# An IoT System and MODIS Images Enable Smart Environmental Management for Mekong Delta
## 2 System Overview  
Figure 1 illustrates of IoTs and MODIS images based smart environmental management system for Mekong Delta. The proposed system architecture includes four main components: (1) Data collection; (2) Cloud storage; (3) Smart data processing; (4) Visualization and management.  
Figure 1: The overview of the IoT and MODIS images for smart environmental management.  
The data collection component plays a critical role in the proposed smart environmental management system. This component is responsible for automatically acquiring remote sensing images from relevant providers, such as MODIS images from NASA (National Aeronautics and Space Administration (NASA), as well as telemetry data from IoT-based measurement ground stations. While the primary focus of this work is to manage PM10, PM2.5, and Land Surface Temperature (LST) values, the IoT-based architecture is designed to integrate multiple sensors, cameras, and drones for additional monitoring purposes.  
The connection between the remote sensing images and the system uses traditional internet connectivity, such as wireless or cable, while the connection with the ground stations uses 3G/4G networks due to the wide coverage area. Ground stations offer precise measurements of PM10, PM2.5, and LST values; however, building and operating these stations are more expensive compared to extracting these values from remote sensing images. This is the primary reason for combining satellite images and ground station measurements in the proposed system. The key requirement for data communication is to overcome data loss. In this system, we buffer telemetry data at the stations when they cannot be sent to the cloud. They will be resent to the cloud when the communication networks become available. Due to the reliability of 3G/4G networks, our communication is almost real-time available.  
The collected data, including remote sensing images and raw telemetry sensor data, undergo preprocessing to eliminate redundancy and are then forwarded to cloud storage services for long-term storage. Raw data, instead of processed data, are stored in the cloud for further study. The Smart Data Processing engine then automatically retrieves and processes the data, including PM10, PM2.5, and LST values from telemetry data and remote sensing images. The engine is designed to process various data types, including images and videos from cameras and drones, providing versatility to the system.  
A Graphic User Interface (GUI) is built for both desktop and mobile devices to visualize data in real time. The GUI also provides alert notifications to administrators based on rules and values of data. Through the GUI, administrators can configure the system by setting alerting rules or calibrating the ground stations. This feature provides a convenient and efficient way to manage the system and ensure that it is working optimally at all times.  
In conclusion, the data collection component of the proposed smart environmental management system provides a robust and versatile way to gather data from remote sensing images and IoT-based ground stations. The preprocessing and storage of raw data in the cloud, combined with the Smart Data Processing engine, ensures that the system can efficiently process and extract meaningful information from the collected data. The GUI provides a convenient and accessible way for administrators to monitor the system, configure it, and receive alert notifications, ensuring that the system is working optimally at all times.