Jabeur Randa, Alaerjan Alaa
Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72341, Saudi Arabia.
Sensors (Basel). 2024 Dec 8;24(23):7849. doi: 10.3390/s24237849.
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.
分布式无线传感器网络（WSN）被广泛用于提高各种应用的质量和安全性。这些网络由众多传感器节点组成，通常部署在维护困难的具有挑战性的地形中。高效的监测方法对于最大化每个传感器节点的功能和寿命至关重要，从而提高WSN的整体性能。在本研究中，我们提出了一种使用WSN有效监测射频电磁场（RF-EMF）暴露的方法。我们的方法利用传感器节点提供实时测量，确保准确及时的数据收集。随着无线通信系统的日益普及，由于公众健康问题，评估RF-EMF暴露变得至关重要。由于个人70%以上的时间都在室内度过，评估室内RF-EMF暴露至关重要。然而，这项任务因复杂的室内环境、家具布置、暴露的时间变化、众多具有未知介电特性的障碍物以及诸如人们的移动和家具与门的随机定位等不可控因素而变得复杂。为了应对这些挑战，我们采用传感器网络使用嵌入式传感器监测RF-EMF暴露极限。通过将物联网嵌入式传感器与先进的建模技术（如克里金法）相结合，我们有效地对室内RF-EMF下行链路（DL）暴露进行了表征和建模。在配备多个蜂窝天线（2G、3G、4G和5G）的基站几百米范围内的几栋建筑物中进行的测量表明，与指数模型相比，使用球形模型的克里金技术提供了更好的RF-EMF预测。使用球形模型，我们为整个走廊构建了高分辨率覆盖图，展示了我们方法的有效性。
