI intelligence for 6G networks through hybrid hierarchical optimization mechanisms. Finally, the experimental results demonstrate that our proposed architecture has better performance in smart cities, achieving lower carbon emissions compared to traditional methods.