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Open AccessArticle

Minimum-Cost Drone–Nest Matching through the Kuhn–Munkres Algorithm in Smart Cities: Energy Management and Efficiency Enhancement

1
Department of Computer Science and Engineering, New Mexico Tech, Socorro, NM 87801, USA
2
Department of Mechanical Engineering, New Mexico Tech, Socorro, NM 87801, USA
*
Author to whom correspondence should be addressed.
Aerospace 2019, 6(11), 125; https://doi.org/10.3390/aerospace6110125
Received: 15 October 2019 / Revised: 10 November 2019 / Accepted: 15 November 2019 / Published: 17 November 2019
(This article belongs to the Special Issue Biomimetics in Aerospace Engineering)
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. View Full-Text
Keywords: smart cities; drones; nest; energy; Kuhn–Munkres algorithm; efficiency smart cities; drones; nest; energy; Kuhn–Munkres algorithm; efficiency
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MDPI and ACS Style

Mirzaeinia, A.; Hassanalian, M. Minimum-Cost Drone–Nest Matching through the Kuhn–Munkres Algorithm in Smart Cities: Energy Management and Efficiency Enhancement. Aerospace 2019, 6, 125.

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