Development and Performance Evaluation of a Real-Time Smart Warehouse Environmental Monitoring System Based on Internet of Things

Abstract

The primary objectives of this research are twofold: first, to design and develop a real-time smart warehouse environmental monitoring system utilizing Internet of Things (IoT) technology; and second, to evaluate the system's performance and operational efficacy. This system addresses the critical limitations of traditional manual inspections, particularly latency, data discontinuity, and human error. The system architecture employs an ESP32 microcontroller as the central processing unit, seamlessly integrated with a sensor suite designed to monitor six key environmental parameters: temperature, relative humidity, PM2.5 and PM10 particulate matter, smoke concentration, and flame detection. The system is configured to continuously acquire and transmit data to the ThingSpeak cloud platform at 2-second intervals. Furthermore, it incorporates an automated alert mechanism via the LINE Messaging API, triggered whenever parameters exceed predefined safety thresholds. Performance evaluation conducted in a simulated warehouse environment demonstrated exceptional system stability, sustaining continuous data transmission for over 31 hours with zero data loss. In terms of measurement accuracy, the test results confirmed high sensor reliability, with the DHT22 sensor achieving an accuracy rate of 98.15%. Regarding system latency, the platform successfully processed and dispatched emergency notifications within a rapid timeframe of 3 to 5 seconds. From an operational management perspective, the implementation of this automated system reduced manual inspection time and associated labor costs by 40% compared to traditional manual logging methods. Ultimately, the development of this innovation significantly elevates warehouse safety standards, mitigates the risk of product degradation, and concretely enhances warehouse management efficiency, aligning with the paradigms of Industry 4.0