Author : Sahil Arora and Apoorva Tewari

The advancement of AI technology in recent times has brought about significant transformations in people’s lives. AI workloads are moving from dispersed edge systems to centralised cloud infrastructures thanks to edge computing, creating a new paradigm known as edge AI. Critical Infrastructures (CIs) functionality becomes essential for the welfare and growth of society and the functioning of governments. There are many different kinds of disruptions that might affect these interdependent infrastructures, which include electricity, communications, healthcare, and transportation. These centralised architectures of historic design fail to cope with the current latency, scale, and requirements of real-time CIs. A potential strategy to improve CI resilience is the combination of AI with Edge Computing (EC). Distributed intelligence is offered by edge AI, which means implementing AI capabilities at the network edge. This reduces latency and bandwidth utilisation, improves privacy, and allows for real-time reaction. Data is processed closer to the source. This study presents a comprehensive overview of how AI and EC are transforming CI management, documenting their benefits and exploring factors that determine CI resilience. By leveraging Edge AI, CIs can become more adaptive, efficient, and secure, ensuring operational continuity even under disruptive conditions. The key contributions of this paper include promoting Edge-AI as a scalable and efficient solution for improving CI resilience by reducing central server loads, enhancing energy efficiency, and facilitating real-time decision-making.

Keywords: Critical Infrastructure, Edge Computing, Artificial Intelligence, Edge-AI