# Overlap Avoidance of Mobility Models for Multi-UAVs Reconnaissance
## 4 Performance Evaluation
### Coverage Rates  
The coverage rate is defined by the ratio of scanned area in the whole area. Figure 7 shows simulation results of RW [23], RMP [12], EGM [16], and DPR [12] and those with overlap avoidance (OA). While Figure 7 shows the cumulative coverage rate, Figure 8 shows the time to reach the coverage rates of 80% and 90% of the whole area in the simulated mobility models.  
Figure 7: Coverage rate results with/without Overlap Avoidance: (**a**) Random Waypoint; (**b**) Random Markov Process; (**c**) Enhanced Gauss–Markov; and (**d**) Distributed Pheromone Repel.  
As shown in Figure 7, the coverage rate of the overlap-avoidance scheme in each model shows slightly higher than each model without considering overlap avoidance. In Figure 8, we measured 80%- and 90%-coverage reaching time of both the proposed models and the previous models to verify the performance. Among those models, the performance of EGM with overlap avoidance model has improved the most. The 80%- and 90%-coverage reaching times are improved by 11.8% and 19.2%, respectively. That is, as UAVs are decentralized by avoiding overlap, they fly to areas which are not reconnoitered. On the contrary, the performance improvement of the DPR model is the lowest at 10.8% among overlap avoidance models in terms of 90% reaching time. Since the previous model attempts to avoid scanned areas based on local information, there is not much performance improvement although overlap avoidance scheme is included. Figures 7 and 8 show that each random mobility model can be improved by applying the proposed overlap avoidance scheme.  
In addition, we analyzed the effect of overlap avoidance in each mobility model by monitoring UAVs' movements during the simulations. When two or more UAVs meet in the same region, they turn to other directions in the proposed models. The following are further analyses of each model.  
* Random Waypoint model: While the proposed scheme improves the RW model, it still has the same limitation as in the RW model itself. The overlap-avoidance model decides a destination randomly on the guidance line. Thus, the model still tends to reconnoiter the center area.
* Random Markov Process model: The main problem of the RMP model is hovering sometimes due to higher turning probability in one direction as in Table 2. However, the proposed overlap-avoidance scheme shows the side-effect of breaking hovering of a UAV by guiding to some direction caused by the overlap avoidance.  
Figure 8: The 80%- and 90%-coverage reaching times with/without Overlap Avoidance: (**a**) Random Waypoint; (**b**) Random Markov Process; (**c**) Enhanced Gauss–Markov; and (**d**) Distributed Pheromone Repel.  
* Enhanced Gauss-Markov model: As we apply the overlap-avoidance scheme in the EGM model, UAVs turn to other directions when two or more UAVs meet in the center. Thus, the coverage rate is improved.
* Distributed Pheromone Repel model: When two UAVs in the DPR model meet towards a same direction, they share almost the same pheromone information which induce both UAVs to fly to similar paths with much overlap. However, the DPR model with overlap avoidance scheme prevents those movements.  
The measurement of coverage rate in Figure 7 does not distinguish how many times an area is scanned or how often an area is visited. As long as a UAV reconnoiters an area once, the area is regarded as covered during the reconnaissance for two hours. Thus, we define the interval coverage rate for a certain period (e.g., 10 min). This metric will be useful to indicate the amount of coverage for a certain time period.  
Figure 9 shows the interval coverage rates of simulated models for 10 min. That is, the rates indicate how much the whole area is scanned for recent 10 min. As shown in Figure 9, the proposed overlap-avoidance schemes improves the interval coverage rate about 5%.