论文标题
为所有人的利益行事：用于资源有效传输车辆传感器数据的上下文匪徒
Acting Selfish for the Good of All: Contextual Bandits for Resource-Efficient Transmission of Vehicular Sensor Data
论文作者
论文摘要
作为一种新型的基于客户的方法，用于资源有效的机会传输延迟耐耐力的车辆传感器数据。 BS -CB采用了一种混合方法，该方法将所有主要的机器学习学科（监督，无监督和强化学习）汇集在一起，以自主安排有关预期资源效率的车辆传感器数据传输。在三个移动网络运营商（MNO）的公共蜂窝网络中的全面现实世界绩效评估中，发现1）1）平均上链链接数据速率提高了125％-195％2）数据速率优化的自私目标优化的自私目标可将占用电池的额外繁殖量减少84％-89％3）降低了53％的平均付费量可以减少53％）。由于机会性中型访问策略而延迟。
as a novel client-based method for resource-efficient opportunistic transmission of delay-tolerant vehicular sensor data. BS-CB applies a hybrid approach which brings together all major machine learning disciplines - supervised, unsupervised, and reinforcement learning - in order to autonomously schedule vehicular sensor data transmissions with respect to the expected resource efficiency. Within a comprehensive real world performance evaluation in the public cellular networks of three Mobile Network Operators (MNOs), it is found that 1) The average uplink data rate is improved by 125%-195% 2) The apparently selfish goal of data rate optimization reduces the amount of occupied cell resources by 84%-89% 3) The average transmission-related power consumption can be reduced by 53%-75% 4) The price to pay is an additional buffering delay due to the opportunistic medium access strategy.
