Design and Implementation of an Intelligent Embedded Monitoring System Based on Multi-Sensor Fusion and Lightweight AI Algorithms

Abstract

With the rapid development of the Internet of Things, edge computing, and artificial intelligence, intelligent embedded monitoring systems are increasingly required to provide accurate, real-time, and low-latency sensing capabilities under resource-constrained conditions. This study proposes an intelligent embedded monitoring system based on multi-sensor fusion and lightweight artificial intelligence algorithms. The system integrates heterogeneous sensor data, including temperature, humidity, vibration, light intensity, and other environmental or equipment-related parameters, and employs a multi-sensor fusion mechanism to improve the reliability and robustness of monitoring information. To address the limited computational resources of embedded platforms, lightweight machine learning models are introduced to perform real-time anomaly detection and status classification at the edge. A complete system architecture is designed, covering sensor acquisition, data preprocessing, feature extraction, information fusion, AI inference, and monitoring visualization. The proposed system is implemented and evaluated on an embedded computing platform through a series of experimental scenarios. The results demonstrate that the combination of multi-sensor fusion and lightweight AI algorithms can effectively improve monitoring accuracy while reducing computational overhead and inference latency. The proposed approach provides a practical and scalable solution for intelligent equipment monitoring, environmental sensing, and edge-based anomaly detection applications.