With the development of intelligent applications (e.g., self-driving, real-time emotion recognition), there are higher requirements for cloud intelligence. However, cloud intelligence depends on the multi-modal data collected by user equipments (UEs). Due to the limited capacity of network bandwidth, offloading all data generated from the UEs to the remote cloud is impractical. Thus, in this article, we consider the challenging issue of achieving a certain level of cloud intelligence while reducing network traffic. In order to solve this problem, we design a traffic control algorithm based on label-less learning on the edge cloud, which is dubbed LLTC. By use of the limited computing and storage resources at the edge cloud, LLTC evaluates the value of data that will be offloaded. Specifically, we first give a statement of the problem and the system architecture. Then we design the LLTC algorithm in detail. Finally, we set up the system testbed. Experimental results show that the proposed LLTC can guarantee the required cloud intelligence while minimizing the amount of data transmission.

中文翻译：

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边缘网络中流量控制的无标签学习

随着智能应用程序（例如，自动驾驶，实时情感识别）的发展，对云智能提出了更高的要求。但是，云智能取决于用户设备（UE）收集的多模式数据。由于网络带宽的容量有限，将从UE生成的所有数据卸载到远程云是不切实际的。因此，在本文中，我们考虑了在降低网络流量的同时实现一定水平的云智能的挑战性问题。为了解决这个问题，我们在边缘云上设计了一种基于无标签学习的流量控制算法，称为LLTC。通过使用边缘云中有限的计算和存储资源，LLTC可以评估将要卸载的数据的价值。具体来说，我们首先给出问题和系统架构的说明。然后，我们详细设计了LLTC算法。最后，我们建立了系统测试平台。实验结果表明，提出的LLTC可以保证所需的云智能，同时最大程度地减少数据传输量。