Cost-Effective TinyML-Ready Design and Field Deployment of a Solar-Powered Environmental Monitoring Data Collector Using LTE-M Communication

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Authors: Emanuel-Crăciun TRÎNC, Valentin Adrian Niță, Cristina Stolojescu-Crișan, Cosmin Ancuţi, Răzvan Marius Mihai, Cristian Paţachia Sultănoiu
Cited by: 1
References: 19

Abstract

Environmental monitoring is essential for smart agriculture, renewable energy assessment, and climate-aware farm management. However, deploying autonomous sensing platforms in rural environments remains challenging because of energy constraints, communication reliability, and real-time processing requirements. This paper presents a modular, solar-powered environmental monitoring platform integrating LTE-M communication and TinyML-enabled edge sensing. The proposed system adopts a dual-microcontroller architecture that combines an Arduino Nano 33 BLE for real-time sensor acquisition and edge processing with an Arduino MKR NB 1500 dedicated to low-power wide-area communication. The platform integrates temperature, humidity, atmospheric pressure, rainfall, wind, and light sensors within a scalable framework. Two monitoring stations were deployed in rural regions of Romania to evaluate communication robustness, sensing stability, and energy autonomy. Field results demonstrated reliable LTE-M connectivity (4306 received signal strength indicator [RSSI] samples; mean −75.51 dBm) and strong agreement with a regional weather station, with mean deviations of −0.71 °C (temperature), 4.98% (humidity), and a stable pressure offset of −9.58 hPa attributable to altitude differences. Despite a total system cost of €315, the platform achieved measurement performance comparable to that of professional meteorological stations while maintaining long-term solar-powered operation. The proposed architecture provides a scalable and cost-effective solution for distributed smart agriculture and environmental monitoring applications.

Fields of Study & Subjects

Smart Agriculture and AI IoT Networks and Protocols Air Quality Monitoring and Forecasting

Publication details

Journal / source
Applied Sciences
Publisher
MDPI AG
Volume
16
Issue
7
Pages
3237–3237
Publication date
27 March 2026
Language
EN