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

Abstract

Overview

Distributed wireless sensor networks (WSNs) are extensively utilized to enhance the quality and safety across various applications. These networks comprise numerous sensor nodes, often deployed in challenging environments, making their maintenance difficult. Efficient monitoring is crucial for maximizing functionality and longevity of each sensor node and optimizing WSN performance.

Findings

• This study proposes a method for efficient monitoring of radiofrequency electromagnetic fields (RF-EMF) exposure indoors using WSNs. The approach utilizes sensor nodes for real-time data collection and measurement.
• With growing wireless communication systems, monitoring RF-EMF exposure is critical due to ongoing public health concerns regarding electromagnetic field safety.
• Given that people spend over 70% of their time indoors, there is a heightened need to assess indoor RF-EMF exposure. However, the complex indoor environment—with variable furniture arrangement, unpredictable obstructions, and uncontrolled human movement—poses significant challenges for accurate assessment.
• The proposed system uses Internet of Things (IoT)-embedded sensors and advanced modeling (kriging) to characterize and model indoor RF-EMF downlink exposure.
• Measurements in buildings near base stations supporting multiple cellular generations (2G, 3G, 4G, 5G) revealed that the kriging technique, especially with the spherical model, provides superior predictions of indoor RF-EMF compared to the exponential model. This enabled construction of high-resolution RF-EMF coverage maps.

Conclusion

• The study presents a WSN-based measurement system (Narda NBM-550, Nucleo-F401RE microcontroller) to monitor and model indoor RF-EMF induced by cellular networks.
• Spherical models proved more effective than exponential models in predicting exposure levels.
• Despite the advanced modeling, observed RF-EMF levels were well below ICNIRP guidelines in measured environments, but the study emphasizes the necessity of ongoing monitoring due to public health risks associated with prolonged EMF exposure.
• Future research should include broader measurement campaigns across diverse environments, integrate long-term monitoring, utilize advanced machine learning with kriging, and analyze measurement uncertainty. Evaluating the impact of newer wireless technologies ("beyond 5G") will also be critical for ongoing RF-EMF risk assessment and public health safety.