BOLD: Bio-Inspired Optimized Leader Election for Multiple Drones

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dc.authorid0000-0002-6253-7597en_US
dc.contributor.authorGanesan, Rajesh
dc.contributor.authorRaajini, X. Mercilin
dc.contributor.authorNayyar, Anand
dc.contributor.authorSanjeevikumar, Padmanaban
dc.contributor.authorHossain, Eklas
dc.contributor.authorErtaş, Ahmet Hanifi
dc.date.accessioned2021-03-20T20:09:25Z
dc.date.available2021-03-20T20:09:25Z
dc.date.issued2020
dc.departmentBTÜ, Mühendislik ve Doğa Bilimleri Fakültesi, Makine Mühendisliği Bölümüen_US
dc.description.abstractOver the past few years, unmanned aerial vehicles (UAV) or drones have been used for many applications. In certain applications like surveillance and emergency rescue operations, multiple drones work as a network to achieve the target in which any one of the drones will act as the master or coordinator to communicate, monitor, and control other drones. Hence, drones are energy-constrained; there is a need for effective coordination among them in terms of decision making and communication between drones and base stations during these critical situations. This paper focuses on providing an efficient approach for the election of the cluster head dynamically, which heads the other drones in the network. The main objective of the paper is to provide an effective solution to elect the cluster head among multi drones at different periods based on the various physical constraints of drones. The elected cluster head acts as the decision-maker and assigns tasks to other drones. In a case where the cluster head fails, then the next eligible drone is re-elected as the leader. Hence, an optimally distributed solution proposed is called Bio-Inspired Optimized Leader Election for Multiple Drones (BOLD), which is based on two AI-based optimization techniques. The simulation results of BOLD compared with the existing Particle Swarm Optimization-Cluster head election (PSO-C) in terms of network lifetime and energy consumption, and from the results, it has been proven that the lifetime of drones with the BOLD algorithm is 15% higher than the drones with PSO-C algorithm.en_US
dc.identifier.doi10.3390/s20113134en_US
dc.identifier.issn1424-8220
dc.identifier.issue11en_US
dc.identifier.pmid32492971en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttp://doi.org/10.3390/s20113134
dc.identifier.urihttps://hdl.handle.net/20.500.12885/412
dc.identifier.volume20en_US
dc.identifier.wosWOS:000552737900127en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorErtaş, Ahmet Hanifi
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofSensorsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectunmanned aerial vehicleen_US
dc.subjectmultiple UAVen_US
dc.subjectclusteringen_US
dc.subjectleader electionen_US
dc.subjectdronesen_US
dc.subjectparticle swarm optimizationen_US
dc.subjectspider monkey optimizationen_US
dc.subjectnetwork lifetimeen_US
dc.titleBOLD: Bio-Inspired Optimized Leader Election for Multiple Dronesen_US
dc.typeArticleen_US

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