<![CDATA[This study explores the application of the Round Robin algorithm for scheduling tasks in Internet of Things (IoT) systems designed for environmental monitoring, such as temperature and humidity tracking. Efficient task scheduling is critical to minimize latency and energy consumption in IoT networks. Using a Python-based simulation, this research evaluates the performance of the Round Robin algorithm in managing 10 to 50 virtual IoT devices tasked with environmental data collection, comparing it with Priority with Aging and Genetic Algorithm approaches. The simulation results indicate that Round Robin reduces the average waiting time by 15% compared to random scheduling, while the Genetic Algorithm outperforms Round Robin by approximately 20% in high-density networks. This approach provides valuable insights into IoT scheduling efficiency without requiring physical deployment, making it relevant for large-scale IoT system development.]]>
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