Research projects in Information Technology

This project aims to develop resource management techniques including scheduling and scaling algorithms for container-based virtual clusters in cloud data centres powered with renewable energy sources. Algorithms are designed to optimise renewable energy use while meeting the Quality of Service requirements of applications running on the cluster. Specifically, the project aims to:

1. Define an architectural framework and principles for renewable energy-aware management of containers in cloud data centres;

Interest is growing in powering data centres by energy generated from renewable sources to reduce high operational cost and carbon footprint. In 2017, Google achieved a major milestone of purchasing 100% renewable energy to match its data centres annual electricity consumption. However, efficient utilisation of renewable energy is a challenging problem due to the variable and intermittent nature of both workload demand and renewable energy supply.

This project aims to build an automated bird repellent system to detect and identify pest birds and repel them from the farming area by frightening them. An Edge computing platform is embedded in a network of devices to bring application execution close to the data source and overcome challenges with high volumes of data and the need for automated, near real-time system responsiveness. The arrangement of electricity for devices in farming areas is also challenging. Thus, we intend to make our system fully off-grid by the deployment of solar-powered devices.

Insects are vital components of natural and agricultural ecosystems that interact with plants in complex ways. Computer simulations can help us understand these interactions to improve crop production, and to assist us to sustain our natural ecosystems as we change the Earth's climate. This technology is vital to inform our strategies to protect global food supplies and manage our national parks and forests.

This project builds on research in which geo-tagged social network site images are used to determine insect and flowering plant distributions on a continent-wide scale. This work was awarded an "AI for Earth" grant by Microsoft, one of only 6 projects in Australia to receive this recognition.

The most challenging problems of our time are social dilemmas. Thes are situations where individuals are incentivised to free ride on others, but successful group outcomes depend on everyone’s contributions. Examples include, climate change action or compliance with non-pharmaceutical interventions in a large-scale pandemic. In both cases, individuals can rely on others doing their share, but when everyone adopts such a free-riding strategy the public good collapses [1].

The project develops methods to use acoustic data for the identification of animals in the wild and in controlled settings. It is part of a broader effort to build AI-enabled methods to support biodiversity and sustainability research. The initial objective is to use deep learning techniques to perform acoustic species identification in real-time on low-cost sensing devices coupled to cloud-based backends. Ultimately, we are aiming to move to Edge-AI, ie.