Location-based beamforming architecture for efficient farming applications with drones

This paper proposes a drone-based architecture with location-based beamforming (LBBF)and edge computing support for efficient crop harvesting and management in order to reduce the food waste in the food chain in farming applications. Monitoring the crop is a crucial part in the food chain. In this work, for monitoring purpose we consider synthetic aperture radar (SAR)mounted on the unmanned aerial vehicles (UAVs). In order to provide the edge computing information with good reliability, small latency and good throughput, we introduce a LBBF technique for the uplink connectivity. Firstly, the LBBF algorithm is proposed for the scenario where a single user is connected to the base station under analog beamforming scheme. Secondly, in the context of LBBF, we apply an optimization of the antenna size under the uniform rectangular array (URA)assumption. Thirdly, we implement a numerical analysis to compare LBBF with the traditional channel state information (CSI)-based beamforming. We show that the LBBF outperforms the CSI-based beamforming in the noisy environments according to the investigated performance metrics, namely the reliability of the connectivity and the capacity. In addition, the LBBF also has smaller latency than CSI-based beamforming.