The recent research trends for achieving ultra-reliable and low-latency communication networks are largely driven by smart manufacturing and industrial Internet-of-Things applications. Such applications are being realized through Tactile Internet that allows users to control remote things and involve the bidirectional transmission of video, audio, and haptic data. However, the end-to-end propagation latency presents a stubborn bottleneck, which can be alleviated by using various artificial intelligence-based application layer and network layer prediction algorithms, e.g., forecasting and preempting haptic feedback transmission. In this paper, we study the experimental data on traffic characteristics of control signals and haptic feedback samples obtained through virtual reality-based human-to-machine teleoperation. Moreover, we propose the installation of edge-intelligence servers between master and slave devices to implement the preemption of haptic feedback from control signals. Harnessing virtual reality-based teleoperation experiments, we further propose a two-stage artificial intelligence-based module for forecasting haptic feedback samples. The first-stage unit is a supervised binary classifier that detects if haptic sample forecasting is necessary and the second-stage unit is a reinforcement learning unit that ensures haptic feedback samples are forecasted accurately when different types of material are present. Furthermore, by evaluating analytical expressions, we show the feasibility of deploying remote human-to-machine teleoperation over fiber backhaul by using our proposed artificial intelligence-based module, even under heavy traffic intensity.

中文翻译：

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通过访问网络通过AI增强型服务器启用远程人机对应用程序

实现超可靠和低延迟通信网络的最新研究趋势主要由智能制造和工业物联网应用推动。这样的应用程序通过触觉Internet实现，该Internet允许用户控制远程事物并涉及视频，音频和触觉数据的双向传输。但是，端到端的传播延迟呈现出一个顽固的瓶颈，可以通过使用各种基于人工智能的应用程序层和网络层预测算法（例如，预测和抢占触觉反馈传输）来缓解这种瓶颈。在本文中，我们研究了通过基于虚拟现实的人机遥控操作获得的控制信号和触觉反馈样本的流量特性的实验数据。此外，我们建议在主从设备之间安装边缘智能服务器，以实现从控制信号中抢占触觉反馈。利用基于虚拟现实的远程操作实验，我们进一步提出了一个基于人工智能的两阶段模块，用于预测触觉反馈样本。第一级单元是监督二元分类器，用于检测是否需要进行触觉样本预测，而第二级单元是强化学习单元，可确保在存在不同类型的材料时准确地预测触觉反馈样本。此外，通过评估分析表达式，我们证明了即使在交通繁忙的情况下，通过使用我们提出的基于人工智能的模块，也可以通过光纤回程部署远程人机远程操作。