RS-RLNC: A Reinforcement Learning-Based Selective Random Linear Network Coding Framework for Tactile Internet
Tactile Internet (TI) has very stringent networking requirements and the transport layer plays a crucial role in meeting these requirements. However, the transport layer has several inherent limitations (e.g., bufferbloat, incast issue, and head of line blocking) due to which the performance of the...
Main Authors: | Shahzad, Rashid Ali, Amir Haider, Hyung Seok Kim |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2023-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10347212/ |
Similar Items
-
Learning-Based Adaptive Sliding-Window RLNC for High Bandwidth-Delay Product Networks
by: Shahzad, et al.
Published: (2023-01-01) -
Reinforcement Learning-Aided Edge Intelligence Framework for Delay-Sensitive Industrial Applications
by: Muhammad Zubair Islam, et al.
Published: (2022-10-01) -
IoTactileSim: A Virtual Testbed for Tactile Industrial Internet of Things Services
by: Muhammad Zubair Islam, et al.
Published: (2021-12-01) -
Scalable Network Coding for Heterogeneous Devices over Embedded Fields
by: Hanqi Tang, et al.
Published: (2022-10-01) -
Current situation of vibration tactile coding
by: Jing-yi DU, et al.
Published: (2021-09-01)