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Unplanned UAV Trajectory-based Data Collection in Large-scale Sensor Networks
  • Nazib, Rezoan Ahmed ;
  • Bouk, Safdar Hussain ;
  • Mir, Zeeshan Hameed ;
  • Ko, Young Bae
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Publication Year
2022-01-01
Journal
International Conference on Information Networking
Publisher
IEEE Computer Society
Citation
International Conference on Information Networking, Vol.2022-January, pp.372-377
Keyword
data collectionQ-learningroutingunmanned aerial vehicleunplanned trajectoryWireless sensor network
Mesh Keyword
Data collectionLarge scale sensor networkMobile sinksQ-learningRecent researchesResearch trendsRoutingsTrajectory-basedUnplanned trajectoryVehicle trajectories
All Science Classification Codes (ASJC)
Computer Networks and CommunicationsInformation Systems
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.
ISSN
1976-7684
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36809
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85125638954&origin=inward
DOI
https://doi.org/10.1109/icoin53446.2022.9687151
Journal URL
http://www.icoin.org/
Type
Conference
Funding
ACKNOWLEDGMENT This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science
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