2023 Scholarship Winners Announced

Congratulations to the winners of the 2023 CIN PhD scholarship round. Up to two places were supported by CIN member universities, with a focus on research within the following areas:

  • Emerging terrestrial and non-terrestrial communications technologies for rural and regional areas and communities
  • Disaster-resilient and sustainable energy supplies
  • Novel sensors and sensor networks for environmental monitoring and prediction relating to flooding and bushfire
  • Emerging technologies for data sharing and cyber security

The scholarship will run for a period of three years, with opportunity for extension at the discretion of the relevant university and CIN. This commitment from CIN member universities is reflective of each university’s support of research and innovation in the connectivity space. 

Full awardees and project details are below:

The University of New South Wales

Jinhong Yuan

Delay-Doppler Multi-Carrier Modulation (DDMC)-Based LEO Satellite Communications for Rural and Regional Areas

This project will develop novel delay-Doppler multi-carrier transmission and receiving techniques for low-earth-orbit satellite-based non-terrestrial wireless networks (LEO-NTN). The outcomes from this project are expected to contribute to supplying state-of-the-art connectivity techniques of LEO satellite communications and contribute to connecting Australia’s rural/regional areas and communities.

Salil Kanhere

Network Forensics of Wearable IoT devices using Encrypted Traffic Analytics (ETA)

Wearable Internet of Things (WIoT) devices such as smartwatches are being widely used. Despite the adoption of end-to-end encryption, their communication is not as private as we think. The project aims to develop a unified framework for encrypted traffic analytics for WIoT devices to collect network forensics data at scale. 

The University of Sydney

Yonghui Li

Spectrally efficient wide-area massive multi-access technologies

The project aims to develop a wide-area massive multi-access framework, which can provide cost-effective and spectrally efficient broadband services, accommodating a massive number of users and devices over large geographical areas. This can lead to a significant reduction in the number of base stations required to provide broadband services to a massive number of users in rural and remote areas, resulting in lower deployment costs. This can help reduce the overall cost of broadband services, making them more affordable and accessible to people in rural and remote areas.

Suranga Seneviratne

Enhanced Cybersecurity through Artificial Intelligence Driven Red Teaming and Blue Teaming

As Australia continues to embrace the digital age, the number of internet-connected enterprise networks, software, and emergency response equipment has increased significantly, leaving them vulnerable to cyber-attacks from anywhere in the world. The situation is particularly concerning for small and medium-sized businesses and first responders in rural areas who often lack access to local cybersecurity expertise. To address this growing problem, our project is focused on developing AI-based defensive solutions that can automate routine security scanning and analysis tasks. By doing so, we aim to release a comprehensive set of tools that can be used by SMEs and first responders to improve their security posture efficiently and cost-effectively.

The University of Newcastle

Duy Ngo

New communication methods to enable federated learning for bushfire prediction using LEO satellites

This project aims to enhance Australia’s capability for bushfire prediction in large areas without needing to deploy extensive sensors, by enabling recently launched LEO satellite constellations to perform remote imaging and in-satellite training of prediction models. Expected outcomes include new transmission designs and resource management schemes that enable distributed machine learning over satellite links with intermittent connectivity and limited resources.

Scott Brown

A secure platform for data sharing

Cybersecurity is crucial in our increasingly connected world, but it remains a challenge due to the rising number of attacks. While software tools have advanced, human operators remain vulnerable. To address this, we suggest using scientific methods to enhance the interaction between human operators and cybersecurity tools, ultimately strengthening overall security.

Charles Sturt University

Lihong Zheng

Multi-Source Data Fusion and Deep Learning for 3D Flood Mapping

This project aims to improve 3D flood mapping accuracy by fusing data from multiple sensors using deep learning algorithms. It will estimate water surface and inundation depth for more effective emergency response. This project has the potential to significantly enhance flood mapping accuracy, thus mitigating the impact of flooding events.

Rafiqul Islam

A Lightweight Adaptive Adversarial Attack-Resistant (A3R) IDS

This project aims to create a lightweight, adaptive IDS for efficiently thwarting adversarial attacks by extracting packet-level mutation-resistant attack-dependent features, ensuring high evasiveness and maliciousness of the adversarial packets, and developing better algorithms to combat transferrable adversarial attacks. The outcome of this project will enhance user privacy and data security

University of Wollongong

Qinghua Guo

Grant-Free Multiple Access for LEO Satellite Communication Networks

LEO satellite networks are promising in providing full-coverage communication services to ground users. This project focuses on addressing the challenges of multiple access due to the large round-trip latency and high mobility of LEO satellites. It aims to develop efficient and reliable grant-free non-orthogonal multiple access schemes with Doppler-resilient modulations.

Ashish Agalgaonkar

Probabilistic Weather Forecasting for Improving Resiliency of Electricity Networks

The proposed research aims to develop an adaptive and predictive framework (or tool), which will help improve Australian power grid’s resilience under extreme weather and climatic variations based on the practical scenarios and historical event data. It is envisaged that the tool covering identified regions (determined in consultation with the industry) will be developed on a prototype basis to assess the impacts of climate change on selected electricity network assets thereby improving the network resiliency. The proposed project is unique in terms of demonstrating the adverse impacts of severe weather conditions on electricity infrastructure in an objective way thereby ensuring overall system reliability is improved.

The University of Technology, Sydney

Jahangir Hossain

Data driven design and management of shared energy resources to improve power supply resilience in communication networks

This project aims to develop a data driven approach for sharing and managing energy resources in power communication network base stations to achieve higher resilience through better visibility, coordination, and management of shared energy resources in optimal ways.  The intended outcome of the project is a framework to design, prioritise, share, and optimise energy scheduling with quantified resilience. End-users will benefit from improved reliability and avoid system failures triggered by extreme weather events.

Peiyuan Qin

UAV Aided LEO Communications Systems for Rural and Regional Areas

This project aims to develop unmanned aerial vehicle (UAV) aided low earth orbit (LEO) communications by employing novel low-energy-consumption steerable antennas. UAV aided LEO communications can provide high-speed internet for poorly connected or unconnected communities in rural and remote areas. This project will generate significant scientific breakthroughs in many aspects of antenna research.

Southern Cross University

Golam Sorwar

Developing a Flood Prediction System using a Hybrid Machine Learning and Physical Modelling Approach: A Case study of Northern Rivers in NSW, Australia.

Floods can have catastrophic impacts on people and infrastructure in Australia. Accurate and timely prediction and warning systems are essential to mitigate these impacts. This PhD research project aims to develop a novel flood monitoring system to deal with future floods more efficiently and effectively.