Design of an Embedded IoT Multi-Parameter Detection System Based on Edge Intelligence

Abstract

To address limited monitoring dimensions, high false alarm rates caused by fixed thresholds, long response latency in cloud-centered detection, and high communication load in small equipment cabins, equipment cabinets, and laboratory environments, this paper develops an embedded IoT multi-parameter detection system based on edge intelligence. The terminal integrates temperature, humidity, vibration, current, voltage, and gas sensing, and performs filtering, sliding-window feature extraction, and lightweight anomaly detection locally. A fusion model combining threshold rules with Isolation Forest classifies operating states as Normal, Warning, or Critical, while MQTT-based graded transmission uploads status summaries, window features, and abnormal events according to risk level. A 24 h experiment collected 86,400 synchronized records and generated 2,879 windows, followed by a 72 h stability test. The proposed method achieved 96.8% accuracy, a 94.6% F1 value, a 31.4 ms average edge decision time, and a 151.3 ms total alarm response time. Compared with cloud raw-data centralized detection, the response time decreased from 465.2 ms to 151.3 ms and the uploaded data volume from 18.6 MB/h to 3.1 MB/h, supporting real-time equipment monitoring and local environmental safety warning.