Influence of AI and MAS in Enhancing Energy Efficiency of 6G Communications

Next-generation Internet of Things (IoT) is progressing rapidly due to the introduction of beyond 5G (B5G) and the approaching arrival of 6G, which have improved the dependability, productivity, and profitability of both industrial and personal operations. Nevertheless, data overload is frequently caused by the abundance of wireless sensors in commercial 6G devices. Data mining gathers useful information in order to solve this. Meanwhile, in the 6G architecture, edge computing enables automated equipment operations and intelligent decision-making in real-time. Even so, despite the drawbacks of sensor-based energy sources, the integration of many technologies has led to an increase in energy. Using a multi-agent system (MAS) approach, a system model for industrial wireless sensor networks in 6G applications has been presented in order to combat this. Sensor node clustering locates and forecasts the locations of principal nodes by employing distributed artificial intelligence (DAI). Convolutional neural networks (CNN) and back-propagation neural networks (BPNN) are used in optimisation. Allocating resources to individual nodes efficiently is made possible by the analysis of inter-cluster correlations. The outcomes of the simulation show how well the approach works to reduce resource waste, increase overall energy efficiency, and protect important data. This strategy improves network performance in industrial 6G applications while simultaneously addressing concerns about energy consumption.