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

TL;DR

This paper presents a compact multi-modal IoT node with edge AI processing capabilities, enabling energy-efficient environmental monitoring and advanced data analysis directly on-device, suitable for smart pollution control and indoor air quality management.

Contribution

Introduces a novel, compact multi-sensor IoT node with integrated edge AI processing, supporting real-time ML inference for environmental monitoring applications.

Findings

Achieved 42% energy savings with on-device YOLOv5 occupancy detection.

Extended indoor air quality monitoring operation up to 143 hours on a single battery.

Demonstrated feasibility of real-time, energy-efficient edge AI in compact IoT devices.

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 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