# Algorithms for Delivery of Data by Drones in an Isolated Area Divided into Squares
## 3 Simulation Results and Discussion  
The proposed network of drones can be applied in various scenarios, such as in remote quarantined or isolated areas, following technical damage due to a disaster (e.g., an earthquake), or in non-urbanized areas without electricity access or communication infrastructure. For instance, a remote quarantined zone (e.g., due to the COVID-19 pandemic), in which buildings are separated by a safe distance, is considered. In this scenario, each building may be a house in which patients are isolated, a warehouse storing food or drugs, a laboratory in which medical tests are performed for patients, or a location at which doctors are working (isolated from patients). Data packages (medical images, tests, results, prescriptions from doctors, etc.) must be sent between these buildings. The communication between these buildings can be achieved by drones organized in a square-shaped network. To validate the proposed method, simulations were performed using the drone network maps as a collection of squares. Most of the simulations were performed using the Java-based simulator ONE and its provided facilities, using an ASUS ROG GL752 VW-T4015D laptop with Intel(r) Core(tm) i7-6700 HQ 2.60 GHz processor (Asus, Taipei City, Tai  
Figure 9: The console output of the Wi-Fi board. wan), and 8 GB of RAM. The routing protocols used for the simulation within the ONE simulator were Epidemic, Spray and Wait, PRoPHET, MaxProp, and MaxDelivery. The following main steps were used for implementation of the ONE scenarios. The first step consists of defining the map (Figure 10) in wkt file format, in which the coordinates of all the points on the map, including points that establish the route of each drone, are defined. The initializations of the algorithms that define the mobility of drones consist of establishing the initial positions of drones and the recharging/changing points, associating each drone with a recharger/changing point, establishing the stationary points for data transfer, and defining the route of each drone. The final step is the establishment of the simulation parameters, as shown in Table 4. The time parameters, such as the travel autonomy time, the hovering time for the transfer points, and the parking time at the charging or changing points, were established based on the experimental flight tests of the DJI Mavic 2 Pro drone. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of drones for cruise & 24 \\ Number of fixed transfer points & 63 \\ Number of charging/ changing battery points & 12 \\ Average cruise speed of a drone & 46.55 km/h (12.93 m/s) \\ Flight height of drones & 30 m \\ Operating time of the drone in one day & 11 h \\ Data transmission speed & 2 Mbps \\ Drone buffer space & 2 Gb \\ Message size & 500 kb-1 Mb \\ Message time to live & 10 h \\ Source and destination of messages & any station \\ No. of route simulations & 1000 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Simulation parameters for the square-shaped flight mission. Figure 10: Design of the squares network in the ONE simulator. The proposed time-dependent Dijkstra variant was implemented in Visual C++ 2017 programming language. The application has about 1100 lines of C++ source code. The application written in C++ was executed for each of the two considered situations: squares with battery charging, and squares with battery changing. For each case, the same 1000 route simulations considered in the ONE experiments were executed. The delivery rate and latency metrics were used to measure the performance of all six routing protocols analyzed in this paper. The delivery rate is determined as a ratio between the number of delivered messages and the number of created messages. The latency is the average time needed for a message to reach the destination starting from the source (departure node).