# IoT-Driven Resilience Monitoring: Case Study of a Cyber-Physical System
## 2 Research Landscape  
Critical infrastructure (CI) encompasses physical assets and systems that provide a necessary service in national, regional, and local scopes. Because of the necessity of the service continuity of CIs, it is extremely important that the service can be delivered under challenging situations like natural disasters or disturbances from cyber-physical attacks [16]. The capacity to withstand or to recover quickly from risks is called resilience. CI should be resilient in all aspects, from governance to operational level [17]. Prior studies unveiled the efficiency of data-driven solutions to enhance the resilience of complex systems [18; 19; 20; 1] and CI [21; 22; 23]. However, achieving a socio-technical transition towards more sustainable and resilient CIs necessitates substantial conceptual changes in the system architecture, coupled with the incorporation of new assets dedicated to collecting real-time data, communication, and computations. The integration of these new components into the system brings more complexity, consequently diminishing the resilience of the overall system. While this transition seems fundamental because of societal challenges of climate change and increasing natural disasters, emerging cyber-physical threats, and the changes in the service end users' behaviour [24], transforming entire socio-technical CI involves a novel resilience engineering approach [25] to guarantee the uninterrupted service continuity of emerging AI-integrated CIs. Nevertheless, at present, the technologies supporting these transitions are either under study or in the developmental stage, typically confined to laboratory-scale implementations. Additionally, the majority of existing CIs continue to operate using traditional monitoring and control systems. Therefore, the body of knowledge in AI-integrated CIs resilience engineering is not fully defined, and there is a gap in standardized models and performance metrics to quantify the resilience of AI-integrated CIs. _Next-Gen Energy Systems Resilience_  
The energy sector is transitioning towards a Next-Gen model, characterized by intelligent energy infrastructures that leverage renewable energy sources [26] and distributed generation [26]. This transformation is further enhanced by emerging technologies such as Digital Twins [19; 27; 28], IoT, data-driven solutions [28], real-time anomaly detection [29], and big data analytics [30; 31; 32], collectively shaping a smart grid that promises a sustainable and efficient future. However, this evolution is not without challenges. Ensuring the resilience of these advanced systems poses a significant challenge, requiring innovative and adaptive solutions. This study is motivated by these challenges and focuses on Next-Gen energy CIs with these defining characteristics. One key concern lies in the growing dependence on renewables [33; 34]. While solar and wind power offer significant environmental benefits, their inherent variability and intermittency pose challenges [35]. Unlike traditional, reliable power plants, renewable sources are not always available, demanding robust energy storage and sophisticated grid management systems to function effectively [36]. Furthermore, extreme weather events can disrupt these already fluctuating sources [37], potentially cascading into power outages and grid instability. Smart grids, another cornerstone of Next-Gen energy systems, introduce a different set of vulnerabilities. These intelligent networks, enabling two-way communication between utilities and consumers, offer real-time monitoring, dynamic pricing, and improved efficiency. However, the increased complexity creates a larger attack surface for cyberthreats [38; 39]. Malicious actors targeting communication networks or control systems could disrupt critical operations, leading to widespread blackouts [40]. The rise of distributed generation, where power is produced closer to consumption points through rooftop solar panels or microgrids, presents a double-edged sword.