Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/2855
Title: Fairness aware multiple drone base station deployment
Authors: Akarsu, Alper
Girici, Tolga
Keywords: Remotely operated vehicles
aircraft communication
particle swarm optimisation
next generation networks
radio networks
fairness aware multiple drone base station deployment
next generation wireless networks
tactical communications
disaster-affected network
fairness-aware multiple DBS deployment algorithm
particle swarm optimisation
PSO
three-dimensional locations
suboptimal algorithm
Issue Date: Mar-2018
Publisher:  Institution of Engineering and Technology
Source: Akarsu, A., and Girici, T. (2017). Fairness aware multiple drone base station deployment. IET Communications, 12(4), 425-431.
Abstract: The recent advances in drone technology significantly improved the effectiveness of applications such as border surveillance, disaster management, seismic surveying, and precision agriculture. The use of drones as base stations to improve communication in the next generation wireless networks is another attractive application. However, the deployment of drone base stations (DBSs) is not an easy task and requires a carefully designed strategy. Fairness is one of the most important metrics of tactical communications or a disaster-affected network and must be considered for the efficient deployment of DBSs. In this study, a fairness-aware multiple DBS deployment algorithm is proposed. As the proposed algorithm uses particle swarm optimisation (PSO) that requires significant processing power, simpler algorithms with faster execution times are also proposed and the results are compared. The simulations are performed to evaluate the performance of the algorithms in two different network scenarios. The simulation results show that the proposed PSO-based method finds the three-dimensional locations of DBSs, achieving the best fairness performance with a minimum number of DBSs for deployment. However, it is shown that the proposed suboptimal algorithm performs very close to the PSO-based solution and requires significantly less processing time.
URI: https://hdl.handle.net/20.500.11851/2855
https://digital-library.theiet.org/content/journals/10.1049/iet-com.2017.0978
ISSN: 1751-8628
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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