Adaptive Threshold Algorithms for Energy Optimization in LoRa-Based Air Quality Monitoring Networks

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Chinese explanation / 中文解读

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Original abstract

This paper presents the design and evaluation of an energy-efficient multisensor platform for air-quality monitoring based on LoRa communication and the TEEN routing protocol.The system integrates a low-power PIC18F45K22 microcontroller, SPEC electrochemical sensors for CO and NO₂ detection, and an LMP91000 analog front-end, powered by a photovoltaic energy-harvesting subsystem.The proposed architecture employs adaptive, threshold-based algorithms to minimize the frequency of wireless data transmissions while maintaining high measurement accuracy.Four transmission strategies were implemented and tested to evaluate the trade-off between data fidelity and energy efficiency.Experimental validation was performed at the archaeological site Mediana (Niš, Serbia), where the system continuously measured temperature, humidity, and pollutant concentrations over seven days.Results demonstrate that the event-driven transmission method achieved correlation coefficients above 0.99 with an 85 % reduction in total energy consumption compared to continuous transmission.The findings confirm that integrating LoRa communication with TEEN-based routing provides a robust and sustainable framework for long-term environmental monitoring.The proposed platform offers scalability, autonomy, and low operational cost, making it suitable for next-generation IoT-based smart-city air-quality networks.

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