DEVELOPMENT OF AN INTEGRATED INSUDTRIAL IOT SYSTEM FOR REAL-TIME DATA MONITORING AND CONTROL

Abstract

This report presents the project about building a working prototype that collects real-time industrial data using actual hardware and sends it securely to the cloud for monitoring, analysis, and future machine learning use. We used the MPS 500 modular production system as our base and set up sensors to measure vibration, temperature, and liquid leaks. These sensors help detect early signs of equipment wear or failure. The data is sent through a mix of modern communication protocols like MQTT and S7 using ESP32 microcontrollers and Siemens PLCs. To make everything work smoothly, we built a central data flow using Node-RED, which handles the logic, processes the data, and pushes it to both a local InfluxDB (for time-series storage) and Google Cloud (for remote access and analytics). We also made a live dashboard where operators can see what's happening in real time—like live sensor values, machine status, and performance alerts. Security was a top priority, so we encrypted everything using TLS, used strong credentials, and added certificate-based authentication to prevent unauthorized access. Our setup balances low cost with real-world functionality, making it a solid option for small manufacturers or researchers who want access to real operational data for digital twin or predictive maintenance development. In the future, this system could be extended further with AI-driven analytics and more edge computing features.