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Power-efficient wireless coverage using minimum number of uavs

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dc.citedby6
dc.contributor.authorSawalmeh A.en_US
dc.contributor.authorOthman N.S.en_US
dc.contributor.authorLiu G.en_US
dc.contributor.authorKhreishah A.en_US
dc.contributor.authorAlenezi A.en_US
dc.contributor.authorAlanazi A.en_US
dc.contributor.authorid57194440590en_US
dc.contributor.authorid56426823300en_US
dc.contributor.authorid56597060900en_US
dc.contributor.authorid24776009900en_US
dc.contributor.authorid57221753256en_US
dc.contributor.authorid57191531115en_US
dc.date.accessioned2023-05-29T09:42:08Z
dc.date.available2023-05-29T09:42:08Z
dc.date.issued2022
dc.descriptionAntennas; Disasters; Genetic algorithms; Iterative methods; K-means clustering; Particle swarm optimization (PSO); 3-D placements; Artificial bee colony; Efficient 3d placement; Genetic algorithm; K-means; Particle swarm optimization; Placement algorithm; Power efficient; Unmanned aerial vehicle; Wireless coverage; Unmanned aerial vehicles (UAV); algorithm; animal; bee; Algorithms; Animals; Bees; Unmanned Aerial Devicesen_US
dc.description.abstractUnmanned aerial vehicles (UAVs) can be deployed as backup aerial base stations due to cellular outage either during or post natural disaster. In this paper, an approach involving multiUAV three-dimensional (3D) deployment with power-efficient planning was proposed with the objective of minimizing the number of UAVs used to provide wireless coverage to all outdoor and indoor users that minimizes the required UAV transmit power and satisfies users� required data rate. More specifically, the proposed algorithm iteratively invoked a clustering algorithm and an efficient UAV 3D placement algorithm, which aimed for maximum wireless coverage using the minimum number of UAVs while minimizing the required UAV transmit power. Two scenarios where users are uniformly and non-uniformly distributed were considered. The proposed algorithm that employed a Particle Swarm Optimization (PSO)-based clustering algorithm resulted in a lower number of UAVs needed to serve all users compared with that when a K-means clustering algorithm was employed. Furthermore, the proposed algorithm that iteratively invoked a PSO-based clustering algorithm and PSO-based efficient UAV 3D placement algorithms reduced the execution time by a factor of ?1/17 and ?1/79, respectively, compared to that when the Genetic Algorithm (GA)-based and Artificial Bees Colony (ABC)-based efficient UAV 3D placement algorithms were employed. For the uniform distribution scenario, it was observed that the proposed algorithm required six UAVs to ensure 100% user coverage, whilst the benchmarker algorithm that utilized Circle Packing Theory (CPT) required five UAVs but at the expense of 67% of coverage density. � 2021 by the authors. Licensee MDPI, Basel, Switzerland.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo223
dc.identifier.doi10.3390/s22010223
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85121781389
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85121781389&doi=10.3390%2fs22010223&partnerID=40&md5=58e7b378700d3139c0cb6935157cdcce
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/27285
dc.identifier.volume22
dc.publisherMDPIen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleSensors
dc.titlePower-efficient wireless coverage using minimum number of uavsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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