<![CDATA[Air pollution in urban areas has become a serious issue due to rapid urbanization, industrial activities, and the increasing number of motor vehicles, which directly impact human health and the environment. Conventional monitoring systems that rely on cloud-based processing often face challenges such as high latency and limitations in providing real-time information. This study aims to develop an Internet of Things (IoT)-based air quality monitoring system integrated with edge computing to improve data processing efficiency and reduce latency. The proposed system utilizes sensors for PM2.5, carbon monoxide (CO), temperature, and humidity, which are connected to an edge device to perform local data processing before being transmitted to the cloud. The research methodology includes system architecture design, hardware and software implementation, and performance evaluation based on latency, data accuracy, and system responsiveness. The results indicate that the edge-based approach significantly reduces latency compared to cloud-based systems while maintaining high data accuracy. In addition, the system is capable of providing real-time data visualization through a dashboard, enabling faster and more effective decision-making. Therefore, the integration of IoT and edge computing offers an efficient, scalable, and promising solution for urban air quality monitoring and supports the development of smart city applications.]]>
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