  In this paper, we propose a framework design for wireless sensor networks
based on multiple unmanned aerial vehicles (UAVs). Specifically, we aim to
minimize deployment and operational costs, with respect to budget and power
constraints. To this end, we first optimize the number and locations of cluster
heads (CHs) guaranteeing data collection from all sensors. Then, to minimize
the data collection flight time, we optimize the number and trajectories of
UAVs. Accordingly, we distinguish two trajectory approaches: 1) where a UAV
hovers exactly above the visited CH; and 2) where a UAV hovers within a range
of the CH. The results of this include guidelines for data collection design.
The characteristics of sensor nodes' K-means clustering are then discussed.
Next, we illustrate the performance of optimal and heuristic solutions for
trajectory planning. The genetic algorithm is shown to be near-optimal with
only $3.5\%$ degradation. The impacts of the trajectory approach, environment,
and UAVs' altitude are investigated. Finally, fairness of UAVs trajectories is
discussed.
