An Empirical Analysis and Overview of a Green 6-G Paradigms and its Various Implementation Applications

The advent of green 6G technologies holds promise for revolutionary improvements across different areas such as data transmission speed, latency, and traffic volume. Incorporating features like sub-terahertz communication and advanced multiple-input multiple-output (MIMO) technologies, Green 6 G networks aim to surpass the data transmission rates of 5 G while slashing latency to a mere fraction. However, this rapid expansion in network data transfer poses notable hurdles concerning data processing systems and energy usage. Despite strides in energy-efficient computing methods such as green cloud and edge computing, unresolved issues persist, including substantial energy consumption during data migration and idle server power usage. To tackle these challenges, this study proposes a novel strategy called “In-Network Computing” (INC), which relocates processing tasks from hosts to network devices, leveraging idle resources to alleviate data transmission burdens and mitigate pressure on cloud computing resources. In contrast to edge computing, INC exhibits advantages in the 6 G landscape due to the enhanced computing capabilities and application awareness of network devices. The proposed approach prioritizes energy efficiency by minimizing data centre inputs and integrating host and network node computing environments through virtualization and software-based models. The paper introduces innovative implementations by implementing green 6 g paradigms, focusing on task scheduling and the creation of a prototype system utilizing low-power multicore processors. Experimental validation across diverse application scenarios demonstrates substantial reductions in system energy consumption and network traffic.