Low cost sensor networks

Abstract

Objective: This study addresses the issue of using low-cost sensors to survey greenhouse gas emissions. Two sets of sensors were used in this study: one designed to be mounted on a drone and the other intended for stationary measurements. . The optimization of an algorithm in the operation of the sensors, presented here, was used to reduce energy consumption; every seven seconds, a scan is performed on all the sensors in the set. Theoretical Framework: The work presents an integrated system for monitoring emissions and tracking stability through statistical analyses, including the use of the Allan Variance method to analyze time-series data. This method is especially effective for assessing the noise and stability of measurements. Data acquisition from Arduino platforms offers a variety of hardware options, providing energy efficiency for logging applications. Method: The methodology outlined in this report enables the unified operation of multiple sensors, presenting a low-cost data logging platform designed for long-term use in remote or harsh environments. In this initial test, three breakout boards from the open-source Arduino ecosystem are integrated into the core of the data logger. The project also introduces drone technologies that expand the coverage area and extend the operational lifespan of the modular design. Results and Discussion: This article explores the feasibility of using low-cost sensors as an alternative method for monitoring greenhouse gases and particulate matter in situations where deploying reference-grade instruments are not practical. Stability tests were carried out by measuring the same air sample under consistent atmospheric conditions with both sensor sets over a period of several hours. Long-term testing of the low-cost sensor system took place in an open field, with experiments spanning 26 hours. The Allan deviation plot, derived from these measurements for both sensor sets, is presented. The results indicate that the CO2 precision of the system is better than 20 ppm. Research Implications: The sensor system presented offers an innovative, low-cost, and portable solution for detecting greenhouse gas emissions. Importantly, the system has proven to be reliable, effectively capturing fluctuations and associated uncertainties as demonstrated through statistical analyses. Widespread deployment of such measurement stations could enable the creation of a comprehensive monitoring network, providing the coverage and responsiveness needed to address climate risks like those we currently face. Originality/Value of the Research: This study presents the development of a low-cost sensor system designed for practical and efficient environmental monitoring. The system is adaptable for deployment over large areas, whether through a network of fixed stations or mobile drone platforms. Data acquired during the measurements are systematically stored and analyzed using Allan variance statistics, processed with OriginPro software, ensuring a robust assessment of sensor stability and precision.