Energy-Efficient Resource Allocation Strategy in Massive IoT for Industrial 6G Applications

Abstract

The birth of beyond 5G (B5G) and emerge of 6G have made personal and industrial operations more reliable, efficient, and profitable, accelerating the development of the next-generation Internet of Things (IoT). We know, one of the most important key performance indicators in 6G is smart network architecture, and in massive IoT applications, energy-efficient ubiquity networks rely mainly on the intelligence and automation for industrial applications. This article addresses the energy consumption problem with a massive IoT system model with dynamic network architecture or clustering using a multiagent system (MAS) for industrial 6G applications. The work uses distributed artificial intelligence (DAI) to cluster the sensor nodes in the system to find the main node and predict its location. The work initially uses the backpropagation neural network (BPNN) and convolutional neural network (CNN), which are, respectively, introduced for optimization. Furthermore, the work analyzes the correlation of mutual clusters to allocate resources to individual nodes in each cluster efficiently. The simulation results show that the proposed method reduces the waste of resources caused by redundant data, improves the energy efficiency of the whole network, along with information preservation.