{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T05:27:35Z","timestamp":1769059655234,"version":"3.49.0"},"reference-count":24,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T00:00:00Z","timestamp":1768953600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper presents the design and optimisation of a low-power embedded sensor-node architecture for real-time environmental monitoring with on-board machine-learning inference. The proposed system integrates heterogeneous sensing elements for air quality and ambient parameters (temperature, humidity, gas concentration, and particulate matter) into a modular embedded platform based on a low-power microcontroller coupled with an energy-efficient neural inference accelerator. The design emphasises end-to-end energy optimisation through adaptive duty-cycling, hierarchical power domains, and edge-level data reduction. The embedded machine-learning layer performs lightweight event\/anomaly detection via on-device multi-class classification (normal\/anomalous\/critical) using quantised neural models in fixed-point arithmetic. A comprehensive system-level analysis, performed via MATLAB Simulink simulations, evaluates inference accuracy, latency, and energy consumption under realistic environmental conditions. Results indicate that the proposed node achieves 94% inference accuracy, 0.87 ms latency, and an average power consumption of approximately 2.9 mWh, enabling energy-autonomous operation with hybrid solar\u2013battery harvesting. The adaptive LoRaWAN communication strategy further reduces data transmissions by \u224888% relative to periodic reporting. The results indicate that on-device inference can reduce network traffic while maintaining reliable event detection under the evaluated operating conditions. The proposed architecture is intended to support energy-efficient environmental sensing deployments in smart-city and climate-monitoring contexts.<\/jats:p>","DOI":"10.3390\/s26020703","type":"journal-article","created":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T13:59:54Z","timestamp":1769003994000},"page":"703","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Low-Power Embedded Sensor Node for Real-Time Environmental Monitoring with On-Board Machine-Learning Inference"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8872-5721","authenticated-orcid":false,"given":"Manuel J. C. S.","family":"Reis","sequence":"first","affiliation":[{"name":"Engineering Department, Institute of Electronics and Informatics Engineering of Aveiro (IEETA), University of Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e34","DOI":"10.1017\/eds.2024.36","article-title":"The Promise of Neuromorphic Edge AI for Rural Environmental Monitoring","volume":"3","author":"Aral","year":"2024","journal-title":"Environ. Data Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"100114","DOI":"10.1016\/j.heha.2024.100114","article-title":"Artificial Intelligence in Environmental Monitoring: Advancements, Challenges, and Future Directions","volume":"12","author":"Olawade","year":"2024","journal-title":"Hyg. Environ. 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