The Internet of Things (IoT) has revolutionized how devices collect, transmit, and analyze data across diverse domains such as agriculture, healthcare, and smart cities. This paper presents a scalable IoT web application designed for real- time data monitoring, analysis, and cloud integration. The system uses Angular for the frontend, Node.js for the backend, and integrates Firebase and InfluxDB for cloud-based data storage. Data from IoT sensors (temperature, motion, humidity) is transmitted through MQTT and WebSocket protocols for low-latency communication. The system supports real-time visualization, threshold-based alerts, and predictive analytics using machine learning models. This solution emphasizes modularity, data security (TLS/SSL encryption and RBAC), and adaptability, making it suitable for high- demand, multi-device environments. Challenges such as device interoperability, latency, and data reliability are addressed through edge computing, event-driven architecture, and sensor calibration. The results confirm strong performance, scalability, and usability, positioning this solution as a robust platform for real-world IoT applications.
Princy et al. (Wed,) studied this question.