FEIT Research Project Database

Adapting communication networks for low-latency human-to-machine (H2M) applications

Project Leader: Elaine Wong
Collaborators: Dr Lihua Ruan (Chinese University of Hong Kong) Dr Sourav Mondal (Trinity College Dublin) Dr Imali Dias (Deakin University) Prof Vincent Chan (Massachusetts Institute of Technology (MIT), USA)
Primary Contact: Prof. Elaine Wong (ewon@unimelb.edu.au)
Keywords: human-to-machine communication networks; low-latency communication networks; Tactile Internet
Disciplines: Electrical & Electronic Engineering

Future 6G real-time and remotely-controlled human-to-machine (H2M) applications, such as extended reality and the Internet of Senses, will need to be supported by communication infrastructures that incur very low and stringent end-to-end communication latency of approximately 1 to 10 ms. Addressing this latency challenge necessitates a strategic rethink on how network resource is allocated and managed.

In supporting H2M communications, converged optical fiber and wireless networks have been considered as a promising network infrastructure. In the uplink direction, machines contend for uplink bandwidth. Typically, in order to avoid uplink bandwidth collisions, uplink bandwidth is allocated to each machine by a central office through a process known as dynamic bandwidth allocation. In remotely-controlled H2M applications, adaptive allocation will promote flexibility and real-time response, thus ensuring that the latency constaint is always satisfied. This project therefore focuses on adaptive allocation schemes based on machine learning techniques such as federated learning and transfer learning.

This project is presently recruiting PhD candidate(s) with a background in Electrical and Electronic Engineering and/or Computer Science. Interested candidates with a keen interest in experimental investigation and analytical modelling, are encouraged to contact Prof Wong. The successful candidate(s) will be mentored by a team of leading experts with pioneering contributions in broadband access networks, 6G transport networks, converged fibre-wireless networks, human-to-machine communication networks, Internet-of-Things, optical wireless networks, and the Tactile Internet.

Relevant research papers

  • S. Mondal, L. Ruan, M. Maier, D. Larrabeiti, G. Das and E. Wong, “Enabling Remote Human-to-Machine Applications With AI-Enhanced Servers Over Access Networks,” in IEEE Open Journal of the Communications Society, vol. 1, pp. 889–899, 2020, doi: 10.1109/OJCOMS.2020.3009023.
  • L. Ruan, M.P.I. Dias and E. Wong, “Achieving Low-Latency Human-to-Machine (H2M) Applications: An Understanding of H2M Traffic for AI-facilitated Bandwidth Allocation,” IEEE Internet of Things Journal, doi: 10.1109/JIOT.2020.3007947.
  • L. Ruan, M. P. I. Dias & E. Wong, “Enhancing latency performance through intelligent bandwidth allocation decisions: a survey and comparative study of machine learning techniques,” IEEE Journal of Optical Communications and Networking, vol. 12, no. 4, pp. B20, 2020, doi:10.1364/jocn.379715
  • L. Ruan, M.P.I. Dias, and E. Wong, “Machine Learning-based Bandwidth Prediction for Low-Latency H2M Applications,” IEEE Internet of Things Journal, vol. 6, no. 2, pp. 3743–3752, 2019.
  • E. Wong, M.P.I. Dias, and L. Ruan, “Predictive Resource Allocation for Tactile Internet Capable Passive Optical LANs,” J. Lightw. Technol., vol. 35, no. 13, pp. 2629–2641, July 2017.

Further information: https://blogs.unimelb.edu.au/ewon/