Optimizing Energy Efficiency and Increasing Scalability in 6G-IoT Networks Through SDN, Duty Cycling, and AI-Driven Slicing

As sixth-generation (6G) and Internet of Things (IoT) networks expand rapidly, concerns are growing about their energy consumption and scalability. This is primarily because more devices are being connected, resulting in increased energy consumption energy consumption.This study examines three primary strategies for optimizing energy efficiency and improving scalability in 6G-IoT networks. This research looks at three experimental setups: 1) using software-defined networking (SDN) with dynamic slicing to organize devices based on when they are most and least used, 2) duty cycling, which turns devices on and off to save energy, and 3) AI-optimized network slicing that uses both convolutional neural networks (CNN) and bidirectional long short-term memory (BiLSTM) models. In the first setup, SDN with dynamic slicing helped reduce unnecessary power consumption by matching device activity to peak times. As more devices were added, this method kept energy use low and improved the network’s ability to handle growth without requiring significantly more power. This resulted in a 66.28 percent decrease in power usage. In the second setup, duty cycling allowed only some devices to be active at a time, which reduced power use by over 60 percent during slow periods. In the third setup, the CNN-BiLSTM model effectively classified service types and reduced power use by 60.14 percent. While these methods were not combined into a single solution, each utilized slicing techniques to more effectively allocate resources and manage power.