Machine Learning-Based Power Consumption Prediction for Unmanned Aerial Vehicles in Dynamic Environments

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dc.contributor.authorGatscher, Julian
dc.contributor.authorBreitenbach, Johannes
dc.contributor.authorBuettner, Ricardo
dc.date.accessioned2022-12-27T19:24:54Z
dc.date.available2022-12-27T19:24:54Z
dc.date.issued2023-01-03
dc.description.abstractUnmanned aerial vehicles are becoming integrated into a wide range of modern IoT and CPS environments for various industrial, military, and entertainment applications. With growing estimations for this market in the future, the problem of energy consumption and its prediction is becoming increasingly important for optimal battery-saving, as well as the safety of the application and thus protection of surrounding persons near the drone flight. This paper presents a machine learning-based approach for the prediction of the power consumption of unmanned aerial vehicles at certain times of the flight. Instead of predicting the power consumption in prescribed environments with complex, time-consuming measurement techniques, our approach is fast, easy to implement, and predicts real-world power consumption in five classes, with a balanced accuracy of 66.7 percent.
dc.format.extent10
dc.identifier.doi10.24251/HICSS.2023.839
dc.identifier.isbn978-0-9981331-6-4
dc.identifier.urihttps://hdl.handle.net/10125/103473
dc.language.isoeng
dc.relation.ispartofProceedings of the 56th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectSoftware Development for Mobile Devices, the Internet-of-Things, and Cyber-Physical Systems
dc.subjectcps
dc.subjectiot
dc.subjectpower consumption
dc.subjectunmanned aerial vehicles
dc.titleMachine Learning-Based Power Consumption Prediction for Unmanned Aerial Vehicles in Dynamic Environments
dc.type.dcmitext
prism.startingpage6924

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