OPTIMIZING ENVIRONMENTAL INTELLIGENCE IN AN INTERNET OF THINGS SYSTEM FOR SUSTAINABLE HEALTH MONITORING

The transformative influence of the Internet and the expansive growth of the Internet of Things (IoT) have become integral components of contemporary life. This paper delves into the intersection of IoT systems and environmental health, emphasizing the challenges posed by memory constraints in low-end IoT devices. As these devices play a role in monitoring and managing environmental parameters, the effective utilization of resources through robust memory management becomes paramount. With focus on design, configuration, scalability, and performance in scene management, this study explores the critical role of memory management in ensuring optimal functionality of IoT systems. In the context of environmental health, the paper sheds light on the intricate dynamics of memory allocation, scene execution, memory reduction, and system scalability. The study highlights the role of efficient memory management in facilitating seamless and adaptive IoT experiences in environmental monitoring applications. In conclusion, the paper underscores the need for memory management strategies as the IoT ecosystem continues to evolve. This comprehensive exploration contributes to the integral role that effective memory management plays in advancing both IoT technologies and environmental health initiatives.