# Gathering Big Data in Wireless Sensor Networks by Drone +
## 8 Conclusions and Future Works  
This work analyzed the problem of finding the best drone tour plan for big data gathering in WSN. We considered the drone a quad-copter with hovering capability as a mobile sink, flying and hovering over all the monitored areas. However, it has a flying time limited by its battery. We also considered sensor nodes storing a relatively large volume of data to be gathered by the drone. Hence, the drone needs time to receive all data packets from every sensor node. We focused on finding the drone's tour plan with the shortest time to gather data from all sensor nodes. The state-of-the-art methods for this scenario are from [PERSON] and [PERSON] [12]. They proposed two heuristics to define drone tours to reduce the data gathering time. A tour is a sequence of locations, or hovering points, inside the monitored area. Each hovering point has a subset of sensor nodes that will send data to the drone when it is hovering over this point. The drone has to follow the sequence and hover over each hovering point for data gathering. Here, we proposed a new algorithm that receives a tour defined by one of these heuristics and creates a subset of nodes that will send data to the drone during its movement. The proposed algorithm guarantees that the drone will stay inside each sensor node's radio range for a minimum time to receive all data. It also defines nodes' sequence to send data to avoid two or more nodes sending data simultaneously. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Heuristics**} & \multicolumn{5}{c}{**Drone Speed**} \\ \cline{2-7}  & **20** & **40** & **60** & **80** & **100** & **120** \\ \hline
**Incremental** & 25.60 & 27.59 & 31.02 & 32.52 & 32.99 & 35.97 \\
**Decremental** & 25.36 & 30.34 & 36.50 & 40.04 & 35.96 & 34.06 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: \(t\)-Test for Scenario 4. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Heuristics**} & \multicolumn{5}{c}{**Data Stored in Each Sensor Node**} \\ \cline{2-7}  & **20** & **40** & **60** & **80** & **100** & **120** \\ \hline
**Incremental** & 29.82 & 30.15 & 30.29 & 19.83 & 18.37 & 17.63 \\
**Decremental** & 21.29 & 24.07 & 23.99 & 24.80 & 25.75 & 25.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: \(t\)-Test for Scenario 2. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Heuristics**} & \multicolumn{5}{c}{**Data Stored in Each Sensor Node**} \\ \cline{2-7}  & **20** & **40** & **60** & **80** & **100** & **120** \\ \hline
**Incremental** & 31.64 & 28.74 & 32.52 & 34.87 & 40.80 & 44.53 \\
**Decremental** & 23.49 & 34.69 & 40.04 & 34.90 & 34.86 & 34.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: \(t\)-Test for Scenario 3. Our simulated experiments showed that the proposed algorithm reduces up to 30% of the overall data gathering. Since the heuristics mentioned above create a better tour in each loop iteration, we applied the proposed algorithm in each tour. Then, we saved that one with the shortest overall data gathering time. We verified that the proposed algorithm provides better results for longer tours and when the sensor nodes have more data to transmit to a drone. We performed the \(t\)-test to affirm, with 99.9% confidence, that the heuristics' results using the proposed algorithm are statistically better than those obtained from the original heuristics by [PERSON] and [PERSON] [12]. As future work, we intend to consider other sets of PDPs. The possible drone positions, or PDPs, are locations inside the monitored area where the drone can hover. These PDPs form a fixed grid, and the tour is a subset of these PDPs. We intend to vary the original PDPs to find better locations to drone gather data. Furthermore, we intend to develop other strategies to create tours. Conceptualization, Methodology, Software, Validation, Investigation, Formal analysis, Writing-Original Draft (J.d.C.V.R. ); Conceptualization, Methodology, Software, Validation, Investigation, Writing-Review-Editing, Supervision (R.I.d.S.)