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DC Field | Value | Language |
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dc.contributor.author | Nazib, Rezoan Ahmed | - |
dc.contributor.author | Bouk, Safdar Hussain | - |
dc.contributor.author | Mir, Zeeshan Hameed | - |
dc.contributor.author | Ko, Young Bae | - |
dc.date.issued | 2022-01-01 | - |
dc.identifier.issn | 1976-7684 | - |
dc.identifier.uri | https://aurora.ajou.ac.kr/handle/2018.oak/36809 | - |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85125638954&origin=inward | - |
dc.description.abstract | Using the Unmanned aerial vehicle (UAV) as a mobile sink to collect data in a wireless sensor network is a recent research trend. UAV as a data mule has gained significant attention due to its flexibility in mobility and trajectory designing compared to ground-based mobile sinks. Most of the research that performed UAV-based data collection in sensor networks assumes an optimized UAV trajectory. However, this is not compatible with the realistic scenario because of environmental uncertainties and the physical design of currently available UAVs. Therefore, in this paper, we propose a novel UAV-Aided data collection scheme for the remote region to address the problem of its unplanned trajectory. The proposed framework tries to alleviate the adversarial effect of the unplanned UAV trajectory on the sensor nodes; the extensive quantitative comparison shows that it also performs well in terms of the amount of collected data and energy conservation of the sensor nodes. | - |
dc.description.sponsorship | ACKNOWLEDGMENT This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science | - |
dc.language.iso | eng | - |
dc.publisher | IEEE Computer Society | - |
dc.subject.mesh | Data collection | - |
dc.subject.mesh | Large scale sensor network | - |
dc.subject.mesh | Mobile sinks | - |
dc.subject.mesh | Q-learning | - |
dc.subject.mesh | Recent researches | - |
dc.subject.mesh | Research trends | - |
dc.subject.mesh | Routings | - |
dc.subject.mesh | Trajectory-based | - |
dc.subject.mesh | Unplanned trajectory | - |
dc.subject.mesh | Vehicle trajectories | - |
dc.title | Unplanned UAV Trajectory-based Data Collection in Large-scale Sensor Networks | - |
dc.type | Conference | - |
dc.citation.conferenceDate | 2022.1.12. ~ 2022.1.15. | - |
dc.citation.conferenceName | 36th International Conference on Information Networking, ICOIN 2022 | - |
dc.citation.edition | 36th International Conference on Information Networking, ICOIN 2022 | - |
dc.citation.endPage | 377 | - |
dc.citation.startPage | 372 | - |
dc.citation.title | International Conference on Information Networking | - |
dc.citation.volume | 2022-January | - |
dc.identifier.bibliographicCitation | International Conference on Information Networking, Vol.2022-January, pp.372-377 | - |
dc.identifier.doi | 10.1109/icoin53446.2022.9687151 | - |
dc.identifier.scopusid | 2-s2.0-85125638954 | - |
dc.identifier.url | http://www.icoin.org/ | - |
dc.subject.keyword | data collection | - |
dc.subject.keyword | Q-learning | - |
dc.subject.keyword | routing | - |
dc.subject.keyword | unmanned aerial vehicle | - |
dc.subject.keyword | unplanned trajectory | - |
dc.subject.keyword | Wireless sensor network | - |
dc.type.other | Conference Paper | - |
dc.description.isoa | false | - |
dc.subject.subarea | Computer Networks and Communications | - |
dc.subject.subarea | Information Systems | - |
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