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 |
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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