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

Abstract

Overview

Distributed wireless sensor networks (WSNs) enhance the quality and safety of various applications by providing real-time measurements of radiofrequency electromagnetic fields (RF-EMF). These sensor nodes are often deployed in challenging terrains where maintenance is difficult, making efficient monitoring crucial.

Findings

We leveraged Internet of Things (IoT) embedded sensors and advanced kriging modeling techniques to evaluate indoor RF-EMF exposure, which has become increasingly significant due to public health concerns. The approach was specifically effective since individuals spend over 70% of their time indoors, despite the complexities posed by indoor environments. Measurements taken in several buildings close to cellular base stations with various antenna systems (2G to 5G) showed that the spherical kriging model provided better predictions than the exponential model, effectively creating a high-resolution exposure map.

Conclusion

This study underscores the efficacy of using WSNs integrated with IoT and kriging techniques for modeling and predicting RF-EMF exposure. A measurement system, including the Narda NBM-550 and a microcontroller board, was utilized to enable this detailed monitoring and modeling. The findings reassure that maximum average RF-EMF DL (downlink) exposure levels are well below ICNIRP limits, emphasizing the safety and accuracy of the proposed method.

Future research directions involve expanding measurements to more varied environments, incorporating machine learning for improved prediction, and detailed frequency band assessment to enhance public safety and system efficacy further.