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Max Schettler, Gurjashan Singh Pannu, Seyhan Ucar, Takamasa Higuchi, Onur Altintas and Falko Dressler, "Learning-based Dwell Time Prediction for Vehicular Micro Clouds," Proceedings of 18th IEEE International Conference on Mobility, Sensing and Networking (MSN 2022), Guangzhou, China, December 2022, pp. 542–549.


Vehicular Micro Clouds (VMCs) are an emerging development in the domain of vehicular networks posed to provide local services to users without the need for external infrastructure. This can significantly improve the user experience, in particular due to the low latencies that such systems can achieve. Due to the distributed nature of such a VMC, effective local coordination is important while using minimal communication resources. To this end, it is important to know, how long vehicles will be participating in, and contributing to a VMC. In this work, we investigate, how previous, heuristic-based approaches can be improved by incorporating local, learning-based techniques. Our analysis indicates a potential improvement of the accuracy of the prediction, and resulted in an improved simulation environment within which the learning-based approach can be deployed.

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Max Schettler
Gurjashan Singh Pannu
Seyhan Ucar
Takamasa Higuchi
Onur Altintas
Falko Dressler

BibTeX reference

    author = {Schettler, Max and Pannu, Gurjashan Singh and Ucar, Seyhan and Higuchi, Takamasa and Altintas, Onur and Dressler, Falko},
    doi = {10.1109/MSN57253.2022.00091},
    title = {{Learning-based Dwell Time Prediction for Vehicular Micro Clouds}},
    pages = {542--549},
    publisher = {IEEE},
    address = {Guangzhou, China},
    booktitle = {18th IEEE International Conference on Mobility, Sensing and Networking (MSN 2022)},
    month = {12},
    year = {2022},

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Last modified: 2024-05-29