Abstract
The development of new concepts for smart cities and the application of drones in this area requires different architecture for the drones’ stations (nests) and their placement. Drones’ stations are designed to protect drones from hazards and utilize charging mechanisms such as solar cells to recharge them. Increasing the number of drones in smart cities makes it harder to find the optimum station for each drone to go to after performing its mission. In classic ordered technique, each drone returns to its preassigned station, which is shown to be not very efficient. Greedy and Kuhn–Munkres (Hungarian) algorithms are used to match the drone to the best nesting station. Three different scenarios are investigated in this study; (1) drones with the same level of energy, (2) drones with different levels of energy, and (3) drones and stations with different levels of energy. The results show that an energy consumption reduction of 25–80% can be achieved by applying the Kuhn–Munkres and greedy algorithms in drone–nest matching compared to preassigned stations. A graphical user interface is also designed to demonstrate drone–station matching through the Kuhn–Munkres and greedy algorithms.
Highlights
In recent years, the concept of smart cities has been developing rapidly
To minimize the power required for the drones to land in the defined nests in an urban area, the Kuhn–Munkres algorithm, called the Hungarian matching algorithm, is used
(1) drones performing their missions have the same level of energy; (2) drones after performing their tasks have a different level of energy, but they will go back to the stations with the same level of energy; (3) drones have different levels of energy and will go back to the stations with different levels of charging
Summary
The concept of smart cities has been developing rapidly. The main goal of smart cities is improving the quality of the life of the residents [1,2,3,4]. In 2014, Yu et al described a cooperative path planning algorithm for tracking a moving target in urban environments using both drones and autonomous ground vehicles. In 2019, Ghazzai et al developed a generic management framework of drones for intelligent transportation systems applications [30] In this study, they investigated the problem of charging station placement in urban environments to find the best locations for a given number of stations and drones. Besides the research performed on path planning and collision avoidance in smart cities, the safety and crash resilience of drones in these cluttered environments is important. Most previous studies focused on the applications of drones in smart cities, path planning, and urban obstacle avoidance.
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