Characterisation of human-to-machine (H2M) traffic
Project Leader: Elaine Wong
Collaborators: Dr Imali Dias (Electrical and Electronic Engineering), Prof Martin Maier (Institut National de la Recherche Scientifique (INRS)), Canada
Primary Contact: Elaine Wong (email@example.com)
Keywords: human-to-machine (H2M) communication networks; low-latency communication networks; Tactile Internet; traffic characterisation
Disciplines: Electrical & Electronic Engineering
Future real-time and remotely-controlled human-to-machine (H2M) applications such as teleoperation and telemedicine 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 through novel AI-assisted resource allocation strategies necessitates a deep understanding of H2M traffic. The main challenge has been the scarcity of real H2M traffic traces, despite the recent interests from both academia and industry. Consequently H2M traffic characteristics/statistics are relatively unknown. This project aims to establish experiments that emulate H2M applications and to develop analytical models for H2M traffic. The key outcome is a deep understanding of the characteristics of H2M traffic and hence its latency and reliability requirements.
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, converged fibre-wireless networks, human-to-machine communication networks, Internet-of-Things, optical wireless networks, and the Tactile Internet.
Relevant research papers
E. Wong and L. Ruan, “Achieving Low-Latency H2M Communications through Predicting Bandwidth Demand: A Comparative Study of Statistical Prediction and Machine Learning Techniques,” to appear in Proc. of OSA Photonic Networks and Devices, July-August 2019.
L. Ruan, M.P.I. Dias, and E. Wong, "Machine Learning-based Bandwidth Prediction for Low-Latency H2M Applications", to appear in IEEE Internet of Things Journal, 2019. Acceptance date: 25th December 2018.
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.
Please contact Prof Wong to access these papers.
Further information: https://www.findanexpert.unimelb.edu.au/display/person3967