AI-Based Efficient WUGS Network Channel Modeling and Clustered Cooperative Communication

Abstract

Wireless underground sensor networks (WUSNs) are sub-ground-surface sensor node networks designed to establish real-time tracking capacities in diverse underground ecosystems composed of soil, water, oil, and other materials. The contact medium is the key distinction between USNs and terrestrial wireless sensor networks. The communication properties of electromagnetic (EM) waves in underwater resources like mud, water, oil, and other materials, as well as major variations between propagation in air, make classification of the underground wireless channel difficult. From the transmission of data, the channels modelling is involved to remove the noise and reduce the energy. After that Multi-driven Clustering Algorithm (MCA) method is used to cluster the channel based on the EDT method (Energy, Distance, and Time). Completing the transmission, the data can be transmitted to the destination. The experimental portion of the proposed method is analyzed by varying parameters such as energy, throughput, channel response, and time duration of the data transmission. From the experimental analysis, it is observed that 55% and 45% more energy levels can be saved with the proposed MCA method than with the LEACH methods for DR and SW media methods, respectively. The throughput of the proposed method is proven with 45% and 42% improvement over the time horizon in the median under the frequency for soil of the LEACH and LEACH-CO2 methods, correspondingly.