## 2 Previous Works  
This section presents some key publications that focus on the problem of wireless data acquisition using UAVs. In [3], [PERSON] and [PERSON] present a particle swarm optimization (PSO) algorithm for the optimized positioning of multiple UAVs for the data acquisition from wireless sensors. The algorithm uses a cost function which includes the value of the sensors, the network connectivity between the UAV and the ground station and the communication path quality. One limitation of the proposed approach is that it does not consider the movement of the UAVs between positions, but only the final position of the UAVs. In [4], [PERSON] et al. compare a one-side and a two-side matching algorithms for the data collection and the wireless charging of remote sensors using UAVs. Their problem description assumes that the UAVs return to the base station after visiting each sensor cluster. The utility function used includes the distance between the UAV and the sensor cluster, the amount of data to be collected and the residual energy of the sensors in the sensor cluster. In [5], [PERSON] et al. propose a Direct Future Prediction model to the path planning of a single UAV for the data collection of wireless sensors. They define a value of information (VoI) function which reduces with time following an event and they use a neural network to predict this VoI. Their method also considers the energy of the UAV and guides the UAV towards the charging station when the energy is below a set threshold. They show that their method performs better than Q-learning and Deep Q-Learning. One limitation of their method is the coarse granularity of the environment representation used as they simply divided the search space in a 5 by 5 grid. In [9], [PERSON] and [PERSON] use dynamic programming to compute the optimal speed for a UAV to collect data from wireless sensors distributed over a line. Their approach can be applied to 2D in the case where a trajectory for the UAV is available beforehand. In [10], [PERSON] et al. decompose the wireless sensor acquisition problem into two sub-problems. The first is essentially the multi-Traveling Salesperson Problem (mTSP) and consists of assigning the wireless sensors to the UAVs and finding in which order they are visited. The second problem consists of minimizing the flight time of each UAV while ensuring that all the data is collected. UAVs are allowed to reduce their speed or hover to ensure sufficient connection time with the wireless sensors. In [11], [PERSON] and [PERSON] propose a hybrid GA coupled with an Ant Colony Optimization (ACO) for wireless sensor data acquisition by a single UAV. The GA is used to find the order in which the sensors are visited basically solving the TSP and the ACO is used at every step of the GA to calculate paths between sensors. They demonstrated their algorithm on a 10 by 10 discretized map. Their work includes very good descriptions of how GA operators can be adapted to solve the TSP problem. In [12], [PERSON] et al. present a Lagrange multiplier-based method to solve simultaneously the wireless sensor data acquisition problem and the wireless power transfer problem. One key characteristic of their approach is that they used a transmission radius around the sensor and the UAV does not have to overfly the sensor, but merely enter the circle defined by this radius. This minimizes the overall distance travelled by the UAV. In [13], [PERSON] et al. use a Shuffled Frog-Leaping Algorithm (SFLA) to solve the wireless sensor data acquisition problem considering the maximum transmission range of the sensors. The SFLA uses a PSO-liked optimization on every candidate solution to select the best hovering point within the transmission range of the wireless sensor. In [14], [PERSON] et al. add a constraint to the problem of wireless sensor data acquisition where the data at a wireless sensor must be collected by a UAV before it becomes outdated. They use a branch, reduce and bound algorithm to find the optimal global solution for relatively small-scale scenarios.