%0 Journal Article
%T Minimum-Cost Drone每Nest Matching through the Kuhn每Munkres Algorithm in Smart Cities: Energy Management and Efficiency Enhancement
%A Amir Mirzaeinia
%A Mostafa Hassanalian
%J Aerospace | An Open Access Journal from MDPI
%D 2019
%R https://doi.org/10.3390/aerospace6110125
%X 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每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每80% can be achieved by applying the Kuhn每Munkres and greedy algorithms in drone每nest matching compared to preassigned stations. A graphical user interface is also designed to demonstrate drone每station matching through the Kuhn每Munkres and greedy algorithms. View Full-Tex
%U https://www.mdpi.com/2226-4310/6/11/125