  Unmanned aerial vehicles (UAVs) with mounted base stations are a promising
technology for monitoring smart farms. They can provide communication and
computation services to extensive agricultural regions. With the assistance of
a Multi-Access Edge Computing infrastructure, an aerial base station (ABS)
network can provide an energy-efficient solution for smart farms that need to
process deadline critical tasks fed by IoT devices deployed on the field. In
this paper, we introduce a multi-objective maximization problem and a
Q-Learning based method which aim to process these tasks before their deadline
while considering the UAVs' hover time. We also present three heuristic
baselines to evaluate the performance of our approaches. In addition, we
introduce an integer linear programming (ILP) model to define the upper bound
of our objective function. The results show that Q-Learning outperforms the
baselines in terms of remaining energy levels and percentage of delay
violations.
