  Due to the high mobility and easy deployment, unmanned aerial vehicles (UAVs)
have attracted much attention in the field of wireless communication and
positioning. To meet the challenges of lack of infrastructure coverage,
uncertain sensor position and large amount of sensing data collection in
wireless sensor network (WSN), this paper presents an efficient joint data
collection and sensor positioning scheme for WSN supported by multiple UAVs.
Specifically, a UAV is set as the main UAV to collect data, and other UAVs are
used as auxiliary UAVs for sensor positioning using time difference of arrival
(TDoA). A mixed-integer non-convex optimization problem with uncertain sensor
position is established. The goal is to minimize the average positioning error
of all sensors by jointly optimizing the UAV trajectories, sensor transmission
schedule and positioning observation points (POPs). To solve this optimization
model, the original problem is decomposed into two sub-problems based on the
path discrete method. Firstly, the block coordinate descent (BCD) and
successive convex approximation (SCA) techniques are applied to iteratively
optimize the trajectory of the main UAV and the sensor transmission schedule,
so as to maximize the minimum amount of data uploaded by the sensor. Then,
based on the trajectory of the main UAV, a particle swarm optimization
(PSO)-based algorithm is designed to optimize the POPs of UAVs. Finally, the
spline curve is applied to generate the trajectories of auxiliary UAVs. The
simulation results show that the proposed scheme can meet the requirements of
data collection and has a good positioning performance.
