  In this paper, we study an unmanned aerial vehicle(UAV)-enabled wireless
sensor network, where a UAV is dispatched to collect the sensed data from
distributed sensor nodes (SNs) for estimating an unknown parameter. It is
revealed that in order to minimize the mean square error (MSE) for the
estimation, the UAV should collect the data from as many SNs as possible, based
on which an optimization problem is formulated to design the UAV's trajectory
subject to its practical mobility constraints. Although the problem is
non-convex and NP-hard, we show that the optimal UAV trajectory consists of
connected line segments only. With this simplification, an efficient suboptimal
solution is proposed by leveraging the classic traveling salesman problem (TSP)
method and applying convex optimization techniques. Simulation results show
that the proposed trajectory design achieves significant performance gains in
terms of the number of SNs whose data are successfully collected, as compared
to other benchmark schemes.
