# UAV Path Optimization for Precision Agriculture Wireless Sensor Networks
## 5 Results  
For each scenario (\(S_{1}\),..., \(S_{5}\)), we carry out \(2\times 10^{3}\) simulations for scenario, running a total \(10^{4}\) simulations for the presented average results. The descriptive results are presented in Table 3. The table presents the main evaluated metrics for the five scenarios. For each scenario, we considered 5, 10, 15, and 20 SNs. In the first column, UAV's Battery Consumption represents the normalized UAV's consumed battery to finish the reconfiguration scenario. The value 0.39 (Scenario \(S_{1}\)-5 SNs) represents UAV's battery consumption to finish it, so only one UAV was necessary to do it. The value 1.80 (Scenario \(S_{1}\)-20 SNs) represents that 100% of the battery of the UAV was consumed, then it returned to the base station, and a new UAV was used to complete the reconfiguration, consuming 80% of its battery to finish it, totaling two UAVs. The Decisions column represents the number of decisions taken by the UAVs considering the Prohibition List, in order to change its own route. The Flight Distance column represents how many kilometers were taken by UAVs flight to reconfigure all SNs, and, finally, the Flight Time column represents the total time (in minutes) taken to finish reconfiguration of all SNs. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Global Parameters**} & \multicolumn{4}{c}{**Observations**} \\ \hline \multicolumn{2}{c}{Path Planning Algorithm} & \multicolumn{2}{c}{DDNN} \\ \multicolumn{2}{c}{Number of sensor nodes (SNs)} & \multicolumn{2}{c}{5, 10, 15 and 20} \\ \multicolumn{2}{c}{SN Communication Range} & \multicolumn{2}{c}{100 m} \\ \multicolumn{2}{c}{Simulation Area} & \multicolumn{2}{c}{1200 m \(\times\) 500 m} \\ \multicolumn{2}{c}{Number of Timeslots (TS)} & \multicolumn{2}{c}{TS \(\geq\) 10} \\ \multicolumn{2}{c}{UAV Flight Autonomy} & \multicolumn{2}{c}{600 s} \\ \multicolumn{2}{c}{Reading Time (\(t_{R}\))} & \multicolumn{2}{c}{3 s} \\ \hline \multicolumn{2}{c}{**Variable Parameters**} & \(S_{1}\) & \(S_{2}\) & \(S_{3}\) & \(S_{4}\) & \(S_{5}\) \\ \hline \multicolumn{2}{c}{UAV Average Speed (m/s)} & 25 & 25 & Wait & 25 & 25 \\ \multicolumn{2}{c}{Shared or Dedicated Timeslots} & Dedicated & Shared & Dedicated & Dedicated & Dedicated \\ \multicolumn{2}{c}{SN Active Time (timeslots)} & 2 & 2 & 2 & 1 \\ \multicolumn{2}{c}{Prohibition List} & Active & Active & Active & Inactive & Active \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Specification of Simulation Scenarios. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Method**} & \multicolumn{2}{c}{**Observations**} \\ \hline \multirow{4}{*}{ \begin{tabular}{c} Proposed \\ work \\ \end{tabular}  } & \multicolumn{2}{c}{ \begin{tabular}{c} Pre-programming of each SN timeslot \\ and UAV sends rendezvous messages to ensure \\ SNs timeslots sync. \\ \end{tabular}  } & \multicolumn{2}{c}{ \begin{tabular}{c} We assumed that each SN is in a random timeslot, \\ and the UAV is used as a sink node to collect data and \\ to reconfigure it accordingly its own route; \\ Our target is the dynamics of reconfiguration process \\ (decisions, restriction list and flight length/time) \\ \end{tabular}  } \\ \hline \multirow{4}{*}{ \begin{tabular}{c} [PERSON] et al. \\ (BEE-Drones) \\ [20] \\ \end{tabular}  } & \multicolumn{2}{c}{ \begin{tabular}{c} UAV uses a wake-up transmitter and installed a wake \\ up receiver in SNs; \\ Authors displaced wireless charging stations over \\ the area to charge UAVs. \\ \end{tabular}  } & \multicolumn{2}{c}{ \begin{tabular}{c} Results based on number of readings \\ and quality of collected data; \\ Clusters the SNs into different groups to each UAV; \\ After that applies path planning. \\ \end{tabular}  } \\ \hline \multirow{4}{*}{ \begin{tabular}{c} [PERSON] et al. \\ (DroneTank) \\ [21] \\ \end{tabular}  } & \multicolumn{2}{c}{\