Discovery Early Career Researcher Award - Grant ID: DE150101092
Funder
Australian Research Council
Funding Amount
$321,000.00
Summary
User-based Radio Spectrum Harvesting System. Demand on wireless traffic is estimated to increase more than one thousand-fold in the next 10 years. As existing networks are quickly becoming overloaded, this project aims to improve radio spectrum utilisation by harvesting temporarily unused spectrum holes to accommodate future traffic. The proposed spectrum harvesting system is cost-effective as it will rely on a software that runs on users' devices to sense and report spectrum holes to mobile ser ....User-based Radio Spectrum Harvesting System. Demand on wireless traffic is estimated to increase more than one thousand-fold in the next 10 years. As existing networks are quickly becoming overloaded, this project aims to improve radio spectrum utilisation by harvesting temporarily unused spectrum holes to accommodate future traffic. The proposed spectrum harvesting system is cost-effective as it will rely on a software that runs on users' devices to sense and report spectrum holes to mobile service providers. An unprecedented communications framework that incentivises both users and service providers to harvest and trade/share radio spectrum holes will be developed. It will reduce the costs of the radio spectrum, allowing cheaper and better mobile data services for the Australian public.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200101439
Funder
Australian Research Council
Funding Amount
$418,998.00
Summary
Towards a Reliable and Explainable Health Monitoring and Caring System. This project aims to unleash the power of deep learning on health monitoring and caring domain through a safe, reliable and explainable way. Its innovations lie on 1) developing a set of robust and explainable deep learning models that are guaranteed to be safe to complex environmental uncertainty; 2) designing an intelligent health monitoring and caring platform, powered by robust deep learning models, to better support the ....Towards a Reliable and Explainable Health Monitoring and Caring System. This project aims to unleash the power of deep learning on health monitoring and caring domain through a safe, reliable and explainable way. Its innovations lie on 1) developing a set of robust and explainable deep learning models that are guaranteed to be safe to complex environmental uncertainty; 2) designing an intelligent health monitoring and caring platform, powered by robust deep learning models, to better support the home-based health monitoring and caring for the elderly. The result will enable end-users to trust the decisions of deep learning models in safety-critical systems and significantly contribute to Australian aging society and national healthcare economy.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE210100263
Funder
Australian Research Council
Funding Amount
$425,775.00
Summary
Adaptive Resource Management for Sustainable Edge Computing Systems. This project aims to develop adaptive resource management solutions in edge computing systems for efficient management of the use of limited computing resources and varying renewable energy resources without compromising the stringent needs of emerging Internet of Things applications. These resources will be jointly managed on the diverse, dispersed, often independently owned and operated edge devices with a set of prediction, ....Adaptive Resource Management for Sustainable Edge Computing Systems. This project aims to develop adaptive resource management solutions in edge computing systems for efficient management of the use of limited computing resources and varying renewable energy resources without compromising the stringent needs of emerging Internet of Things applications. These resources will be jointly managed on the diverse, dispersed, often independently owned and operated edge devices with a set of prediction, scheduling and energy saving techniques. The expected outcome is to realise a sustainable edge computing system to reduce both operational cost and negative environmental impact of the system. This project will elevate Australia to be a dominant player in sustainable computing and lead future development trends.
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Discovery Early Career Researcher Award - Grant ID: DE140101628
Funder
Australian Research Council
Funding Amount
$301,970.00
Summary
Non-Intrusive Resource Sharing for Cloud Data Centre Efficiency. Resource sharing using hardware virtualisation has become increasingly common for cloud data centre efficiency. Such virtualisation allows multiple workloads to share a common set of resources in a single physical machine. In practice, however, these co-located workloads often compete for resources, leading to their resource usage being non-isolable and intrusive. This intrusive resource sharing is a major source of cloud data cent ....Non-Intrusive Resource Sharing for Cloud Data Centre Efficiency. Resource sharing using hardware virtualisation has become increasingly common for cloud data centre efficiency. Such virtualisation allows multiple workloads to share a common set of resources in a single physical machine. In practice, however, these co-located workloads often compete for resources, leading to their resource usage being non-isolable and intrusive. This intrusive resource sharing is a major source of cloud data centre inefficiency. This project will develop non-intrusive resource allocation and scheduling solutions that enable co-located workloads to organically use resources. These solutions exploit the heterogeneity and dynamicity of cloud data centres that are often perceived as the main hurdles of resource management.Read moreRead less