# An Efficient Mobility Model for Improving Transmissions in Multi-UAVs Enabled WSNs
## 5 Performance Evaluations
\begin{table}
\begin{tabular}{l l} \hline \hline
**Simulation Settings** & **Values** \\ \hline Ground Nodes & 100 \\ UAV & 1 \\ Ground Node Type & Static WSN \\ Area & 1200 \(\times\) 1200 m\({}^{2}\) \\ WSN-WSN Communication & IEEE 802.11, Direct Sequence Spread Spectrum (DSSS) Rate 1 Mbps \\ WSN UAV Communication & Low Power Wide Area Network (LPWAN), 2 km Line of Sight Transmission \\ Propagation Loss Model & Fiss Propagation Loss Model \\ Packet Size & 512 bytes \\ Data Rate & 5120 bytes/s \\ Data Burst & 10 s \\ Bit Rate & Constant \\ Protocol & User Datagram Protocol (UDP) \\ Simulation & NS3 \\ Analysis & Matlab \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation Settings. The proposed approach was evaluated against the above-defined metrics in comparison with the 3D Random Way Point, 3D Random Walk, Gauss-Markov Mobility Model and fixed UAV maneuvers. The most critical point over which a mobility model can be evaluated is the coverage. Coverage in terms of collaborative networks is defined as the number of ground nodes served or guided over the course of time. The proposed approach provides a steep coverage of 98%. The random way-point and random walk models are characterized by following the same direction for longer tenures. Both the models provide excellent coverage if the ground nodes are laid across their movement diagonal, which is not always, the case, in real-time scenarios. The random way-point and random walk models provide 91% and 90% coverage, respectively. In the Gauss-Markov model, the next way-point relies heavily on the previous speed and direction. It possesses the tendency to skip densely populated regions as the next way-point is not selected on the basis of density and transmission characteristics of the nodes. The model provides 55% coverage. The fixed maneuver for a proactive model performs well over its fixed coordinates, but the overall geography demands constant survey and analysis to manually fix new coordinates over time. The 10% coverage provided by the model comes from the path fixed previously without considering the ever-changing patterns of the erratic dynamic network. Figure 4 presents the overall coverage comparisons. Throughput is directly proportional to the coverage. The greater the coverage, the more ground nodes gets serviced or receive a chance to forward data. Throughput is also affected by the density of the areas served. The close-packed areas tend to have more ground nodes thus increasing the chance of data transmission. The approach works better as it focuses on identifying the denser areas from the scarce ones and then selecting way-points accordingly instead of random movement or movements dictated by speed and direction. The pre-fixed maneuvers do not suffice as both life and transmission density of the WSN nodes is dynamic, and the technique cannot adapt to the changes. The proposed technique provides throughput levels of 82%. The random-way-point and random walk models achieve 74% and 72% throughput, respectively. The Gauss-Markov model achieves a throughput of 55.7% while the fixed technique significantly underperforms with throughput levels of 7.5%. Figure 5 presents the throughput evaluation of approaches in confederation. To test the Quality of Service provided by the proposed approach, the increases and decreases in throughput levels over the course of simulation are presented in the bar graph. The test results demonstrate consistent throughput levels for the proposed approach. Figure 6  
Figure 4: Geographical coverage comparison among the Proposed Approach, Random Waypoint, Random Walk, Fixed Manuevers and Gauss Markov. presents the QoS comparison based on the throughput levels for the stated approaches. Although random way-point and random walk are evenly matched, they underperform significantly. The latency of the system, which is a measure of a signal's travel time from source to destination, plays an important role in the applicability of a model. The system throughput drastically declines with the increase in latency.