Smart City Gnosys

Smart city article details

Title Lessons Learned From A Distributed Rf-Emf Sensor Network
ID_Doc 34992
Authors Aerts S.; Vermeeren G.; Van Den Bossche M.; Aminzadeh R.; Verloock L.; Thielens A.; Leroux P.; Bergs J.; Braem B.; Philippron A.; Martens L.; Joseph W.
Year 2022
Published Sensors, 22, 5
DOI http://dx.doi.org/10.3390/s22051715
Abstract In an increasingly wireless world, spatiotemporal monitoring of the exposure to environmental radiofrequency (RF) electromagnetic fields (EMF) is crucial to appease public uncertainty and anxiety about RF-EMF. However, although the advent of smart city infrastructures allows for dense networks of distributed sensors, the costs of accurate RF sensors remain high, and dedicated RF monitoring networks remain rare. This paper describes a comprehensive study comprising the design of a low-cost RF-EMF sensor node capable of monitoring four frequency bands used by wireless telecommunications with an unparalleled temporal resolution, its application in a small-scale distributed sensor network consisting of both fixed (on building façades) and mobile sensor nodes (on postal vans), and the subsequent analysis of over a year of data between January 2019 and May 2020, during which slightly less than 10 million samples were collected. From the fixed nodes’ results, the potential errors were determined that are induced when sampling at lower speeds (e.g., one sample per 15 min) and measuring for shorter periods of time (e.g., a few weeks), as well as an adequate resolution (30 min) for diurnal and weekly temporal profiles which sufficiently preserves short-term variations. Furthermore, based on the correlation between the sensors, an adequate density of 100 sensor nodes per km2 was deduced for future networks. Finally, the mobile sensor nodes were used to identify potential RF-EMF exposure hotspots in a previously unattainable area of more than 60 km2. In summary, through the analysis of a small number of RF-EMF sensor nodes (both fixed and mobile) in an urban area, this study offers invaluable insights applicable to future designs and deployments of distributed RF-EMF sensor networks. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Author Keywords Distributed sensor network; Radiofrequency electromagnetic fields (RF-EMF); Sensor node; Spatiotemporal exposure assessment


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