About the Project
Modern cyber-physical systems such as the autonomous vehicles, unmanned aerial vehicles, distributed and cloud-controlled robotics, telehealth systems, water distribution network, and smart energy grids are made more sophisticated and fault-tolerant by including new functionality and capabilities into them. For instance, redundant components are used as part of a fault-tolerant strategy to increase their availability. As the systems get larger and more complex, the number of components in a system grows significantly. A growing number of components also lead to many possible complex interactions between them. Moreover, taking advantage of increased functionality, systems can afford to operate in many possible states during operation. While the additional features and sophistication bring significant benefits, this leads to additional complexity, making the system development and safety and reliability analysis of such systems more challenging and less manageable.
This project aims to develop new methodologies for safety and reliability analysis of cyber-physical systems, especially internet of things systems. The solutions will combine advanced software engineering techniques with model-based system analysis methods where system-level safety analysis artefacts such as fault trees and Bayesian networks can be generated from component-level failure logic and the topology of the system. This will allow a rapid probabilistic analysis of systems and enable to produce more meaningful results by refining and synchronising the reliability analysis results with the evolving system designs. Both predictive and diagnostic analyses can be performed using the Bayesian network models. Predictive analysis can determine the failure probability of a system given the failure probability of the components. At the same time, the diagnostic analysis will help to update the prior-belief about the probability of the failure modes based on real-time evidence obtained during system operation.
The project offers the candidate new opportunities to gain invaluable experience in the relevant areas. The successful candidate will have the opportunity to work within a dynamic, effective and multi-disciplinary team, working closely with partners both from academia and industry.
 S. Kabir, I. Sorokos, K. Aslansefat, Y. Papadopoulos, Y. Gheraibia, J. Reich, M. Saimler, and R. Wei, “A Runtime Safety Analysis Concept for Open Adaptive Systems,” In International Symposium on Model-Based Safety and Assessment, pp. 332-346, 2019.
 S. Kabir, “An overview of fault tree analysis and its application in model based dependability analysis,” Expert Systems with Applications, 77, 114-135, 2017.
 S. Sharvia, S. Kabir, M. Walker, and Y. Papadopoulos, “Model-based dependability analysis: State-of-the-art, challenges, and future outlook,” In Software Quality Assurance, pp. 251-278, 2016.
 S. Getir, L. Grunske, A. van Hoorn, T. Kehrer, Y. Noller, and M. Tichy, “Supporting semi-automatic co-evolution of architecture and fault tree models,” Journal of Systems and Software, 142, 115-135, 2018.
 D. Codetta-Raiteri and L. Portinale, “Dynamic Bayesian networks for fault detection, identification, and recovery in autonomous spacecraft,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, 45(1), 13-24, 2015.