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dc.contributor.authorWei Chong Ngen_US
dc.contributor.authorWei Yang Bryan Limen_US
dc.contributor.authorJer Shyuan Ngen_US
dc.contributor.authorSuttinee Sawadsitangen_US
dc.contributor.authorZehui Xiongen_US
dc.contributor.authorDusit Niyatoen_US
dc.description.abstractToday, modern unmanned aerial vehicles (UAVs) are equipped with increasingly advanced capabilities that can run applications enabled by machine learning techniques, which require computationally intensive operations such as matrix multiplications. Due to computation constraints, the UAVscan offload their computation tasks to edge servers. To mitigate stragglers, coded distributed computing (CDC) based offloading can be adopted. In this paper, we propose an Optimal Task Allocation Scheme (OTAS) based on Stochastic Integer Programming with the objective to minimize energy consumption during computation offloading. The simulation results show that amid uncertainty of task completion, the energy consumption in the UAV network is minimized.en_US
dc.subjectComputer Scienceen_US
dc.subjectDecision Sciencesen_US
dc.titleOptimal Stochastic Coded Computation Offloading in Unmanned Aerial Vehicles Networken_US
dc.typeConference Proceedingen_US
article.title.sourcetitle2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedingsen_US University of Technology and Designen_US of Computer Science and Engineeringen_US Mai Universityen_US Groupen_US JRIen_US
Appears in Collections:CMUL: Journal Articles

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