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

arXiv: https://arxiv.org/abs/2212.03809 · HTML: https://mlsanity.com/paper/2212.03809/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 7 Dec 2022.

## Builds on (most cited first, 3 of 3)

- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Closing the Loop: A High-Performance Connectivity Solution for Realizing Wireless Closed-Loop Control in Industrial IoT Applications](https://mlsanity.com/paper/2012.11504/) (2020)
- [Evaluating the Performance of Over-the-Air Time Synchronization for 5G and TSN Integration](https://mlsanity.com/paper/2104.13873/) (2021)

## Abstract (arXiv)

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.