A Superficial BigData Communication Model to Improve Energy Efficiency Over Wireless Networking Platform Using Deep Learning Strategy

Due to their limited energy capacity, wireless networks will have a difficult time handling the tremendous volume of data that comes with a big number of users. This data is also rapidly changing and diverse. Superficial BigData Communication (SDBC), a novel paradigm for data integrity and energy efficiency, is introduced in this paper. By using a deliberate deployment strategy that permits quick reaction times and significantly increases network uptime, SDBC offers a novel method of obtaining important raw data. Learning enabled Energy Optimization Protocol (LEOP), a novel proposed technique, reduces the efficiency of data aggregation while simultaneously increasing data gathering accuracy as compared to the current Energy aware Route Optimization (ERO) method. The protocol employs sophisticated algorithms to analyze large-scale energy consumption data, identify patterns, and predict future energy needs. By continuously learning from operational data and environmental factors, LEOP adapts its optimization strategies in real time, ensuring robust performance under varying conditions. The primary goal of LEOP is to send as much data as necessary across wireless networks while minimizing energy consumption. The proposed method subsumes optimal energy control by using intelligent routing algorithms along with new data compression techniques. Our experimental results indicate that SDBC consumes less energy compared with the state-of-the-art scheduling approaches while ensuring high data quality. A new class of wireless networks that are sustainable and suitable for the requirements placed upon service providers by BigData applications emerged. Experimental results demonstrate that LEOP significantly reduces energy consumption and operational costs compared to traditional optimization methods. This research underscores LEOP's potential to revolutionize energy management practices, offering a scalable and adaptable solution for addressing the complex challenges of modern energy systems.