As the roll-out of the fifth generation (5G) of mobile telecommunications is well underway, standardized methods to assess the human exposure to radiofrequency electromagnetic fields from 5G base station radios are needed in addition to existing numerical models and preliminary measurement studies. Challenges following the introduction of 5G New Radio (NR) include the utilization of new spectrum bands and the widespread use of technological advances such as Massive MIMO (Multiple-Input Multiple-Output) and beamforming. We propose a comprehensive and ready-to-use exposure assessment methodology for use with common spectrum analyzer equipment to measure or calculate in-situ the time-averaged instantaneous exposure and the theoretical maximum exposure from 5G NR base stations. Besides providing the correct method and equipment settings to capture the instantaneous exposure, the procedure also comprises a number of steps that involve the identification of the Synchronization Signal Block, which is the only 5G NR component that is transmitted periodically and at constant power, the assessment of the power density carried by its resources, and the subsequent extrapolation to the theoretical maximum exposure level. The procedure was validated on site for a 5G NR base station operating at 3.5 GHz, but it should be generally applicable to any 5G NR signal, i.e., as is for any sub-6 GHz signal and after adjustment of the proposed measurement settings for signals in the millimeter-wave range.

The development of new wireless communication technologies that emit radio frequency electromagnetic fields (RF-EMF) is ongoing, but little is known about the RF-EMF exposure distribution in the general population. Previous attempts to measure personal exposure to RF-EMF have used different measurement protocols and analysis methods making comparisons between exposure situations across different study populations very difficult. As a result, observed differences in exposure levels between study populations may not reflect real exposure differences but may be in part, or wholly due to methodological differences. The aim of this paper is to develop a study protocol for future personal RF-EMF exposure studies based on experience drawn from previous research. Using the current knowledge base, we propose procedures for the measurement of personal exposure to RF-EMF, data collection, data management and analysis, and methods for the selection and instruction of study participants. We have identified two basic types of personal RF-EMF measurement studies: population surveys and microenvironmental measurements. In the case of a population survey, the unit of observation is the individual and a randomly selected representative sample of the population is needed to obtain reliable results. For microenvironmental measurements, study participants are selected in order to represent typical behaviours in different microenvironments. These two study types require different methods and procedures. Applying our proposed common core procedures in future personal measurement studies will allow direct comparisons of personal RF-EMF exposures in different populations and study areas.

Unmanned Aerial Vehicles (UAV) are part of precision agriculture; also, their impact on fast deployable wireless communication is offering new solutions and systems never envisioned before such as collecting information from underground sensors by using low power Internet of Things (IoT) technologies. In this paper, we propose a (Narrow Band IoT) NB-IoT system for collecting underground soil parameters in potato crops using a UAV-aided network. To this end, a simulation tool implementing a gateway mounted on a UAV using NB-IoT based access network and LTE based backhaul network is developed. This tool evaluates the performance of a realistic scenario in a potato field near Bogota, Colombia, accounting for real size packets in a complete IoT application. While computing the wireless link quality, it allocates access and backhaul resources simultaneously based on the technologies used. We compare the performance of wireless underground sensors buried in dry and wet soils at four different depths. Results show that a single drone with 50 seconds of flight time could satisfy more than 2000 sensors deployed in a 20 hectares field, depending on the buried depth and soil characteristics. We found that an optimal flight altitude is located between 60 m and 80 m for buried sensors. Moreover, we establish that the water content reduces the maximum reachable buried depth from 70 cm in dry soils, down to 30 cm in wet ones. Besides, we found that in the proposed scenario, sensors' battery life could last up to 82 months for above ground sensors and 77 months for the deepest buried ones. Finally, we discuss the influence of the sensor's density and buried depth, the flight service time and altitude in power-constrained conditions and we propose optimal configuration to improve system performance.