A novel drone-station matching model in smart cities based on strict preferences

Nath, D, Bandyopadhyay, A, Rana, A, Gaber, TMA ORCID: https://orcid.org/0000-0003-4065-4191 and Hassanien, AE 2022, 'A novel drone-station matching model in smart cities based on strict preferences' , Unmanned Systems , pp. 1-11.

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Abstract

There has been a considerable increase in the use of drones, or unmanned aerial vehicles (UAVs), in recent times, for a wide variety of purposes such as security, surveillance, delivery, search and rescue operations, penetration of inaccessible or unsafe areas, etc. The increasing number of drones working in an area poses a challenge to finding a suitable charging or resting station for each drone after completing its task or when it goes low on its charge. The classical methodology followed by drones is to return to their pre-assigned charging station every time it requires a station. This approach is found to be inefficient as it leads to an unnecessary waste of time as well as power, which could be easily saved if the drone is allotted a nearby charging station that is free. Therefore, we propose a drone-allocation model based on a preference matching algorithm where the drones will be allotted the nearest available station to land if the station is free. The problem is modeled as three entities: Drones, system controllers and charging stations. The matching algorithm was then used to design a Drone-Station Matching model. The simulation results of our proposed model showed that there would be considerably less power consumption and more time saving over the conventional system. This would save its travel time and power and ensure more efficient use of the drone.

Item Type: Article
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: Unmanned Systems
Publisher: World Scientific Pub Co Pte Ltd
ISSN: 2301-3869
SWORD Depositor: Publications Router
Depositing User: Publications Router
Date Deposited: 26 Oct 2022 12:45
Last Modified: 26 Oct 2022 12:46
URI: https://usir.salford.ac.uk/id/eprint/65162

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