Project
I developed a full-stack Industrial IoT framework that integrates Python-based Digital Twins, simulated embedded firmware, and a native Android monitoring application. The system models industrial devices through digital twins while communicating with simulated microcontroller firmware built for the ESP32 using the Wokwi simulator. A native Android application built using Android Studio provides real-time telemetry visualization, device control, and monitoring.
The architecture enables real-time synchronization between the simulated physical device and its digital twin. Sensor data generated by the firmware is transmitted to the Python backend, where the digital twin processes, analyzes, and stores the state of the system. The Android application retrieves telemetry data and displays it through an intuitive interface for monitoring and interaction.
Industrial IoT development often faces three major challenges:
Lack of accessible testing environments Developing embedded IoT systems typically requires physical hardware, which increases cost and slows experimentation.
Difficulty in validating system behavior before deployment Without a digital representation of devices, it is difficult to simulate scenarios, monitor internal states, and test system responses safely.
Fragmented development pipelines Firmware, backend processing, and user interfaces are often developed separately, making integration complex.
Key Technical Components
Embedded Layer: Simulated ESP32 firmware generates sensor telemetry and device status data.
Digital Twin Layer: Python services maintain virtual device models, process telemetry streams, and manage device state.
Mobile Telemetry Layer: A native Android application visualizes live device data and provides control interfaces.
Communication Layer: The system enables real-time communication between firmware, digital twin services, and the mobile application.
Challenges Faced
Maintaining consistent state between simulated firmware and its digital twin required careful design of data pipelines and update mechanisms.
Processing streaming sensor data while maintaining responsiveness in the mobile application required efficient backend handling and structured APIs.
Ensuring smooth communication between firmware simulation, backend services, and the mobile app required careful protocol design and modular architecture.
The firmware simulation needed to replicate realistic sensor outputs and device responses to properly test system logic.
Building next-generation defence avionics and embedded AI for India
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