## 8 Discussion  
This paper makes three main contributions which also highlight its novelty compared to previous works. The first contribution is to propose a complete framework for the data acquisition from wireless sensors using a team of UAVs. In Section 2 of this paper, a literature survey of recent research works that focused on this problem was provided. Several of the papers focused on one aspect of the problem. Here, the proposed framework covers all the steps from the sensor clustering to the collision avoidance strategy. This last part is critical to any multi-UAV application, but is often omitted in papers related to data acquisition using UAVs. Several of the papers surveyed in Section 2 defined the problem of wireless sensor data acquisition using UAVs in a 2D environment or used a simplistic representation of the environment. A second contribution made in this paper is that it proposes a solution \begin{table}
\begin{tabular}{l r r r} \hline \hline \multirow{2}{*}{**Operation**} & \multicolumn{2}{c}{**Runtime (s)**} & \multirow{2}{*}{**Speed-Up**} \\ \cline{2-2} \cline{4-4}  & **Sequential** & & **Parallel** \\ \hline Positioning the DPs & 0.097 & - & 1.0\(\times\) \\ Build CSC graph & 0.447 & - & 1.0\(\times\) \\ Reduce graph & 0.223 & - & 1.0\(\times\) \\ Build CSR graph & 1.847 & - & 1.0\(\times\) \\ SSSP & 684.898 & 17.612 & 38.9\(\times\) \\ Fill cost matrix & 0.314 & 0.050 & 6.3\(\times\) \\ Genetic algorithm & 2.424 & 0.375 & 6.5\(\times\) \\ Build final path & 0.049 & - & 1.0\(\times\) \\ Collision avoidance & 0.029 & - & 1.0\(\times\) \\ \hline Total & 690.328 & 20.729 & 33.3\(\times\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Runtime and Speed-up for the Proposed Wireless Sensor Data Acquisition Algorithm (3 UAVs, 55 POIs, and resolution of 500 \(\times\) 294 \(\times\) 5). Figure 16: Speed-up of the three SSSP implementations tested. using a 3D representation with a high resolution of the terrain. The scenarios included a large number of sensors, hilly terrains, and no-fly zones in order to show that the proposed algorithm based on the SSSP and the GA is truly capable of calculating optimized trajectories in difficult and complex 3D environments. Finally, a third contribution is the parallel implementation of the algorithm on GPU. Given the computational complexity of the proposed framework, its runtime is too long to allow for a quick recalculation when a new sensors or obstacles are detected. To speed up the calculation, a parallel implementation of the SSSP algorithm proposed by [PERSON] et al. [28] was used and adapted to this specific application where multiple SSSP instances needed to be solved simultaneously. This parallel implementation on GPU resulted in a 33.3\(\times\) acceleration compared to a sequential implementation, a significant advantage when it comes to recalculating the UAV trajectories during the mission. In their future works, the authors of this paper intend to extend their proposed framework to include a communication network between the UAVs to relay the data collected from the sensors to the ground stations as the data is collected instead of waiting for the UAV to return to the ground station. This will increase the problem complexity as the UAVs will have to remain in communication range between themselves to maintain the network integrity, but the service provided would be far superior. Another research avenue they intend to explore is the implementation of the proposed framework in a field experiment using real UAVs and real wireless sensors. This would raise new challenges such as the imprecision of GPS localization, the limited computation capability of the UAVs, the limited bandwidth of the UAV-to-UAV communication, the management of hardware faults and the scheduling of UAV battery recharging. Moreover, in this present paper, the position of the sensors was assumed to be initially known. In a real scenario, the sensors would have to be discovered by the UAVs who would initially fly a search pattern over the terrain.