Urban and rail transport noise pollution is an increasing concern due to its negative impact on public health, including cardiovascular diseases, sleep disturbances, and cognitive impairments (WHO, 2018). Traditional noise monitoring systems, which rely on static measurements, lack real-time adaptability and struggle to accurately classify noise sources in dynamic environments. This study explores the application of AWS IoT Core in smart acoustic monitoring systems to enhance noise detection, classification, and mitigation. By integrating AWS IoT Core with advanced noise sensors and spectrum analyzers, real-time noise data can be securely transmitted, processed, and analyzed in the cloud. AWS IoT Core enables continuous data collection and facilitates predictive modeling, improving noise classification accuracy and supporting proactive noise reduction strategies. The study focuses on leveraging AWS IoT Core for real-time noise monitoring, automated noise classification, and data-driven urban planning, ensuring more effective and scalable noise management solutions. Furthermore, AWS IoT Core’s ability to connect and manage thousands of IoT-enabled noise monitoring devices provides a foundation for smart city infrastructure. The research highlights key challenges and opportunities in integrating cloud-based noise monitoring systems, emphasizing the role of AWS IoT Core in optimizing urban noise management, improving public health outcomes, and supporting sustainable city development. Through adaptive noise mitigation strategies and enhanced decision-making, AWS IoT Core offers
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