Betrayed by Apps: Automated, Scalable Detection of Mobile App Malpractices. This project aims to develop a novel framework to detect content and privacy malpractices perpetrated by thousands of mobile apps. It will use innovative models and algorithms to achieve unprecedented levels of automation and scalability, making it possible for the first time to identify compliance violations across the global app ecosystem. Outcomes will include a knowledge base of prevalent app malpractices, detection ....Betrayed by Apps: Automated, Scalable Detection of Mobile App Malpractices. This project aims to develop a novel framework to detect content and privacy malpractices perpetrated by thousands of mobile apps. It will use innovative models and algorithms to achieve unprecedented levels of automation and scalability, making it possible for the first time to identify compliance violations across the global app ecosystem. Outcomes will include a knowledge base of prevalent app malpractices, detection algorithms, and a software framework for scalable app analysis. New evidence and tools will benefit both Australian and global policymakers and regulators in combating malpractices, users in identifying safe mobile apps for themselves, and local and global app market stakeholders in being more diligent about compliance.Read moreRead less
A mmWave Sensor Network for Hand Gesture Monitoring. This project aims to realise a world-first mmWave radar-based sensor network for device-free ubiquitous hand gesture monitoring. By harnessing recent radar technology breakthrough in mmWave, hand gesture may be monitored in a non-privacy intrusive manner. Pilot studies show different handrub gestures can be sensed and recognised by analysing the radio signal variations in the receiver. Given the many social, economic and health advantages of ....A mmWave Sensor Network for Hand Gesture Monitoring. This project aims to realise a world-first mmWave radar-based sensor network for device-free ubiquitous hand gesture monitoring. By harnessing recent radar technology breakthrough in mmWave, hand gesture may be monitored in a non-privacy intrusive manner. Pilot studies show different handrub gestures can be sensed and recognised by analysing the radio signal variations in the receiver. Given the many social, economic and health advantages of low-cost and non-privacy intrusive hand gesture sensing --- including enabling interactions and communications with smart environments (e.g., homes and offices) in a natural way --- the proposed research promises multiple benefits while positioning Australia as smart buildings innovator.Read moreRead less
Energy-Efficient Human-Sensing with Photovoltaic Internet-of-Things. This project aims to realise a world-first photovoltaic (PV)-based system for device free ubiquitous human monitoring. By harnessing next generation flexible organic PV cells, Internet-of-Things (IoT) devices may be powered using only indoor lighting. Pilot studies show different activities can, in turn, be sensed and recognised by analysing the variations in the energy harvesting patterns in the PV-powered IoT. Given the many ....Energy-Efficient Human-Sensing with Photovoltaic Internet-of-Things. This project aims to realise a world-first photovoltaic (PV)-based system for device free ubiquitous human monitoring. By harnessing next generation flexible organic PV cells, Internet-of-Things (IoT) devices may be powered using only indoor lighting. Pilot studies show different activities can, in turn, be sensed and recognised by analysing the variations in the energy harvesting patterns in the PV-powered IoT. Given the many social, economic and environmental advantages of cost and energy-efficient sensing – including falls detection for the elderly and power savings in smart building – the proposed research promises multiple benefits while positioning Australia as an IoT innovator.
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
Blockchain-Enabled Federated Learning for Secure and Decentralised Learning. This project aims to develop novel blockchain-enabled federated learning techniques for secure and decentralised learning. It addresses an important and urgent machine learning problem, that is, the data useful for training machine learning models are often held by different owners who are not willing to share their data due to privacy concerns, resulting in isolated data islands. The project will result in a set of inn ....Blockchain-Enabled Federated Learning for Secure and Decentralised Learning. This project aims to develop novel blockchain-enabled federated learning techniques for secure and decentralised learning. It addresses an important and urgent machine learning problem, that is, the data useful for training machine learning models are often held by different owners who are not willing to share their data due to privacy concerns, resulting in isolated data islands. The project will result in a set of innovative algorithms that provide solutions to the key challenges in blockchain-enabled federated learning. The expected outcomes of the project will dramatically advance the frontier of machine learning and blockchain research, and have massive social and economic benefits for Australia and international communities.Read moreRead less
Reliable and Seamless Service Provisioning in Mobile Edge Computing . This project aims to develop enabling technologies to provide reliable and seamless services in mobile edge computing environments. This project will develop advanced algorithms with performance guarantees and efficient mechanisms for such service provisioning. The project expects to lay theoretical foundations and generate new knowledge for the provisioning of reliability-aware and mobility-aware services in mobile edge compu ....Reliable and Seamless Service Provisioning in Mobile Edge Computing . This project aims to develop enabling technologies to provide reliable and seamless services in mobile edge computing environments. This project will develop advanced algorithms with performance guarantees and efficient mechanisms for such service provisioning. The project expects to lay theoretical foundations and generate new knowledge for the provisioning of reliability-aware and mobility-aware services in mobile edge computing. The expected outcome of the project is a set of solutions to the myriad of services relying on mobile edge computing including e-Health, autonomous vehicles, and Internet of Things. This project will develop key fundamental technologies to improve Australia’s standing in the international research community.
Read moreRead less
Edge-Accelerated Deep Learning. Implementing deep learning (DL) applications usually requires a large amount of collected data and powerful computing resources in the cloud. However, this centralised approach has issues of high latency, large bandwidth usage, and possible privacy violation for many practical applications. Without properly addressing these issues, the wider application of DL in practice will seriously be hindered. This project aims to solve several key challenging problems in eff ....Edge-Accelerated Deep Learning. Implementing deep learning (DL) applications usually requires a large amount of collected data and powerful computing resources in the cloud. However, this centralised approach has issues of high latency, large bandwidth usage, and possible privacy violation for many practical applications. Without properly addressing these issues, the wider application of DL in practice will seriously be hindered. This project aims to solve several key challenging problems in effective deployment and efficient execution of DL applications in a distributed edge-computing environment. Several innovative edge-computing methods will be developed for DL training, inference and implementation to achieve high performance with low latency and enhanced privacy.Read moreRead less
Cost-effective App Service Management in Edge Computing Environment. This project aims to deliver a framework and a suite of approaches for cost-effective app service management in the edge computing (EC) environment facilitated by the 5G mobile network. Edge computing offers great promises for rapidly advancing mobile and IoT apps in many active domains in Australia, e.g., self-driving cars, medical services, etc. Using a variety of optimization techniques and game theory, this project attacks ....Cost-effective App Service Management in Edge Computing Environment. This project aims to deliver a framework and a suite of approaches for cost-effective app service management in the edge computing (EC) environment facilitated by the 5G mobile network. Edge computing offers great promises for rapidly advancing mobile and IoT apps in many active domains in Australia, e.g., self-driving cars, medical services, etc. Using a variety of optimization techniques and game theory, this project attacks the new challenges in the deployment, delivery and adaptation of app services in the EC environment. The outcomes of this project will significantly promote new mobile and IoT apps over Australia's 5G mobile network by allowing app vendors to manage their services cost-effectively with ease in the EC environment.Read moreRead less
Adaptive context caching for fast concurrent access in Internet of Things. Context-awareness in Internet of Things (IoT) applications has profound impact on smartness, relevance, adaptability, dependability, performance and flexibility of such applications. This project will address the significant knowledge gap by investigating, proposing and validating a novel adaptive context caching scheme for fast near real-time access in multiple concurrent context queries coming from multiple and diverse ....Adaptive context caching for fast concurrent access in Internet of Things. Context-awareness in Internet of Things (IoT) applications has profound impact on smartness, relevance, adaptability, dependability, performance and flexibility of such applications. This project will address the significant knowledge gap by investigating, proposing and validating a novel adaptive context caching scheme for fast near real-time access in multiple concurrent context queries coming from multiple and diverse IoT applications. The outcome will be a critical component of the IoT context management platform called Context-as-a-Service which is currently under development. The expected benefits will be far ranging and applicable to many domains including intelligent transportation, industrial internet and smart cities..Read moreRead less