# Low-Cost Sensors in 5G RF-EMF Exposure Monitoring: Validity and Challenges

## TL;DR

This paper reviews the use of low-cost sensors for monitoring 5G RF-EMF exposure, highlighting their potential and challenges in accuracy and data management.

## Contribution

The paper introduces standardized calibration protocols and machine learning solutions to improve sensor reliability for 5G exposure monitoring.

## Key findings

- Well-calibrated low-cost sensors can achieve ±3–6 dB deviation compared to professional instruments.
- Outdoor 5G exposure levels near small cells range from 0.01 to 0.5 W/m2.
- Major challenges include calibration drift, frequency band gaps, and data interoperability.

## Abstract

The deployment of 5G networks has transformed the landscape of radiofrequency electromagnetic field (RF-EMF) exposure patterns, shifting from high-power macro base stations to dense networks of small, beamforming cells. This review critically assesses the validity, challenges, and research gaps of low-cost RF-EMF sensors used for 5G exposure monitoring. An analysis of over 60 studies covering Sub-6 GHz and emerging mmWave systems shows that well-calibrated sensors can achieve measurement deviations of ±3–6 dB compared to professional instruments like the Narda SRM-3006, with long-term calibration drift less than 0.5 dB per month and RMS reproducibility around 5%. Typical outdoor 5G FR1 exposure levels range from 0.01 to 0.5 W/m2 near small cells, while personal device use can cause transient exposures 10–30 dB higher. Although mmWave (24–100 GHz) and Wi-Fi 7/8 (~60 GHz) are underrepresented due to antenna and component limitations, Sub-6 GHz sensing platforms, including software-defined radio (SDR)-based and triaxial isotropic designs, provide sufficient sensitivity for both citizen and institutional monitoring. Major challenges involve calibration drift, frequency band gaps, data interoperability, and ethical management of participatory networks. Addressing these issues through standardized calibration protocols, machine learning-assisted drift correction, and open data frameworks will allow affordable sensors to complement professional monitoring, improve spatial coverage, and enhance public transparency in 5G RF-EMF exposure governance.