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Sensing in EMF Exposure Monitoring and Mastering

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Environmental Sensing".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 13055

Special Issue Editors


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Guest Editor
Chaire C2M, LTCI, Télécom Paris, 19 Place Marguerite Perey, 91120 Palaiseau, France
Interests: dosimetry; numerical methods; artificial intelligence and statistics applied in electromagnetism and stochastic dosimetry

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Guest Editor
Chaire C2M, LTCI, Télécom Paris, 19 Place Marguerite Perey, 91120 Palaiseau, France
Interests: cellular radio; stochastic geometry; EMF Exposure; sensor networks; monte carlo methods; neural networks; time series analysis

Special Issue Information

Dear Colleagues,

Nowadays, monitoring and mastering EMF exposure is key for, on one hand, the use of personal devices that have to control their emissions and, on the other hand, the deployment of any infrastructures of wireless communication systems. In the case of monitoring EMF induced by mobile phones, sensors can be used to detect body presence, forecast usage, limit the power emitted, adapt antenna impedance, or assess power emitted.

In terms of different types of monitoring equipment, the measurement of EMF exposure can be carried out using fixed-location sensors or portable devices, such as exposimeters and spectrum analyzers. Depending on the objectives they can be autonomic, energy-saving, can have a wide range of RF frequency, are wirelessly connected, and light. Sensors can also be designed to allow EMF-aware networks.

The post-processing of data collected includes temporal–spatial mapping, time series forecasting, abnormality detection and the use of statistical analysis, surrogate models or artificial neural networks. Recently, deep learning, especially artificial neural networks, are gaining more attention, due to their potential in solving problems in a wide range of disciplines.

To summarize, assessments regarding EMF exposure have different aspects: (1) type of measurement devices: fixed-location sensors or portable equipment; (2) type of RF sources: electronic devices or cellular base stations; and (3) type of application period: design or post-processing of sensor measurements. The interest of this Special Issue is focused on, but not limited to, the following areas of interest:

  • The design of sensors/exposimeter/spectrum analyzer in characterizing and recording EMF exposure;
  • Measurement protocols on environmental monitoring EMF exposure;
  • Drive test measurements of EMF exposure;
  • The design of sensors for body presence and usage;
  • The use of sensors for power limitation, antenna impedance adaptation, or power emitted assessment;
  • Post-processing of data measured by EMF sensors;
  • Time series analysis and anomaly detection of EMF sensor networks;
  • Temporal and spatial mapping of EMF exposure using machine learning/ANN-based approaches;
  • Statistical modeling on EMF exposure;
  • EMF exposure from 5G networks and devices.

Prof. Dr. Joe Wiart
Dr. Shanshan Wang
Guest Editors

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Keywords

  • monitoring EMF exposure
  • sensors
  • spatial mapping of exposure
  • drive test
  • spectrum analyzer
  • exposimeter
  • 5G networks
  • machine learning
  • ANN
  • time series

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Published Papers (3 papers)

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Research

15 pages, 14661 KiB  
Article
Human Exposure to Non-Ionizing Radiation from Indoor Distributed Antenna System: Shopping Mall Measurement Analysis
by Júlia da L. A. Silva, Vicente A. de Sousa, Jr., Marcio E. C. Rodrigues, Fred Sizenando Rossiter Pinheiro, Gutembergue Soares da Silva, Halysson B. Mendonça, Ricardo Q. de F. H. Silva, João V. L. da Silva, Fernanda E. S. Galdino, Vitor F. C. de Carvalho and Lucas I. C. Medeiros
Sensors 2023, 23(10), 4579; https://doi.org/10.3390/s23104579 - 9 May 2023
Cited by 2 | Viewed by 1895
Abstract
It is crucial to monitor the levels of Non-Ionizing Radiation (NIR) to which the general population may be exposed and compare them to the limits defined in the current standards, in view of the rapid rise of communication services and the prospects of [...] Read more.
It is crucial to monitor the levels of Non-Ionizing Radiation (NIR) to which the general population may be exposed and compare them to the limits defined in the current standards, in view of the rapid rise of communication services and the prospects of a connected society. A high number of people visits shopping malls and since these locations usually have several indoor antennas close to the public, it is therefore a kind of place that must be evaluated. Thus, this work presents measurements of the electric field in a shopping mall located in Natal, Brazil. We proposed a set of six measurement points, following two criteria: places with great the flow of people and the presence of one or more Distributed Antenna System (DAS), co-sited or not with WiFi access points. Results are presented and discussed in terms of the distance to DAS (conditions: near and far) and flow density of people in the mall (scenarios: low and high number of people). The highest peaks of electric field measured were 1.96 and 3.26 V/m, respectively corresponding to 5% and 8% of the limits defined by the International Commission on Non-Ionizing Radiation Protection (ICNIRP) and the Brazilian National Telecommunication Agency (ANATEL). Full article
(This article belongs to the Special Issue Sensing in EMF Exposure Monitoring and Mastering)
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11 pages, 3603 KiB  
Article
Assessment of the Electromagnetic Radiation Exposure at EV Charging Facilities
by Hongguk Bae and Sangwook Park
Sensors 2023, 23(1), 162; https://doi.org/10.3390/s23010162 - 23 Dec 2022
Cited by 8 | Viewed by 7493
Abstract
As the number of electric vehicles (EV) increases, the number of EV chargers also increases. Charging infrastructure will be built into our close environment. Because of this, the assessment of the electromagnetic field exposure generated from the charger is an important issue. This [...] Read more.
As the number of electric vehicles (EV) increases, the number of EV chargers also increases. Charging infrastructure will be built into our close environment. Because of this, the assessment of the electromagnetic field exposure generated from the charger is an important issue. This paper valuates the electromagnetic field exposure of six EV chargers. To assess the level of exposure of EV chargers, the electromagnetic fields from six chargers were measured and analyzed. In addition, measured electromagnetic field exposure levels were evaluated against ICNIRP guidelines. Higher electromagnetic fields were measured with standard chargers than with fast chargers. For the fast charger in the charging state, the magnetic field increased with the charging current. Electromagnetic field exposures for all six chargers did not exceed standard limits. The results of the assessment of the electromagnetic field exposure of the six EV chargers will contribute to the establishment of standards for the evaluation of the electromagnetic field exposure of the EV chargers in the future. Full article
(This article belongs to the Special Issue Sensing in EMF Exposure Monitoring and Mastering)
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14 pages, 3928 KiB  
Article
Towards Outdoor Electromagnetic Field Exposure Mapping Generation Using Conditional GANs
by Mohammed Mallik, Angesom Ataklity Tesfay, Benjamin Allaert, Redha Kassi, Esteban Egea-Lopez, Jose-Maria Molina-Garcia-Pardo, Joe Wiart, Davy P. Gaillot and Laurent Clavier
Sensors 2022, 22(24), 9643; https://doi.org/10.3390/s22249643 - 9 Dec 2022
Cited by 5 | Viewed by 2917
Abstract
With the ongoing fifth-generation cellular network (5G) deployment, electromagnetic field exposure has become a critical concern. However, measurements are scarce, and accurate electromagnetic field reconstruction in a geographic region remains challenging. This work proposes a conditional generative adversarial network to address this issue. [...] Read more.
With the ongoing fifth-generation cellular network (5G) deployment, electromagnetic field exposure has become a critical concern. However, measurements are scarce, and accurate electromagnetic field reconstruction in a geographic region remains challenging. This work proposes a conditional generative adversarial network to address this issue. The main objective is to reconstruct the electromagnetic field exposure map accurately according to the environment’s topology from a few sensors located in an outdoor urban environment. The model is trained to learn and estimate the propagation characteristics of the electromagnetic field according to the topology of a given environment. In addition, the conditional generative adversarial network-based electromagnetic field mapping is compared with simple kriging. Results show that the proposed method produces accurate estimates and is a promising solution for exposure map reconstruction. Full article
(This article belongs to the Special Issue Sensing in EMF Exposure Monitoring and Mastering)
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