A Multi-Modal IoT Node for Energy-Efficient Environmental Monitoring with Edge AI Processing

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Abstract
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 analyzing complex environmental data efficiently. However, current IoT EM platforms are typically limited to a narrow set of sensors and lack the computational capabilities to support advanced ML and AI on the edge. To overcome these limitations, we introduce a compact (17x38 mm2), multi-modal, microcontroller based environmental IoT node integrating 11 sensors, including CO2 concentration, volatile organic compounds (VOCs), light intensity, UV radiation, pressure, temperature, humidity, 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 on-device ML processing. We implemented a YOLOv5-based occupancy detection pipeline (0.3M parameters, 42MOP 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, 3.7V battery.
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