Discover inspiring projects from students at top colleges like IITs and NITs. Find high-quality ideas for your resume, learn from peer-developed code, and see what's possible with today's technology.
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: 1. Lack of accessible testing environments Developing embedded IoT systems typically requires physical hardware, which increases cost and slows experimentation. 2. 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. 3. 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 1. Synchronizing Physical and Digital States Maintaining consistent state between simulated firmware and its digital twin required careful design of data pipelines and update mechanisms. 2. Real-Time Telemetry Handling Processing streaming sensor data while maintaining responsiveness in the mobile application required efficient backend handling and structured APIs. 3. Cross-Layer Integration Ensuring smooth communication between firmware simulation, backend services, and the mobile app required careful protocol design and modular architecture. 4. Simulating Realistic Device Behavior The firmware simulation needed to replicate realistic sensor outputs and device responses to properly test system logic.
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: 1. Lack of accessible testing environments Developing embedded IoT systems typically requires physical hardware, which increases cost and slows experimentation. 2. 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. 3. 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 1. Synchronizing Physical and Digital States Maintaining consistent state between simulated firmware and its digital twin required careful design of data pipelines and update mechanisms. 2. Real-Time Telemetry Handling Processing streaming sensor data while maintaining responsiveness in the mobile application required efficient backend handling and structured APIs. 3. Cross-Layer Integration Ensuring smooth communication between firmware simulation, backend services, and the mobile app required careful protocol design and modular architecture. 4. Simulating Realistic Device Behavior The firmware simulation needed to replicate realistic sensor outputs and device responses to properly test system logic.
### Overview I built a **real-time multiplayer gaming platform** designed to support live gameplay between connected players. The platform uses Redis for managing shared game state and player sessions while WebSockets enable low-latency communication between clients and the server. ### Key Features * **Real-time Multiplayer:** Built a multiplayer gaming system where connected players can interact and receive game updates in real time. * **Shared Game State:** Used **Redis** to manage shared game state and active player sessions across connected clients. * **WebSocket Communication:** Implemented event-driven communication using **Pusher Pub/Sub** to broadcast live game updates to connected players. * **Connection Resilience:** Developed TypeScript-based WebSocket management hooks supporting reconnection and optimistic UI updates. * **Responsive Interface:** Designed a pixel-art gaming interface using **Next.js App Router** and **Tailwind CSS** with a focus on mobile responsiveness. * **Scalable State Management:** Separated real-time communication and shared state handling to provide a reliable multiplayer experience.
### Overview I built a **real-time multiplayer gaming platform** designed to support live gameplay between connected players. The platform uses Redis for managing shared game state and player sessions while WebSockets enable low-latency communication between clients and the server. ### Key Features * **Real-time Multiplayer:** Built a multiplayer gaming system where connected players can interact and receive game updates in real time. * **Shared Game State:** Used **Redis** to manage shared game state and active player sessions across connected clients. * **WebSocket Communication:** Implemented event-driven communication using **Pusher Pub/Sub** to broadcast live game updates to connected players. * **Connection Resilience:** Developed TypeScript-based WebSocket management hooks supporting reconnection and optimistic UI updates. * **Responsive Interface:** Designed a pixel-art gaming interface using **Next.js App Router** and **Tailwind CSS** with a focus on mobile responsiveness. * **Scalable State Management:** Separated real-time communication and shared state handling to provide a reliable multiplayer experience.
### Overview I built and deployed a **production-grade AI-powered WhatsApp chatbot** that uses Retrieval-Augmented Generation (RAG) to generate context-aware responses from custom knowledge bases. The system integrates WhatsApp webhooks with a FastAPI backend and asynchronous workers for reliable message processing. ### Key Features * **WhatsApp Integration:** Integrated WhatsApp Business API webhooks using WAHA for receiving and processing user messages. * **RAG Pipeline:** Built a Retrieval-Augmented Generation pipeline using embeddings and **Qdrant** for semantic search across custom knowledge bases. * **Asynchronous Processing:** Used **Redis** and queue-based background workers to handle chatbot requests efficiently and improve responsiveness. * **Context-Aware Responses:** Retrieved relevant information from vector databases before generating responses, enabling the chatbot to answer using domain-specific knowledge. * **Production Deployment:** Containerized the application using **Docker Compose** for consistent and reproducible deployments. * **Secure Access:** Deployed the system behind a **Cloudflare Tunnel**, avoiding direct exposure of public application endpoints. * **Monitoring & Reliability:** Implemented retries and queue-based processing to improve reliability during message ingestion and response generation.
### Overview I built and deployed a **production-grade AI-powered WhatsApp chatbot** that uses Retrieval-Augmented Generation (RAG) to generate context-aware responses from custom knowledge bases. The system integrates WhatsApp webhooks with a FastAPI backend and asynchronous workers for reliable message processing. ### Key Features * **WhatsApp Integration:** Integrated WhatsApp Business API webhooks using WAHA for receiving and processing user messages. * **RAG Pipeline:** Built a Retrieval-Augmented Generation pipeline using embeddings and **Qdrant** for semantic search across custom knowledge bases. * **Asynchronous Processing:** Used **Redis** and queue-based background workers to handle chatbot requests efficiently and improve responsiveness. * **Context-Aware Responses:** Retrieved relevant information from vector databases before generating responses, enabling the chatbot to answer using domain-specific knowledge. * **Production Deployment:** Containerized the application using **Docker Compose** for consistent and reproducible deployments. * **Secure Access:** Deployed the system behind a **Cloudflare Tunnel**, avoiding direct exposure of public application endpoints. * **Monitoring & Reliability:** Implemented retries and queue-based processing to improve reliability during message ingestion and response generation.
