Optimized Efficiency of IoT-Based Next Generation Smart Wireless Sensor Networks Using a Machine Learning Algorithm

Abstract

The rapid advancement of IoT-based smart wireless sensor networks has necessitated optimized efficiency to meet the growing demands of various applications. In our daily lives, smart devices increasingly depend on these networks for seamless functioning. Traditional methods, such as Round-Robin Scheduling (RRS) and Static Resource Allocation (SRA), have shown limitations in handling dynamic workloads, leading to inefficiencies and increased latency. These conventional methods often struggle with scalability, resulting in suboptimal performance in large-scale IoT deployments. To address these challenges, the Optimized Machine Learning-Based Efficiency Algorithm (OMLEA) is proposed for next-generation smart wireless sensor networks. OMLEA leverages advanced machine learning techniques to dynamically adjust network parameters, ensuring optimal performance under varying conditions. By intelligently predicting and managing network resources, OMLEA significantly enhances efficiency and reliability while minimizing latency and resource wastage. Experimental results demonstrate that OMLEA achieves up to a 0.25% improvement in efficiency and a 0.30% increase in network reliability compared to RRS and SRA. Additionally, the algorithm effectively reduces network latency by approximately 0.40%, ensuring timely data transmission and improved overall network performance. This innovative approach not only overcomes the drawbacks of existing methods but also sets a new benchmark for the performance of IoT-based smart wireless sensor networks, paving the way for future developments.