Toward Multi-Service Edge-Intelligence Paradigm: Temporal-Adaptive Prediction for Time-Critical Control over Wireless

TL;DR

This paper proposes a multi-service edge-intelligence framework integrating wireless, edge computing, and machine learning to ensure stable, time-critical control over wireless networks despite imperfections.

Contribution

It introduces the concept of multi-service edge-intelligence and a temporal-adaptive prediction method for dynamic wireless environments, with application to robotic teleoperation.

Findings

Temporal-adaptive prediction improves control stability

Framework effectively handles wireless imperfections

Performance demonstrated in robotic teleoperation scenario

Abstract

Time-critical control applications typically pose stringent connectivity requirements for communication networks. The imperfections associated with the wireless medium such as packet losses, synchronization errors, and varying delays have a detrimental effect on performance of real-time control, often with safety implications. This paper introduces multi-service edge-intelligence as a new paradigm for realizing time-critical control over wireless. It presents the concept of multi-service edge-intelligence which revolves around tight integration of wireless access, edge-computing and machine learning techniques, in order to provide stability guarantees under wireless imperfections. The paper articulates some of the key system design aspects of multi-service edge-intelligence. It also presents a temporal-adaptive prediction technique to cope with dynamically changing wireless environments. It provides performance results in a robotic teleoperation scenario. Finally, it discusses some open research and design challenges for multi-service edge-intelligence.