Cloud-Native Framework for Urban Noise and Air Pollution Analysis

Rapid urbanization, industrialization, and vehicle emissions all contribute to the widespread ‎environmental and public health problem of urban air pollution. Because of their low spatial-temporal resolution and dependence on static sensor networks, conventional air quality ‎monitoring systems frequently fall short in providing the high-resolution, real-time data required ‎for environmental management and well-informed decisions. In order to achieve scalable, ‎economical, and high-resolution environmental monitoring, this dissertation offers a thorough ‎framework for an Internet of Things-enabled urban air quality monitoring system that combines ‎multi-layer sensor architectures, sophisticated communication protocols, and predictive ‎analytics. This study shows that the suggested framework is effective in attaining notable ‎improvements in spatial-temporal resolution and prediction accuracy when compared to current ‎systems through thorough testing and implementation in urban settings. The framework's ability ‎to pinpoint pollution hotspots, predict air quality indices, and offer useful information to ‎legislators and urban planners is demonstrated by case studies carried out in urban regions. The ‎results also emphasize how important user accessibility, system interoperability, and data ‎security are to the expansion of IoT-based monitoring systems for widespread usage. This ‎study presents a cloud-native architecture for real-time noise and air pollution analytics, ‎leveraging IoT technology and cloud computing to enhance urban environmental monitoring. ‎The system collects and analyses data on various pollutants and noise levels, providing timely ‎insights for effective decision-making and improved public health. By integrating AI-driven ‎analytics and IoT sensors, this innovative approach enables scalable, secure, and data-driven ‎urban planning strategies. This study advances the state-of-the-art in IoT-enabled air quality ‎monitoring systems, adding to the expanding corpus of information on sustainable urban ‎development. In addition to addressing important technological issues, the suggested strategy ‎offers a way to combine environmental monitoring with smart city projects, promoting more ‎sustainable and healthy urban settings. To improve the system's scalability and usefulness even ‎more, future research topics include integrating policy-driven frameworks, cross-sector IoT ‎applications, and adaptive artificial intelligence algorithms‎.