Efficient Multi-UAV Assisted Data Gathering Schemes for Maximizing the Operation Time of Wireless Sensor Networks in Precision Farming

Abstract

Measurement data from wireless sensors deployed in large agricultural areas could be used to help the automation of precision farming activities like irrigation management, fertilization, etc. The widespread use of sensors with limited battery capacity in precision farming largely depends on data collection methods that reduce the energy consumption of transmitting measurement data and prolong the battery run time. In this paper, we investigate joint clustering and multi-UAV-assisted data-gathering schemes to save the energy consumption of sensors. We establish a theoretical lower bound for the energy consumption of sensors to transport data to cluster heads and prove that the energy consumption of sensors approaches the theoretical lower bound if clusters are balanced regarding energy consumption. Therefore, the essential step of proposed heuristic multi-UAV schemes, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Gathering data Assisted by Multi-UAV with a BAlanced Clustering</i> (GAMBAC), is to find balanced or near-balanced clusters concerning energy consumption. For sensor networks with a small number of nodes our heuristic algorithms give results close to the ones obtained by the reference solution. Numerical results show that the GAMBAC schemes extend network lifetime and requires less energy to support a specific number of collection rounds than the best existing approach. Numerical results show that the GAMBAC schemes extend network lifetime and requires less energy to support a specific number of collection rounds than the best existing approach. Therefore, the GAMBAC algorithms could enhance the reliable data collection of sensor networks for precision farming.