*3.3. Benchmark UAV Deployment Strategies*

As a benchmark solution to the zoning approach, a strategy termed closest, which assigns the closest available UAV to a demand without restrictions in its operation area, can be considered. Note that from our initial experiments, the closest strategy showed poor performance, because distant demands are often assigned to UAVs, hampering the efficient use of UAVs.

To avoid such a distant demand allocation to a UAV, a relaxed version of the closest strategy, termed closest with thresholds, is introduced. Given base locations, we first set a maximum UAV flight range such that all demand nodes can be covered by at least one UAV. For each demand node, any UAVs placed within the maximum flight range are then considered as a candidate server for the demand. Illustrations of the service areas derived by the three different UAV deployment strategies—the zoning approach, the closest, and the closest with thresholds—are given in Figure 1. In the figure, the base and demand node are represented as a red box and a black circle, respectively.

**Figure 1.** Different service area configurations by the different UAV deployment strategies: (**a**) zoning; (**b**) closest; (**c**) closest with thresholds.

#### **4. Experimental Results**

Following the aim of this study (i.e., to demonstrate the performance of the zoning approach), we simulate UAV trajectories for package delivery scenarios and measure how close to each other UAVs are supposed to fly based on the trajectories. We also evaluate the service quality level for customers based on the simulated trajectories to evaluate the service quality degradation by zoning approach.
