RF-EMF Exposure Assessments in Greek Schools to Support Ubiquitous IoT-Based Monitoring in Smart Cities

Abstract

Everyday living environments concentrate a growing amount of wireless communications leading to increased public concern for radiofrequency (RF) electromagnetic fields (EMF) exposure. Recent technological advances are turning the focus on Internet of Things (IoT) systems that enable automated and continuous real-time EMF monitoring, facing however several challenges mainly stemming from infrastructural costs. This paper seeks to provide a comprehensive view of RF-EMF levels in Greece and evidence-based decision support for a spatially prioritized deployment of an IoT RF-EMF monitoring system. We applied the stratified sampling method to estimate Electric Field Strength (EFS) in the 27MHz-3GHz range in 661 schools. Three different residential areas were considered, i.e. urban, semi-urban and rural. Results showed that the 95% confidence interval for the EFS is (0.40, 0.44) with central value equal to the sample mean 0.42 V/m. We obtained strong evidence that the mean EFS value for all Greek schools is 0.42, which is 52 times lower than the Greek safety limit and equal to 1% of international limits. Mean EFS values of individual residential areas were also significantly below safety limits. Rural areas displayed the highest EFS peaks comprising the strongest candidate to start the deployment of an IoT RF-EMF monitoring system from.INDEX TERMS Radiofrequency electromagnetic fields (RF-EMF), RF-EMF exposure in schools, Internet of Things, RF-EMF monitoring, smart cities.

Assessment of Electromagnetic Field Exposure on European Roads: A Comprehensive In Situ Measurement Campaign

“…The obtained results also show that in a very dense urban area, the electric field values are higher compared to rural ones. These observations are also confirmed by the results of other authors [38].…”

“…The obtained results also show that in a very dense urban area, the electric field values are higher compared to rural ones. These observations are also confirmed by the results of other authors[38].Analyzing the data from all measurements, we see that the mean and median of the E-field on the y-axis of LSProbe are slightly higher for the road sections presented in Figures3-6, 9-11, 13, 14 and 16-24 compared to the E-field values on the other two axes. These results are due to the complex environment in which the electromagnetic waves propagate in the car cabin.…”

Frequency-selective and Broadband Measurements of Radio Frequency Electromagnetic Field Levels in the University Campus

“…The research on identifying RF-EMF exposure-sensitive areas is directed toward high-density population microenvironments with multiple and diverse emitting sources, such as schools, hospitals, and shopping malls. The results of potential worst-case scenario of personal exposure to RF-EMF for specific wireless-enabled workplaces and environments are presented in [3] whereas results of indoor and outdoor measurements and RF-EMF personal exposure for pre-university education institutions environments are presented in [4][5][6].…”

Multivariate Time-Series Forecasting: A Review of Deep Learning Methods in Internet of Things Applications to Smart Cities

“…Going beyond the use of digital technologies for better resource use and less emissions, the development of smart cities entails smarter urban transportation networks, more responsive and interactive administration, improved water supply and waste disposal facilities, more efficient building lighting and heating systems, safer public places, and more. To this end, smart cities employ Internet of Things (IoT) devices, such as connected sensors, embedded systems, and smart meters, to collect various measurements at regular intervals (time-series data), which are subsequently analyzed and ultimately used to improve infrastructure and services [3].…”