## 1 Introduction
This problem is known as the data mule problem [8]. A concrete example of the data mule problem would be in a military operation where wireless sensors are deployed from an aircraft over a terrain to monitor the environment and UAVs are used to collect the information from the sensors. Following a natural disaster such as flooding, sensors could also be deployed to monitor the water level and UAVs could be used to collect the information from the sensors. There are many other scenarios where UAVs would be helpful to collect the data from wireless sensors. In this UAV-based data collection, the path planning and coordination of the UAVs is a complex problem, especially if done in a complex 3D environment with obstacles. This present paper proposes a framework for the wireless sensor data acquisition using a team of UAVs. The sensors are scattered over a 3D environment which also includes some no-fly zones or obstacles that must be avoided by the UAVs. The proposed framework uses an iterative method based on the k-means clustering algorithm to group the sensors into clusters and identify the location of DPs, locations where the UAVs hover to download the data from the sensors that are part of the cluster. A sensor is associated to a DP when it is within communication range and in line of sight. After the locations of the DPs have been calculated, the framework uses a SSSP algorithm to find the optimal path between all possible pairs of DPs. This ensures that the final trajectory for each UAV includes the sequence of DPs to visit, but also the path between the DPs. The SSSP algorithm uses multiple grids of nodes superposed at the same location to impose a cost when changing direction. After the SSSP has run, a Genetic Algorithm (GA) is used to assign the DPs to the UAV and to find the order in which the DPs are visited. The GA is supplemented with a 2-opt local search heuristic to accelerate convergence. The results of the SSSP and the GA are combined to build complete trajectories for each UAV. A model of the UAV based on experimental flights is used to estimate the speed and the power consumption of the UAVs along their trajectory. Finally, a collision-avoidance strategy is implemented to ensure collision-free trajectories. Being the most time-consuming part of the framework, the SSSP algorithm is parallelized on a Graphics Processing Unit (GPU) and executes 33.3\(\times\) faster than the sequential implementation on CPU. The framework is tested on two different 3D maps with scenarios containing 70 and 100 sensors grouped into 15, 40, and 55 DPs. This paper makes three main contributions which highlight its novelty compared to previous works. Firstly, it presents a complete framework for the problem of wireless sensor data acquisition using multiple UAVs including optimized trajectories between DPs and a collision-avoidance strategy. Secondly, it expands on previous research by solving the problem in a 3D environment instead of 2D. Thirdly, it parallelizes the SSSP algorithm on GPU to reduce the runtime of the overall framework by a factor of 33.3\(\times\) which allows for the fast computation of the UAV trajectories. The rest of the paper is organized as follows. Section 2 covers some key publications on the problem of wireless sensor data acquisition using UAVs. Section 3 formulates the problem. Section 4 describes the architecture and the programming model of GPUs. The modeling of the UAV speed and power consumption is described in Section 5. The details of the proposed framework are presented in Section 6. Finally, Section 7 presents the details and results of the numerical simulations.