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Deep Q-Learning based Resource Management in UAV-assisted Wireless Powered IoT Networks

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dc.contributor.authorLi, Kai
dc.contributor.authorNi, Wei
dc.contributor.authorTovar, Eduardo
dc.contributor.authorJamalipour, Abbas
dc.date.accessioned2020-10-30T10:48:55Z
dc.date.embargo2120
dc.date.issued2020
dc.description.abstractIn Unmanned Aerial Vehicle (UAV)-assisted Wireless Powered Internet of Things (IoT), the UAV is employed to charge the IoT nodes remotely via Wireless Power Transfer (WPT) and collect their data. A key challenge of resource management for WPT and data collection is preventing battery drainage and butter overflow of the ground IoT nodes in the presence of highly dynamic airborne channels. In this paper, we consider the resource management problem in practical scenarios, where the UAV has no a-prior information on battery levels and data queue lengths of the nodes. We formulate the resource management of UAV-assisted WPT and data collection as Markov Decision Process (MDP), where the states consist of battery levels and data queue lengths of the IoT nodes, channel qualities, and positions of the UAV. A deep Q-learning based resource management is proposed to minimize the overall data packet loss of the IoT nodes, by optimally deciding the IoT node for data collection and power transfer, and the associated modulation scheme of the IoT node.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1109/ICC40277.2020.9149282pt_PT
dc.identifier.issn1938-1883
dc.identifier.urihttp://hdl.handle.net/10400.22/16382
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherIEEEpt_PT
dc.relationARNET, ref. POCI-01-0145-FEDER-029074pt_PT
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/9149282pt_PT
dc.subjectUnmanned Aerial Vehiclept_PT
dc.subjectInternet of Thingspt_PT
dc.subjectWireless power transferpt_PT
dc.subjectResource managementpt_PT
dc.subjectDeep Qlearningpt_PT
dc.titleDeep Q-Learning based Resource Management in UAV-assisted Wireless Powered IoT Networkspt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlaceOnlinept_PT
oaire.citation.titleProceedings of the IEEE International Conference on Communications (ICC 2020)pt_PT
person.familyNameLi
person.familyNameTovar
person.givenNameKai
person.givenNameEduardo
person.identifier.ciencia-idEE10-B822-16ED
person.identifier.ciencia-id6017-8881-11E8
person.identifier.orcid0000-0002-0517-2392
person.identifier.orcid0000-0001-8979-3876
person.identifier.scopus-author-id7006312557
rcaap.rightsclosedAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublication21f3fb85-19c2-4c89-afcd-3acb27cedc5e
relation.isAuthorOfPublication80b63d8a-2e6d-484e-af3c-55849d0cb65e
relation.isAuthorOfPublication.latestForDiscovery21f3fb85-19c2-4c89-afcd-3acb27cedc5e

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ISEP – CISTER – Comunicações em eventos científicos