Enabling Remote Human-to-Machine Applications With AI-Enhanced Servers Over Access Networks

Abstract

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.

Keywords

• Human-to-machine applications
• reinforcement learning
• supervised learning
• ultra-low latency communication