# A New Mobility Model for Multi-UAVs Reconnaissance Based on Partitioned Zone
## 2 Related Work and Research Motivation
### Research Motivation  
To analyze the reconnaissance performance, we simulated two well-known mobility models, the Random Waypoint model and the Pheromone Repel model. Random mobility models for reconnaissance can be categorized into two methods according to sharing information. Random Waypoint and Pheromone Repel models are representative models in each category. In our simulations, other random models without sharing information show similar performance as the Random Waypoint.  
Figure 1: The examples of movement: (**a**) Random Waypoint model; and (**b**) Markov Process model.  
In the case of models with sharing information, other related work enhancing Pheromone Repel model have different objectives, not just reconnaissance. In terms of reconnaissance, those work perform the same as Pheromone Repel model.  
Although previous mobility models show good performance in terms of coverage rate of reconnaissance, they did not consider the balance of the number of visits in each area. In Figure 2, we count and plot the number of visits in each unit area so that the bright color indicates high rate of revisits.  
As shown in Figure 2a, the bright-color regions are mostly located at the center of the square, which indicates that most of the reconnaissance are focused in the center of the simulation area. That is, although the UAVs in the Random Waypoint model reconnoiter the area by choosing the destination randomly, they tend to move around the central part of the area.  
Similarly, the Pheromone model in Figure 2b also shows high revisit rates in some regions. In the case of the Pheromone Repel model, there are two reasons for high revisiting rates in some regions. The first one is due to the Markov model in the absence of any pheromone information. The Markov model leads a UAV to keep turning left (or right) if the UAV turns left (or right). The second reason occurs when two or more UAVs meet. When two UAVs moving in similar direction meet each other, they share their pheromone information. Thus, they move similar ways since they have the same pheromone information.  
In Figure 3a, we plot the number of unit regions for each visited counts from the results of Figure 2a. As shown in Figure 3a, the Random Waypoint model reconnoiters 337 unit regions (37.4%) at most twice for 2 h, while it guides UAVs to scan 53 unit regions (5.8%) 10 times or more. Similarly, the Pheromone Repel model also shows imbalance in terms of visiting number among unit regions since some regions are visited even 16 or 17 times, as shown in Figure 3b.  
In this paper, we consider this imbalance of visits in each region for designing reconnaissance mobility model. The imbalance of the number of visits affects not only the coverage rate but also the uniform reconnaissance by searching the designated area periodically. Since this imbalance problem becomes worse as the reconnaissance proceeds, we need a new random mobility model to solve it. Therefore, we propose a novel mobility model for the purpose of uniform reconnaissance.  
Figure 2: The distribution of visits (the bright color indicates the high rate of revisits): (**a**) Random Waypoint model; and (**b**) Pheromone Repel model.