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Deep Q-Networks for Aerial Data Collection in Multi-UAV-Assisted Wireless Sensor Networks

2021-07-02Journal article Open access
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dc.contributor.authorEmami, Yousef
dc.contributor.authorWei, Bo
dc.contributor.authorLi, Kai
dc.contributor.authorNi, Wei
dc.contributor.authorTovar, Eduardo
dc.date.accessioned2021-08-30T10:54:52Z
dc.date.available2021-08-30T10:54:52Z
dc.date.issued2021-07-02
dc.description.abstractUnmanned Aerial Vehicles (UAVs) can collaborate to collect and relay data for ground sensors in remote and hostile areas. In multi-UAV-assisted wireless sensor networks (MA-WSN), the UAVs' movements impact on channel condition and can fail data transmission, this situation along with newly arrived data give rise to buffer overflows at the ground sensors. Thus, scheduling data transmission is of utmost importance in MA-WSN to reduce data packet losses resulting from buffer overflows and channel fading. In this paper, we investigate the optimal ground sensor selection at the UAVs to minimize data packet losses . The optimization problem is formulated as a multiagent Markov decision process, where network states consist of battery levels and data buffer lengths of the ground sensor, channel conditions, and waypoints of the UAV along the trajectory. In practice, an MA-WSN contains a large number of network states, while the up-to-date knowledge of the network states and other UAVs' sensor selection decisions is not available at each agent. We propose a Multi-UAV Deep Reinforcement Learning based Scheduling Algorithm (MUAIS) to minimize the data packet loss, where the UAVs learn the underlying patterns of the data and energy arrivals at all the ground sensors. Numerical results show that the proposed MUAIS achieves at least 46\% and 35\% lower packet loss than an optimal solution with single-UAV and an existing non-learning greedy algorithm, respectively.pt_PT
dc.description.sponsorshipThis work was partially supported by National Funds through FCT/MCTES (Portuguese Foundation for Science and Technology), within the CISTER Research Unit (UIDP/UIDB/04234/2020); also by national funds through the FCT, under CMU Portugal partnership, within project CMU/TIC/0022/2019 (CRUAV).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1109/IWCMC51323.2021.9498726pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.22/18260
dc.language.isoengpt_PT
dc.relationUIDP/UIDB/04234/2020pt_PT
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/9498726pt_PT
dc.subjectUnmanned aerial vehiclespt_PT
dc.subjectCommunication schedulingpt_PT
dc.subjectMulti-UAV Deep Reinforcement Learningpt_PT
dc.subjectDeep QNetworkpt_PT
dc.titleDeep Q-Networks for Aerial Data Collection in Multi-UAV-Assisted Wireless Sensor Networkspt_PT
dc.title.alternative210304pt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/CMU%2FTIC%2F0022%2F2019/PT
oaire.citation.title2021 International Wireless Communications and Mobile Computing (IWCMC)pt_PT
oaire.citation.volume210304pt_PT
oaire.fundingStream3599-PPCDT
person.familyNameemami
person.familyNameLi
person.familyNameTovar
person.givenNameyousef
person.givenNameKai
person.givenNameEduardo
person.identifier.ciencia-idEE10-B822-16ED
person.identifier.ciencia-id6017-8881-11E8
person.identifier.orcid0000-0002-8842-2616
person.identifier.orcid0000-0002-0517-2392
person.identifier.orcid0000-0001-8979-3876
person.identifier.scopus-author-id7006312557
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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relation.isAuthorOfPublication21f3fb85-19c2-4c89-afcd-3acb27cedc5e
relation.isAuthorOfPublication80b63d8a-2e6d-484e-af3c-55849d0cb65e
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