An adaptive task scheduler for a cloud of drones

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dc.contributor.author Alshareef, Hazzaa N.
dc.contributor.author Grigoras, Dan
dc.date.accessioned 2019-08-26T14:21:41Z
dc.date.available 2019-08-26T14:21:41Z
dc.date.issued 2018-11
dc.identifier.citation Alshareef, H. N. and Grigoras, D. (2018) 'An adaptive task scheduler for a cloud of drones'. 2018 4th International Conference on Cloud Computing Technologies and Applications (Cloudtech), Brussels, Belgium, 26-28 Nov., pp. 1-8. doi: 10.1109/CloudTech.2018.8713336 en
dc.identifier.startpage 1 en
dc.identifier.endpage 8 en
dc.identifier.isbn 978-1-7281-1637-2
dc.identifier.uri http://hdl.handle.net/10468/8392
dc.identifier.doi 10.1109/CloudTech.2018.8713336 en
dc.description.abstract Drones are now being widely used in different civilian applications, such as delivering shipments to consumers, as proposed by Amazon, and providing internet access to users, as offered by Facebook and Google. Drones can also contribute in emergencies by helping to find victims in places that are not reachable by rescuers, as well as assisting emergency centers to better manage a reported emergency. However, drones have a short flying time due to limited battery life. Therefore, a reliable strategy that minimizes energy consumption and uses collaborative working is required in order to increase drones' ability to operate for longer periods in emergency situations. This paper presents an adaptive task scheduler that allows tasks to be shared/transferred among the drones in a cloud of drones, in order to extend the operational time, achieve faster task execution and, at the same time, reduce the usage of each drone's resources. The ultimate result is an extension of battery life that leads to longer flying and service time for individual drones. en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Institute of Electrical and Electronics Engineers (IEEE) en
dc.relation.uri https://ieeexplore.ieee.org/document/8713336
dc.rights © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works. en
dc.subject Aerospace computing en
dc.subject Aerospace robotics en
dc.subject Cloud computing en
dc.subject Groupware en
dc.subject Mobile computing en
dc.subject Multi-robot systems en
dc.subject Remotely operated vehicles en
dc.subject Scheduling en
dc.subject Adaptive task scheduler en
dc.subject Mobile cloud en
dc.subject Drone resources en
dc.subject Collaborative working en
dc.subject Energy consumption en
dc.subject Internet access en
dc.subject Cloud of drones en
dc.subject Civilian applications en
dc.subject Drones en
dc.subject Task analysis en
dc.subject Batteries en
dc.subject Streaming media en
dc.subject Protocols en
dc.subject Collaboration en
dc.subject Emergency en
dc.subject Battery power en
dc.subject Task scheduling en
dc.subject Services en
dc.title An adaptive task scheduler for a cloud of drones en
dc.type Conference item en
dc.internal.authorcontactother Dan Grigoras, Computer Science, University College Cork, Cork, Ireland. +353-21-490-3000 Email: d.grigoras@cs.ucc.ie en
dc.internal.availability Full text available en
dc.date.updated 2019-08-26T14:16:16Z
dc.description.version Accepted Version en
dc.internal.rssid 498060090
dc.description.status Peer reviewed en
dc.internal.copyrightchecked No
dc.internal.licenseacceptance Yes en
dc.internal.conferencelocation Brussels, Belgium en
dc.internal.IRISemailaddress d.grigoras@cs.ucc.ie en


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