Vehicular big data is anticipated to become the "new oil" of the automotive industry which fuels the development of novel crowdsensing-enabled services. However, the tremendous amount of transmitted vehicular sensor data represents a massive challenge for the cellular network. A promising method for achieving relief which allows to utilize the existing network resources in a more efficient way is the utilization of intelligence on the end-edge-cloud devices. Through machine learning-based identification and exploitation of highly resource efficient data transmission opportunities, the client devices are able to participate in overall network resource optimization process. In this work, we present a novel client-based opportunistic data transmission method for delay-tolerant applications which is based on a hybrid machine learning approach: Supervised learning is applied to forecast the currently achievable data rate which serves as the metric for the reinforcement learning-based data transfer scheduling process. In addition, unsupervised learning is applied to uncover geospatially-dependent uncertainties within the prediction model. In a comprehensive real world evaluation in the public cellular networks of three German Mobile Network Operators (MNOs), we show that the average data rate can be improved by up to 223 % while simultaneously reducing the amount of occupied network resources by up to 89 %. As a side-effect of preferring more robust network conditions for the data transfer, the transmission-related power consumption is reduced by up to 73 %. The price to pay is an increased Age of Information (AoI) of the sensor data.

中文翻译：

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基于客户端的智能，可在未来的6G网络中实现资源高效的车载大数据传输

预计车载大数据将成为汽车行业的“新油”，从而推动新型的基于人群感应的服务的发展。然而，传输的车辆传感器数据的巨大量对蜂窝网络提出了巨大的挑战。允许以更有效的方式利用现有网络资源的减轻负担的有希望的方法是利用端云设备上的智能。通过基于机器学习的识别和高资源效率数据传输机会的开发，客户端设备能够参与整个网络资源优化过程。在这项工作中，我们基于混合机器学习方法，提出了一种针对延迟容忍应用的基于客户端的新型机会数据传输方法：监督学习用于预测当前可达到的数据速率，作为基于强化学习的数据传输的指标调度过程。此外，将无监督学习应用于发现预测模型中与地理空间相关的不确定性。在对三个德国移动网络运营商（MNO）的公共蜂窝网络进行的全面的现实评估中，我们表明，平均数据速率最多可以提高223％，同时占用的网络资源量最多可以减少89％ 。作为首选更健壮的网络条件进行数据传输的副作用，与传输相关的功耗最多降低了73％。付出的代价是增加了传感器数据的信息时代（AoI）。