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arXiv · 1904.10186

Boosting Vehicle-to-cloud Communication by Machine Learning-enabled Context Prediction

Benjamin Sliwa·Robert Falkenberg·Thomas Liebig·Nico Piatkowski·Christian Wietfeld
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Abstract

The exploitation of vehicles as mobile sensors acts as a catalyst for novel crowdsensing-based applications such as intelligent traffic control and distributed weather forecast. However, the massive increases in Machine-type Communication (MTC) highly stress the capacities of the network infrastructure. With the system-immanent limitation of resources in cellular networks and the resource competition between human cell users and MTC, more resource-efficient channel access methods are required in order to improve the coexistence of the different communicating entities. In this paper, we present a machine learning-enabled transmission scheme for client-side opportunistic data transmission. By considering the measured channel state as well as the predicted future channel behavior, delay-tolerant MTC is performed with respect to the anticipated resource-efficiency. The proposed mechanism is evaluated in comprehensive field evaluations in public Long Term Evolution (LTE) networks, where it is able to increase the mean data rate by 194% while simultaneously reducing the average power consumption by up to 54%.

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Benjamin Sliwa, Robert Falkenberg, Thomas Liebig, Nico Piatkowski, Christian Wietfeld. 2019-07-19. Boosting Vehicle-to-cloud Communication by Machine Learning-enabled Context Prediction. https://doi.org/10.1109/tits.2019.2930109

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