Intelligent machine-learning methodology for real-time network management framework

Project Member(s): Hussain, F.

Funding or Partner Organisation: Australian Mathematical Sciences Institute (Australian Mathematical Sciences Institute Partnership)
Australian Mathematical Sciences Institute (Australian Mathematical Sciences Institute Partnership)
Insitec Pty Ltd
Insitec Pty Ltd

Start year: 2020

Summary: Defence has a number of networked enabled bearers across the battlespace from high bandwidth cable and satellite networks to low bandwidth line of sight radios. At times an operator has multiple network communications mediums at their disposal. In order to effectively pass information at the right time to the right people by the most efficient method right now requires a lot of human intervention which means the solution is not really efficient. By researching and developing a framework that allows real-time network management information would be passed by the most efficient means improving the performance of the Battle Management System and allowing more effective war fighting.

FOR Codes: Artificial Intelligence and Image Processing not elsewhere classified, Command, Control and Communications, Graphics, augmented reality and games not elsewhere classified, Command, control and communications

Acknowledgement of Country

UTS acknowledges the Gadigal people of the Eora Nation, the Boorooberongal people of the Dharug Nation, the Bidiagal people and the Gamaygal people, upon whose ancestral lands our university stands. We would also like to pay respect to the Elders both past and present, acknowledging them as the traditional custodians of knowledge for these lands.