ClimaGuardIoT aims to create a smart, low-cost IoT system to monitor local environmental conditions and alert communities about extreme weather events. Traditional weather stations are expensive, slow, and often unreliable in high-risk areas. Our goal is to design a working prototype that integrates a smart weather device, cloud backend, and mobile app. The device will collect real-time climate data, detect unusual patterns using TinyML on-device, and send alerts through reliable communication...Подробнее

Develop and test a smart early-warning system that integrates hardware, Edge AI, software, and cloud technologies to detect climate risks and deliver timely notifications in real time.

The project will follow an iterative workflow according to the MIEM HSE project calendar. Tasks and expected outcomes are defined at the start of each iteration. Completed tasks are evaluated, plans adjusted, and new tasks set for the next cycle. Progress and results are reported at scheduled project sessions, with regular reviews including feedback from the academic supervisor.

Working prototype of the early-warning IoT system, including experimental data collection device, cloud processing service, and mobile app for visualization and notifications. Project documentation covering system architecture, technical solutions, and testing results. At the end of the project cycle, a functional prototype ready for further development and scaling.

1. Hardware & Computational Resources - Edge Hardware: ESP32 microcontroller — collects sensor data, runs TinyML, and sends alerts. - Sensors: SHT31, BME280, Capacitive Soil Moisture Sensor v2.0, Tipping-Bucket Rain Gauge, MPU6050. - Connectivity: LoRa, NB-IoT, Wi-Fi. - Power: Solar panel + Li-ion battery + charge controller for off-grid operation. - Cloud & Software Stack: Docker-ready server (4+ cores, 8+ GB RAM, 50+ GB SSD, public IP). - Backend & Analytics: FastAPI backend, MQTT broker,...Подробнее

At the start of the project, the following skills and knowledge are available: - Basic IoT knowledge and experience with microcontrollers. - Python programming and development of simple backend services. - General understanding of machine learning and data analysis principles. - Familiarity with IoT system architecture and data flow from devices to user applications.