Sensing in EMF Exposure Monitoring and Mastering

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