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dc.contributor.authorZhang, Liang
dc.contributor.authorCelik, Abdulkadir
dc.contributor.authorDang, Shuping
dc.contributor.authorShihada, Basem
dc.date.accessioned2021-04-26T07:42:10Z
dc.date.available2021-04-26T07:42:10Z
dc.date.issued2021
dc.identifier.citationZhang, L., Celik, A., Dang, S., & Shihada, B. (2021). Energy-Efficient Trajectory Optimization for UAV-Assisted IoT Networks. IEEE Transactions on Mobile Computing, 1–1. doi:10.1109/tmc.2021.3075083
dc.identifier.issn2161-9875
dc.identifier.doi10.1109/TMC.2021.3075083
dc.identifier.urihttp://hdl.handle.net/10754/668940
dc.description.abstractIn this paper, we propose and study an energy-efficient trajectory optimization scheme for unmanned aerial vehicle (UAV) assisted Internet of Things (IoT) networks. In such networks, a single UAV is powered by both solar energy and charging stations (CSs), resulting in sustainable communication services, while avoiding energy outage. In particular, we optimize the trajectory design of UAV by jointly considering the average data rate, the total energy consumption, and the fairness of coverage for the IoT terminals. A dynamic spatial-temporal configuration scheme is operated for terminals working in the discontinuous reception (DRX) mode. The module-free, action-confined on-policy and off-policy reinforcement learning approaches are proposed and jointly applied to solve the formulated optimization problem in this paper. We evaluate the effectiveness of the proposed strategy by comparing it with other dynamic benchmark algorithms. The extensive simulation results provided in this paper reveal that the proposed scheme outperforms the benchmarks in terms of data transmission, energy efficiency and adaptivity of avoiding battery depletion. By deploying the proposed trajectory scheme, the UAV is able to adapt itself according to the temporal and dynamic conditions of communication networks.
dc.publisherIEEE
dc.relation.urlhttps://ieeexplore.ieee.org/document/9411725/
dc.relation.urlhttps://ieeexplore.ieee.org/document/9411725/
dc.relation.urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9411725
dc.rights(c) 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
dc.subjectUnmanned aerial vehicle (UAV)
dc.subjectInternet of Things (IoT)
dc.subjectenergy harvesting
dc.subjectreinforcement learning (RL)
dc.subjecttrajectory optimization
dc.titleEnergy-Efficient Trajectory Optimization for UAV-Assisted IoT Networks
dc.typeArticle
dc.contributor.departmentElectrical Engineering Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.identifier.journalIEEE Transactions on Mobile Computing
dc.eprint.versionPost-print
dc.identifier.pages1-1
kaust.personZhang, Liang
kaust.personCelik, Abdulkadir
kaust.personDang, Shuping
kaust.personShihada, Basem
refterms.dateFOA2021-04-26T07:45:41Z


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