Ultra-Low Latency (ULL) Networks: The IEEE TSN and IETF DetNet Standards and Related 5G ULL Research
Not on the map. Why?
Abstract (arXiv)
Many network applications, e.g., industrial control, demand Ultra-Low Latency (ULL). However, traditional packet networks can only reduce the end-to-end latencies to the order of tens of milliseconds. The IEEE 802.1 Time Sensitive Networking (TSN) standard and related research studies have sought to provide link layer support for ULL networking, while the emerging IETF Deterministic Networking (DetNet) standards seek to provide the complementary network layer ULL support. This article provides an up-to-date comprehensive survey of the IEEE TSN and IETF DetNet standards and the related research studies. The survey of these standards and research studies is organized according to the main categories of flow concept, flow synchronization, flow management, flow control, and flow integrity. ULL networking mechanisms play a critical role in the emerging fifth generation (5G) network access chain from wireless devices via access, backhaul, and core networks. We survey the studies that specifically target the support of ULL in 5G networks, with the main categories of fronthaul, backhaul, and network management. Throughout, we identify the pitfalls and limitations of the existing standards and research studies. This survey can thus serve as a basis for the development of standards enhancements and future ULL research studies that address the identified pitfalls and limitations.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Mar 2018.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 1; 2023: 2; 2024: 2; 2025: 1.
7 papers.
- 2023Industrial Internet of Things Intelligence Empowering Smart Manufacturing: A Literature Reviewcited by 8
- 2021Communication-oriented Model Fine-tuning for Packet-loss Resilient Distributed Inference under Highly Lossy IoT Networkscited by 7
- 2022An Intelligent Deterministic Scheduling Method for Ultra-Low Latency Communication in Edge Enabled Industrial Internet of Thingscited by 1
- 2023Data-Driven Latency Probability Prediction for Wireless Networks: Focusing on Tail Probabilitiescited by 1
- 2024Iterative Forgetting: Online Data Stream Regression Using Database-Inspired Adaptive Granulationcited by 0
- 2024OFDM-Standard Compatible SC-NOFS Waveforms for Low-Latency and Jitter-Tolerance Industrial IoT Communicationscited by 0
- 2025KD-PINN: Knowledge-Distilled PINNs for ultra-low-latency real-time neural PDE solverscited by 0
- 2025KD-PINN: Knowledge-Distilled PINNs for ultra-low-latency real-time neural PDE solvers15 Dec 2025
- 2024OFDM-Standard Compatible SC-NOFS Waveforms for Low-Latency and Jitter-Tolerance Industrial IoT Communications7 Jun 2024
- 2024Iterative Forgetting: Online Data Stream Regression Using Database-Inspired Adaptive Granulation14 Mar 2024
- 2023Industrial Internet of Things Intelligence Empowering Smart Manufacturing: A Literature Review2 Dec 2023
- 2023Data-Driven Latency Probability Prediction for Wireless Networks: Focusing on Tail Probabilities20 Jul 2023
- 2022An Intelligent Deterministic Scheduling Method for Ultra-Low Latency Communication in Edge Enabled Industrial Internet of Things17 Jul 2022
- 2021Communication-oriented Model Fine-tuning for Packet-loss Resilient Distributed Inference under Highly Lossy IoT Networks17 Dec 2021