UAV-IoUT: Empirical Characterization and Energy Efficient Data Collection in UAV-Enabled Internet of Underground Things

Uncrewed Aerial Vehicles (UAVs) have emerged as highly adaptable equipment in modern agriculture, transforming traditional farming practices with data-driven decision-making to improve crop yields. While UAVs are mainly employed for aerial imaging and surveillance, their potential for collecting underground (UG) sensor data is underexplored. This paper investigates the path loss and fading characteristics between UAV and UG nodes using outdoor measurements, to facilitate energy-efficient data collection for air-to-underground wireless links. We show significant impacts from UAV antenna position, UAV 3D location, and soil properties on both path loss and fading. A novel channel model is developed that estimates the path loss with an average RMSE improvement of 3.16 dB and a maximum improvement of up to 10.45 dB over previous models in various 3D positions of the UAV. The analysis extends to the fading distribution within the channel, which conforms to the Rician distribution, where the Rician-K is contingent upon the UAV's altitude, elevation angle, and antenna configurations. Specifically, two Gaussian functions are derived to capture the effects attributed to the altitude and elevation angle, with root mean square errors (RMSEs) of 2.86 dB -4.38 dB and 2.1 dB -6.1 dB, respectively. The developed model is employed to drive an energy-efficient data collection strategy, UAV-Collect, for UAV-aided Internet of Underground Things. UAV-Collect substantially reduces the energy use of UG sensor nodes. Our approach has the potential to optimize resources on UAV and UG nodes, enabling efficient agricultural monitoring.