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
Efficient and fair context-aware resource allocation in networks. This project aims to develop a flexible mathematical framework for internet resource allocation among competing demands by exploiting application context to allocate resources more efficiently. The project will extend an existing framework which allocates resources independently at each time period, by considering benefits over periods of time relevant to users. The expected outcome of this project is a systematic method for desig ....Efficient and fair context-aware resource allocation in networks. This project aims to develop a flexible mathematical framework for internet resource allocation among competing demands by exploiting application context to allocate resources more efficiently. The project will extend an existing framework which allocates resources independently at each time period, by considering benefits over periods of time relevant to users. The expected outcome of this project is a systematic method for designing next-generation congestion-avoidance protocols that anticipate and accommodate different types of demand. This project will provide significant benefits including better provision of internet services and new ways to help combat traffic congestion, bringing benefits to both the environment and society.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.
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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