Improving Monitoring of Indoor RF-EMF Exposure Using IoT-Embedded Sensors and Kriging

Abstract

Improving Monitoring of Indoor RF-EMF Exposure Using IoT-Embedded Sensors and Kriging Techniques Jabeur R, Alaerjan A. Improving Monitoring of Indoor RF-EMF Exposure Using IoT-Embedded Sensors and Kriging Techniques. Sensors. 2024; 24(23):7849. doi.org Abstract Distributed wireless sensor networks (WSNs) are widely used to enhance the quality and safety of various applications. These networks consist of numerous sensor nodes, often deployed in challenging terrains where maintenance is difficult. Efficient monitoring approaches are essential to maximize the functionality and lifespan of each sensor node, thereby improving the overall performance of the WSN. In this study, we propose a method to efficiently monitor radiofrequency electromagnetic fields (RF- EMF) exposure using WSNs. Our approach leverages sensor nodes to provide real-time measurements, ensuring accurate and timely data collection. With the increasing prevalence of wireless communication systems, assessing RF-EMF exposure has become crucial due to public health concerns. Since individuals spend over 70% of their time indoors, it is vital to evaluate indoor RF-EMF exposure. However, this task is complicated by the complex indoor environments, furniture arrangements, temporal variability of exposure, numerous obstructions with unknown dielectric properties, and uncontrolled factors such as people’s movements and the random positioning of furniture and doors. To address these challenges, we employ a sensor network to monitor RF-EMF exposure limits using embedded sensors. By integrating Internet of Things-embedded sensors with advanced modeling techniques, such as kriging, we characterize and model indoor RF-EMF downlink (DL) exposure effectively. Measurements taken in several buildings within a few hundred meters of base stations equipped with multiple cellular antennas (2G, 3G, 4G, and 5G) demonstrate that the kriging technique using the spherical model provides superior RF-EMF prediction compared with the exponential model. Using the spherical model, we constructed a high-resolution coverage map for the entire corridor, showcasing the effectiveness of our approach. Conclusions This study proposes the use of WSN to monitor the indoor RF-EMF exposure induced by cellular networks. To this end, we first proposed a measurement system based on the Narda NBM-550 and Nucleo-F401RE microcontroller board. The aim is to characterize and model indoor RF-EMF DL exposure using the collected measurements and kriging technique. First, several indoor measurements are conducted in an area covered by various frequency bands, including those used for 5G. By comparing the spherical and exponential models, we demonstrated that the spherical model provides a superior fit for predicting RF-EMF exposure levels. The high-resolution coverage map constructed using the spherical model revealed that the maximum average RF-EMF DL exposure levels within the corridor are well below the limits established by the ICNIRP. These findings underscore the effectiveness of the kriging technique in accurately modeling and predicting RF-EMF exposure in complex indoor environments. For future work, several avenues can be explored to enhance the understanding and assessment of indoor RF-EMF exposure. Firstly, expanding the measurement campaign to include a wider variety of indoor environments, such as residential buildings, offices, and public transport, would provide a more comprehensive dataset. Additionally, incorporating temporal variations by conducting long-term measurements could offer insights into the fluctuations of RF-EMF exposure over time. Furthermore, integrating advanced machine learning algorithms with the kriging technique could improve the accuracy and efficiency of exposure predictions. Another important research axis involves the analysis of measurement uncertainty, which is planned for future investigation. For large-scale deployments, the Narda NBM-550 can be replaced with frequency-selective equipment such as the ExpoM-RF4, MVG EME Spy Evolution, or Narda SRM-3006. This substitution ensures that only downlink bands are considered and enables the reconstruction of RF-EMF exposure maps for each frequency band, facilitating a more detailed assessment by frequency band. Finally, investigating the impact of emerging wireless technologies, such as beyond 5G, on indoor RF-EMF exposure will be crucial as these technologies become more widespread. These future directions will contribute to a more thorough understanding of indoor RF-EMF exposure and help address public concerns regarding wireless communication systems. Open access paper: mdpi.com

AI evidence extraction

Main findings

Indoor RF-EMF downlink exposure was monitored in several buildings within a few hundred meters of cellular base stations (2G/3G/4G/5G). Kriging with a spherical model provided better RF-EMF prediction than an exponential model, and the maximum average DL exposure levels in the mapped corridor were reported to be well below ICNIRP limits.

Outcomes measured

• Indoor RF-EMF downlink (DL) exposure measurements
• Spatial prediction/modeling of indoor RF-EMF exposure using kriging (spherical vs exponential models)
• High-resolution indoor RF-EMF coverage/exposure mapping
• Comparison of measured/predicted exposure levels to ICNIRP limits

Limitations

• No numeric exposure levels or uncertainty metrics reported in the provided abstract
• Sample size, number of buildings/measurement points, and measurement duration not specified
• Focus is on downlink exposure modeling in a corridor/buildings near base stations; generalizability to other indoor environments not established in the abstract
• Temporal variability and long-term measurements are noted as future work rather than addressed here
• Measurement uncertainty analysis is planned for future investigation

Suggested hubs

• who-icnirp (0.78)
  Exposure levels are compared to ICNIRP limits.
• 5g-policy (0.55)
  Measurements include bands used for 5G and base stations with 5G antennas.

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    "exposure": {
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        "source": "base station",
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    "outcomes": [
        "Indoor RF-EMF downlink (DL) exposure measurements",
        "Spatial prediction/modeling of indoor RF-EMF exposure using kriging (spherical vs exponential models)",
        "High-resolution indoor RF-EMF coverage/exposure mapping",
        "Comparison of measured/predicted exposure levels to ICNIRP limits"
    ],
    "main_findings": "Indoor RF-EMF downlink exposure was monitored in several buildings within a few hundred meters of cellular base stations (2G/3G/4G/5G). Kriging with a spherical model provided better RF-EMF prediction than an exponential model, and the maximum average DL exposure levels in the mapped corridor were reported to be well below ICNIRP limits.",
    "effect_direction": "no_effect",
    "limitations": [
        "No numeric exposure levels or uncertainty metrics reported in the provided abstract",
        "Sample size, number of buildings/measurement points, and measurement duration not specified",
        "Focus is on downlink exposure modeling in a corridor/buildings near base stations; generalizability to other indoor environments not established in the abstract",
        "Temporal variability and long-term measurements are noted as future work rather than addressed here",
        "Measurement uncertainty analysis is planned for future investigation"
    ],
    "evidence_strength": "low",
    "confidence": 0.7399999999999999911182158029987476766109466552734375,
    "peer_reviewed_likely": "yes",
    "keywords": [
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        "wireless sensor networks",
        "IoT-embedded sensors",
        "kriging",
        "spherical model",
        "exponential model",
        "base stations",
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