# An Efficient Mobility Model for Improving Transmissions in Multi-UAVs Enabled WSNs
## 5 Performance Evaluations
The system performance degrades, as with increasing latency, the packet drop also increases. The proposed model features an overall latency of 9%. The random way-point,  
Figure 5: Throughput comparison among the Proposed Approach, Random Waypoint, Random Walk, Fixed Manuevers and Gauss Markov. Figure 6: QoS comparison among the Proposed Approach, Random Waypoint, Random Walk, Fixed Manuevers and Gauss Markov based on throughput levels. random walk, Gauss-Markov and fixed models have 12.5%, 12.5%, 15% and 85% latency, respectively. The latency comparisons are presented in Figure 7.  
The minimized average delay is a necessary condition for an efficient model. Facilitation of direct communication between UAV and sensor nodes as well as a reduction in the multi-hopping nature of data transmission effectively brings down the overall delay. The statistically important areas are served directly by the UAV and very few of the remaining scarce areas resort to multi-hopping. The average delay of the proposed model is restricted to 0.625%. The random way-point and random walk models have 11.25% and 35.625% delays, respectively. The Gauss-Markov model with its variable velocity property restricts the average delay to 5%. The fixed models have an important characteristic of following their well-defined path and always staying in connection to the base station, thus effectively matching the proposed model with 0.625% average delays. However, this delay is with respect to their coverage and throughput values. Figure 8 gives the average delays for the different approaches. Jitter, which is the measure of the variations in delay, is presented in Figure 9. With the minimized delay, the proposed approach features the jitter value around 2% and the fixed maneuver model matches the jitter values of the proposed approach. The random way-point, random walk, and Gauss-Markov models have 9%, 6%, and 5% jitter, respectively. PDR, which measures the number of packets delivered successfully across the network over the course of time, is an important metric for the mobility model evaluation. The proposed approach that features movement from one dense to another dense region and also collecting data from scarce regions by means of multi-hopping or direct transmission towards base station has a high PDR value of 99%. The random way-point and random walk models that boast of their capabilities of traversing the whole geographical area slowly also possess high PDR of 91% and 90%, respectively, which is slightly less than the proposed approach. The Gauss-Markov model has a PDR of 62%, while the fixed model due to lack of its coverage has a resultant PDR of 12%. Figure 10 presents the PDR comparisons. The overall data transferred and the end-to-end delivery comparisons are presented in Figures 11 and 12. The proposed approach features 91% average data transferred statistics. The random  
Figure 7: Latency comparison among the Proposed Approach, Random Waypoint, Random Walk, Fixed Manuevers and Gauss Markov. way-point and random walk models are at 74% and 72%, respectively. Gauss-Markov and fixed models deliver data transfer average of 49% and 7%, respectively. The proposed approach features a constant EED delivery timing statistics, which is 0.8%. The random way-point and random walk models result in 12.5% and 24.1%, respectively. Gauss-Markov had an EDD of 3.3% and the fixed model matches the proposed approach as it is following its fixed trajectory and consistent connection with the base station. However, this improved characteristic of the fixed model is compromised by their lesser throughput and coverage values. Figure 8: Delay comparison among the Proposed Approach, Random Waypoint, Random Walk, Fixed Manuevers and Gauss Markov. Figure 9: Jitter comparison among the Proposed Approach, Random Waypoint, Random Walk, Fixed Manuevers and Gauss Markov. has a high coverage, less end-to-end delivery times and delay. The packet drop ratio of the proposed approach is 0.8%.