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Machine Learning Based Context-Predictive Car-to-Cloud Communication Using Multi-Layer Connectivity Maps for Upcoming 5G Networks.

, , , , и . VTC Fall, стр. 1-7. IEEE, (2018)

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Bodo Sliwa

Edwin Sliwa

Horst Sliwa

Karen Sliwa-Hähnle

Benjamin Köckemann

Abundance, niche breadth and stress in the centre and at the border of the distribution range: a macroecological study on abundant and rare tree specie. Uni Göttingen, (2008)
 

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Car-to-Cloud Communication Traffic Analysis Based on the Common Vehicle Information Model., , , , и . CoRR, (2018)System-in-the-loop design space exploration for efficient communication in large-scale IoT-based warehouse systems., , , и . SysCon, стр. 1-6. IEEE, (2018)DRaGon: Mining Latent Radio Channel Information from Geographical Data Leveraging Deep Learning., , , , , и . WCNC, стр. 2459-2464. IEEE, (2022)Unmanned Aerial Vehicles in Logistics: Efficiency Gains and Communication Performance of Hybrid Combinations of Ground and Aerial Vehicles., , и . VNC, стр. 1-8. IEEE, (2019)B.A.T.Mobile: Leveraging Mobility Control Knowledge for Efficient Routing in Mobile Robotic Networks., , , и . GLOBECOM Workshops, стр. 1-6. IEEE, (2016)Rushing Full Speed with LTE-Advanced Is Economical - A Power Consumption Analysis., , и . VTC Spring, стр. 1-7. IEEE, (2017)Lightweight joint simulation of vehicular mobility and communication with LIMoSim., , , , , и . VNC, стр. 81-88. IEEE, (2017)Exploiting Map Topology Knowledge for Context-Predictive Multi-Interface Car-to-Cloud Communication., , , и . VTC Fall, стр. 1-6. IEEE, (2018)Resource-Efficient Vehicle-to-Cloud Communications Leveraging Machine Learning.. Technical University of Dortmund, Germany, (2021)Deep Learning-based Signal Strength Prediction Using Geographical Images and Expert Knowledge., , , и . GLOBECOM, стр. 1-6. IEEE, (2020)