一款17x38mm²的多模态物联网节点，支持边缘AI实时分析环境数据。
A Multi-Modal IoT Node for Energy-Efficient Environmental Monitoring with Edge AI Processing
- 集成11类传感器与低功耗芯片，实现多源环境数据采集
- 部署YOLOv5模型，较原始数据上传节能42%
- 支持智能空气监测与自适应采样，续航达143小时
物联网技术的普及推动了环境监测（EM）的发展，实现了低成本、可扩展的感知方案。然而，现有物联网平台通常仅支持有限传感器，难以全面评估环境状况，且缺乏足够算力部署先进机器学习和人工智能算法于边缘端。为此，我们提出一种紧凑（17×38 mm²）、基于MCU的多模态环境物联网节点，集成11种传感器，包括二氧化碳浓度、挥发性有机物（VOCs）、光照强度、紫外线辐射、气压、温度、湿度、通过RGB相机的视觉传感以及通过GNSS模块的精确定位。该节点搭载GAP9并行超低功耗系统级芯片，支持在设备端实时、高效地处理先进机器学习模型。我们实现了基于YOLOv5的占用检测流程（0.3 M参数，每推理42 MOP），相比原始数据流传输节省42%能耗。此外，我们构建了一套智能室内空气质量（IAQ）监测系统，结合占用检测与自适应采样率，在单节600 mAh、3.7 V电池下可运行长达143小时。该平台为预测性室内空气质量等创新应用奠定基础，支持高效的边缘智能预测，实现节能、自主、主动的污染治理控制策略。
原文摘要 · Abstract (English)
The widespread adoption of Internet of Things (IoT) technologies has significantly advanced environmental monitoring (EM) by enabling cost-effective and scalable sensing solutions. Concurrently, machine learning (ML) and artificial intelligence (AI) are introducing powerful tools for the efficient and accurate analysis of complex environmental data. However, current IoT platforms for environmental sensing are typically limited to a narrow set of sensors, preventing a comprehensive assessment of environmental conditions and lacking sufficient computational capabilities to support the deployment of advanced ML and AI algorithms on the edge. To overcome these limitations, we introduce a compact (17x38 mm2), multi-modal, MCU-based environmental IoT node integrating 11 sensors, including CO2 concentration, volatile organic compounds (VOCs), light intensity, UV radiation, pressure, temperature, humidity, visual sensing via an RGB camera, and precise geolocation through a GNSS module. It features GAP9, a parallel ultra-low-power system-on-chip, enabling real-time, energy-efficient edge processing of advanced ML models directly on-device. We implemented a YOLOv5-based occupancy detection pipeline (0.3 M parameters, 42 MOP per inference), demonstrating 42% energy savings over raw data streaming. Additionally, we present a smart indoor air quality (IAQ) monitoring setup that combines occupancy detection with adaptive sample rates, achieving operational times of up to 143 h on a single compact 600 mAh, 3.7 V battery. Our platform lays the groundwork for innovative applications such as predictive indoor IAQ, enabling efficient AI-driven on-edge forecasting for energy-efficient and autonomous, proactive pollution-mitigation control strategies
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