The Smart Queue Management System is an AI + IoT project that combines Inventory sensing and AI demand prediction, built with ESP32. The project follows a clean, modular pipeline where sensor data acquisition, AI processing, and presentation stay separated, making it easy to test, extend, and present. It showcases practical AI and IoT techniques while producing a working, demo-ready system.
Manual monitoring and decision making for this task is slow, inefficient, and cannot scale across many devices. Without an AI + IoT approach built on AI demand prediction and ESP32, users cannot collect data from the physical world and act on it intelligently in real time.
This project applies AI + IoT techniques through Inventory sensing, orchestrated with ESP32 and AI demand prediction. The system is designed for reliability and intelligence, with sensing, connectivity, AI analysis, and clear evaluation. It produces consistent, reusable results and can be adapted to related smart applications with minimal changes.
ESP32
Weight sensor
ESP32 / Arduino / Raspberry Pi
MQTT / HTTP
Cloud dashboard
Machine learning models
RFID reader
Modular IoT pipeline around Inventory sensing and AI demand prediction
Configurable sensor and AI settings
Real-time data collection and monitoring
Clear alerts, logging, and error handling
Reusable components for related smart systems
AI based decision and prediction
The project is layered: the sensing layer acquires data through Inventory sensing; the connectivity and AI layer processes it with ESP32 and AI demand prediction; and the output layer monitors and presents results via Store dashboard. Shared connectivity, alerting, and dashboard modules support all layers, keeping the system robust and easy to extend.
Set up the hardware, sensors, and development environment.
Build the sensor data acquisition layer with Inventory sensing.
Implement connectivity and AI processing using ESP32 and AI demand prediction.
Add the dashboard and alert layer via Store dashboard.
Wire up end-to-end flows and add error handling and retries.
Test with real data, tune AI models, and refine the system.
Package the project, document it, and prepare the demo and viva report.
Build production-style AI + IoT applications
Apply Customer analytics with AI and Demand forecasting
Connect and control IoT hardware
Analyze sensor data with machine learning
Present and defend a complete AI + IoT project in viva
Add edge AI for on-device decisions
Add more sensors and cloud services
Add mobile app control
Deploy on scalable cloud infrastructure
The Smart Queue Management System delivers a complete AI + IoT workflow — from sensor data acquisition and connectivity to AI analysis and presentation. It is practical, modern, and easy to explain, making it an excellent final year project that demonstrates in-demand AI and IoT skills.
