Book chapter on a low-power low-cost indoor air quality monitoring system

Prof. Ing. Owen Casha, Ing. Matthew Meli, Prof. Ing. Edward Gatt, Prof. Ivan Grech, and Prof. Ing. Joseph Micallef, from the Department of Microelectronics & Nanoelectronics, co-authored and published an open-access peer-reviewed book chapter titled ‘Adaptation of the LoRa Transmission Protocol for a Low-Power Low-Cost Indoor Air Quality Monitoring System’ in Urban Pollution - Environmental Challenges in Healthy Modern Cities, which is a book edited by Prof. Ciro Fernando Bustillo-Lecompte (IntechOpen: London and Rijeka, 2024).

IntechOpen is the world's leading publisher of open-access books.

Many state-of-the-art air quality sensor nodes exhibit high power consumption. This limits them to being either mains powered or having a very short battery longevity. Moreover, a detailed study on their power consumption has not yet been presented. Despite their high manufacturing cost, their accuracy and sensing functionality are often limited too. This chapter presents the design of an innovative, low-power, and low-cost wireless sensor node for air quality monitoring with extensive measurement capabilities. The design adapts the LoRa transmission protocol by configuring and optimising the bandwidth and the spreading factor values.

An optimal balance between data rate, range, and power was achieved. In addition to providing a thorough literature and market survey on available solutions, the work carried out on a scalable, low-cost big data capture and analysis system is also discussed. The proposed sensor node can accurately measure carbon dioxide, volatile organic compounds, particulate matter, temperature, relative humidity, and atmospheric pressure. The device features an average energy consumption of 327 μAh and a 40-month autonomy with a 10,500 mAh battery. The low-cost factor enables the provision of a large-scale system. Multiple nodes, distributed across a university campus, provide extensive location-based data and LoRa metadata, which enable comprehensive data analysis.