**1. Introduction**

Recent reports from the Federal Aviation Agency state that there will be an increase from 2.75 to 4.47 million small drones operating in the United States by 2021. Since the end of May 2017, more than 772,000 owners have already registered with the Federal Aviation Administration (FAA) [1]. The main reason for the sudden increase in drone ownership is due to consumers purchasing drones for their high mobility and applications in the field of computer vision [2]. Thus, a shift in vision related jobs (building inspection, traffic monitoring and temporary cellular coverage extension) slowly being taken over by drones to perform these tasks. This is because drones can provide the required perspective for jobs such as bird's eye view. Additionally, by using machines to take pictures in hazardous areas, we can minimize the risk to human safety. However, there is still no proposal on the initial deployment of drones which do not include random placement. Furthermore, the cost of privately owning drones can be far too expensive for companies who may only require drones for a single task, in comparison to renting drones [3]. In the case of a rental system, companies and users are not required to purchase a drone allowing the cost to be fairly distributed amongs<sup>t</sup> them. Although the cost to rent can reduce the overall cost when compared to owning a drone. Typically, users must visit a rental center to collect the drone. Otherwise, the drone must fly from the shop to the task, reducing the total energy available for completing tasks. Therefore, to overcome these foreseen issues, an unmanned drone rental service that utilizes drones placed at distributed drone ports is necessary. By providing a public service that allows the rental of distributed autonomous drones waiting at drone ports, this can reduce the total number of drones in the sky and the total cost of utilizing drones to complete tasks requested by the user. Thus, we propose an algorithm that can be applied to a shared drone service to reduce the excessive utilization to increase the efficiency by intelligently placing drone ports in respect to the demand and limitations of drones.

#### *1.1. Motivation for Distributed Drone Ports*

In this paper, we assume that drones will return to the drone port and charge after each cycle since there are already companies creating landing pads, including Skysense (San Francisco, CA, USA) [4]. We imagine that drones must land, take off and be stored at the base of the drone port to charge drones while they are not completing tasks in the air to remove the risk of users being injured by rotating blades and electrical components that charge the drone. Multiple charging pads can be distributed over an area to reduce the average distance drones must travel to recharge. By doing so, we believe the cost of operating a fleet of drones located at distributed drone ports will be less than the cost of operating a central drone port because miniature drones don't require a large drone and can be located from small areas such as building tops to open fields closer to tasks. Finally, the introduction of drone ports will give future research a foundation to justify their initial placement of drones.
