AI-Powered Smart Warehouse Monitoring System Using NodeMCU ESP 32 - Yong Jin Wei; Bahari, Azlina (Progress in Engineering Application and Technology, 2026)
The rapid growth of e-commerce and global supply chains has increased the demand for efficient warehouse management. Traditional warehouse systems frequently struggle with inventory errors, inefficient space utilization, and inadequate environmental monitoring, resulting in increased operational costs and product losses. This project presents the development of an AI-powered Smart Warehouse Monitoring System that integrates IoT sensors (DHT11, MQ-2), RFID technology, and machine learning to optimize inventory management and environmental conditions. The system utilizes an ESP32 microcontroller to collect real-time data on temperature, humidity, and gas levels, transmitting this information to a Blynk cloud platform for remote monitoring and automated alerting. The results for this project demonstrate significant improvements in inventory accuracy and storage efficiency, validating the effectiveness of AI-powered innovative warehousing in reducing human error and preventing spoilage.