Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/129653
Title: Adaptation of the LoRa transmission protocol for a low-power low-cost indoor air quality monitoring system
Other Titles: Urban pollution - environmental challenges in healthy modern cities
Authors: Casha, Owen
Meli, Matthew
Gatt, Edward
Grech, Ivan
Micallef, Joseph
Keywords: Indoor air quality
Internet of things
Air pollution
Wide area networks (Computer networks)
Issue Date: 2024
Publisher: IntechOpen
Citation: Casha, O., Meli, M., Gatt, E., Grech, I., & Micallef, J. (2024). Adaptation of the LoRa transmission protocol for a low-power low-cost indoor air quality monitoring system. In C. F. Bustillo-Lecompte (Ed.), Urban pollution - environmental challenges in healthy modern cities (pp. 1-33). IntechOpen.
Abstract: Many state-of-the-art air quality sensor nodes feature a very 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 is not yet 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 air quality monitoring wireless sensor node 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.
URI: https://www.um.edu.mt/library/oar/handle/123456789/129653
Appears in Collections:Scholarly Works - FacICTMN



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