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Wireless Coverage for Mobile Users in Dynamic Environments Using UAV

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dc.citedby15
dc.contributor.authorSawalmeh A.H.en_US
dc.contributor.authorOthman N.S.en_US
dc.contributor.authorShakhatreh H.en_US
dc.contributor.authorKhreishah A.en_US
dc.contributor.authorid57194440590en_US
dc.contributor.authorid56426823300en_US
dc.contributor.authorid57193610106en_US
dc.contributor.authorid24776009900en_US
dc.date.accessioned2023-05-29T07:29:24Z
dc.date.available2023-05-29T07:29:24Z
dc.date.issued2019
dc.descriptionAntennas; Genetic algorithms; Heuristic methods; Mobile telecommunication systems; Particle swarm optimization (PSO); Point groups; Trajectories; Brute force search; Dynamic environments; Genetics algorithms; Group mobility model; Heuristic approach; Random waypoints; Search and rescue operations; Total transmit power; Unmanned aerial vehicles (UAV)en_US
dc.description.abstractIn this paper, the dynamic deployment of a single UAV as an aerial base station in providing wireless coverage for mobile outdoor and indoor users is studied. The problem of finding the efficient UAV trajectory is formulated with the objective to minimize the required UAV transmit power that satisfies the users' minimum data rate. The proposed solution to the problem considers the users' movement in a search and rescue (SAR) operation. More specifically, the outdoor rescue team members are considered to move in a group with the reference point group mobility (RPGM) model. Whilst, the indoor rescue team members are considered to move individually and in a group with random waypoint and RPGM models, respectively. The efficient UAV trajectory is developed using two approaches, namely, heuristic and optimal approaches. The employment of the heuristic approach, namely particle swarm optimization (PSO) and genetics algorithm (GA), to find the efficient UAV trajectory reduced the execution time by a factor of ?eq 1/60 and ?eq 1/9 compared to that when using the optimal approach of brute-force search space algorithm. Furthermore, the use of PSO algorithm reduced the execution time by a factor of ?eq 1/7 compared to that when the GA algorithm is invoked.The performance of the dynamic UAV deployment also outperformed the static UAV deployment in terms of the required transmit power. More specifically, the dynamic UAV deployment required less total transmit power by a factor of about 1/2 compared to the static UAV deployment, in providing wireless coverage for rescue team to perform SAR operation within a rectangular sub-region. � 2013 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo8819951
dc.identifier.doi10.1109/ACCESS.2019.2938272
dc.identifier.epage126390
dc.identifier.scopus2-s2.0-85072578067
dc.identifier.spage126376
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85072578067&doi=10.1109%2fACCESS.2019.2938272&partnerID=40&md5=d5d306c57b7dc2043b94af3f99088ddf
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24955
dc.identifier.volume7
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleIEEE Access
dc.titleWireless Coverage for Mobile Users in Dynamic Environments Using UAVen_US
dc.typeArticleen_US
dspace.entity.typePublication
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