Resilience Modelling of Cyber-Physical Systems
The first workstream of the “Resilience Modelling of Cyber-Physical Systems” Module is to develop efficient collaborative learning algorithms for jointly processing sensor data from various devices, and possibly coupling these with engineering models to build robust CPS twins. The outcome will be diagnostic and prognostic indicators to feed decision support with a focus on resilience.
The second workstream focuses on resilience against cyber disruptions and aims to study the impact of data corruption in power system operation, and devise strategies for ensuring resilience and protection through a novel society-in-the-loop approach.
The research objectives of the “Resilience Modelling of Cyber-Physical Systems” Module are:
- Develop efficient collaborative learning algorithms.
- Develop a scheme for Augmented Cyber Physical Twins, exploiting data, engineering models and experts in the loop.
- Develop a dynamic Mobile Sensing Platform (DMSP) demonstrator, where roving sensors are used to deliver diagnostic maps of infrastructure CPS.
- Develop a resilience-centered decision support framework and validate this on prototype demonstrators.
- Analyse the impact of data corruption in power system operation.
- Develop distributed immunity schemes for resilience against data corruption.
Expected outcomes
- Novel collaborative learning framework for online maintenance.
- Innovative condition monitoring and faulty isolation framework to assess the condition of physical latent quantities of equipment.
- Novel strategies to improve grid resilience against various types of data corruption.
- Analysis of how consumer behaviour affects the resilience of power grids.
- An Augmented Cyber Physical twin framework incorporating experts in the loop
- A Dynamic Mobile Sensing paradigm for distributed cognition across CP systems & networks.