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.
