Integrated Risk-based Multiple-Criteria Surveillance Analytics for Critical Infrastructures Safety

We plan to expand and build up on the study “Multi-Criteria Surveillance AI-based Scheduling Analytics for Undersea Infrastructure Safety”. The research aim of the large TRIF project was to develop a novel AI-based surveillance scheduling prototype for safety and security of the undersea infrastructure combining the information obtained from various sources such as remotely operated underwater vehicles (ROVs), autonomous underwater vehicles (AUVs), unmanned aerial vehicles (UAVs) and surface vessels. The study addressed the vulnerability of undersea infrastructure to deliberate attacks, natural disasters, accidents and anchor droppage incorporating their associated risks.

To address extra line of research to make the problem more realistic, we will address more complex undersea infrastructure such as wind farm and internet cables, in addition to the single gas pipelines considered in the TRIF study. Therefore, currently used AI components should be enhanced to deal with more complex realistic case studies. We will also look into multi-criteria decision making methods including social, environmental and security factors.