Critical infrastructures(CIs)embody cyber-physical-social systems(CPSSs)where physical entities are integrated with cyber components,shaping service delivery through end-user behavior.The seamless operation of CIs is ...Critical infrastructures(CIs)embody cyber-physical-social systems(CPSSs)where physical entities are integrated with cyber components,shaping service delivery through end-user behavior.The seamless operation of CIs is vital for society,and the CPSS resilience relies on interdependencies with AI-integrated technologies.The complexity of the system,and the interconnections with other infrastructures,along with the socio-technical transition towards digitization raised the necessity of implementing Resilience Engineering.This motivates exploration of the scientific literature on resilience key performance indicators(R-KPIs)which support strategies for ensuring service continuity.Therefore,this article aims to identify R-KPIs for AI-integrated CIs and prioritize the extracted R-KPIs using a hybrid Multi-Criteria Decision-Making(MCDM)approach.The results show the importance of employing R-KPIs that measure risk probability,energy self-sufficiency level of the system under study,and performance indicators including functionality loss,recovery time,and minimum performance level after disturbance as the most effective R-KPIs in the domain of this study.After identifying and prioritizing the R-KPIs,a general framework is proposed to employ these R-KPIs in modeling the resilience of a CPS.Finally,a case study demonstrates the implementation of the framework and KPIs in a real-life scenario.展开更多
文摘Critical infrastructures(CIs)embody cyber-physical-social systems(CPSSs)where physical entities are integrated with cyber components,shaping service delivery through end-user behavior.The seamless operation of CIs is vital for society,and the CPSS resilience relies on interdependencies with AI-integrated technologies.The complexity of the system,and the interconnections with other infrastructures,along with the socio-technical transition towards digitization raised the necessity of implementing Resilience Engineering.This motivates exploration of the scientific literature on resilience key performance indicators(R-KPIs)which support strategies for ensuring service continuity.Therefore,this article aims to identify R-KPIs for AI-integrated CIs and prioritize the extracted R-KPIs using a hybrid Multi-Criteria Decision-Making(MCDM)approach.The results show the importance of employing R-KPIs that measure risk probability,energy self-sufficiency level of the system under study,and performance indicators including functionality loss,recovery time,and minimum performance level after disturbance as the most effective R-KPIs in the domain of this study.After identifying and prioritizing the R-KPIs,a general framework is proposed to employ these R-KPIs in modeling the resilience of a CPS.Finally,a case study demonstrates the implementation of the framework and KPIs in a real-life scenario.
