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A method for real-time error detection in low-cost environmental sensors data

A method for real-time error detection in low-cost environmental sensors data The purpose of this paper is to propose a simple, fast, and effective method for detecting measurement errors in data collected with low-cost environmental sensors typically used in building monitoring, evaluation, and automation applications.Design/methodology/approachThe method combines two unsupervised learning techniques: a distance-based anomaly detection algorithm analyzing temporal patterns in data, and a density-based algorithm comparing data across different spatially related sensors.FindingsResults of tests using 60,000 observations of temperature and humidity collected from 20 sensors during three weeks show that the method effectively identified measurement errors and was not affected by valid unusual events. Precision, recall, and accuracy were 0.999 or higher for all cases tested.Originality/valueThe method is simple to implement, computationally inexpensive, and fast enough to be used in real-time with modest open-source microprocessors and a wide variety of environmental sensors. It is a robust and convenient approach for overcoming the hardware constraints of low-cost sensors, allowing users to improve the quality of collected data at almost no additional cost and effort. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Smart and Sustainable Built Environment Market Emerald Publishing

A method for real-time error detection in low-cost environmental sensors data

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Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
2046-6099
DOI
10.1108/sasbe-10-2018-0051
Publisher site
See Article on Publisher Site

Abstract

The purpose of this paper is to propose a simple, fast, and effective method for detecting measurement errors in data collected with low-cost environmental sensors typically used in building monitoring, evaluation, and automation applications.Design/methodology/approachThe method combines two unsupervised learning techniques: a distance-based anomaly detection algorithm analyzing temporal patterns in data, and a density-based algorithm comparing data across different spatially related sensors.FindingsResults of tests using 60,000 observations of temperature and humidity collected from 20 sensors during three weeks show that the method effectively identified measurement errors and was not affected by valid unusual events. Precision, recall, and accuracy were 0.999 or higher for all cases tested.Originality/valueThe method is simple to implement, computationally inexpensive, and fast enough to be used in real-time with modest open-source microprocessors and a wide variety of environmental sensors. It is a robust and convenient approach for overcoming the hardware constraints of low-cost sensors, allowing users to improve the quality of collected data at almost no additional cost and effort.

Journal

Smart and Sustainable Built Environment MarketEmerald Publishing

Published: Jul 31, 2019

Keywords: Error detection; Environmental sensors; Environmental data cleaning; Smart buildings

References