Problem
Research questions and friction points this paper is trying to address.
Industrial Internet of Things
Edge-Cloud Continuum
Cyber Threats
AI Vulnerabilities
Smart Industry
Downsides of Smartness Across Edge-Cloud Continuum in Modern Industry
🤖 AI Summary
This study addresses the intertwined security vulnerabilities, interoperability challenges, and network threats arising from the large-scale deployment of intelligent industrial systems across the edge–cloud continuum. For the first time, it jointly examines dual risk dimensions stemming from both the software layer—encompassing traditional and generative AI—and the Industrial Internet of Things (IIoT) edge–cloud infrastructure, uncovering the hidden costs introduced by increasing intelligence. By integrating machine learning, reinforcement learning, and generative AI within an edge–fog–cloud collaborative architecture, the work conducts a cross-layer analysis of security and compatibility, identifying multiple critical risk categories. The findings provide both theoretical foundations and practical guidance for developing secure, resilient, and sustainable intelligent industrial systems.
📝 Abstract
The fast pace of modern AI is rapidly transforming traditional industrial systems into vast, intelligent and potentially unmanned autonomous operational environments driven by AI-based solutions. These solutions leverage various forms of machine learning, reinforcement learning, and generative AI. The introduction of such smart capabilities has pushed the envelope in multiple industrial domains, enabling predictive maintenance, optimized performance, and streamlined workflows. These solutions are often deployed across the Industrial Internet of Things (IIoT) and supported by the Edge-Fog-Cloud computing continuum to enable urgent (i.e., real-time or near real-time) decision-making. Despite the current trend of aggressively adopting these smart industrial solutions to increase profit, quality, and efficiency, large-scale integration and deployment also bring serious hazards that if ignored can undermine the benefits of smart industries. These hazards include unforeseen interoperability side-effects and heightened vulnerability to cyber threats, particularly in environments operating with a plethora of heterogeneous IIoT systems. The goal of this study is to shed light on the potential consequences of industrial smartness, with a particular focus on security implications, including vulnerabilities, side effects, and cyber threats. We distinguish software-level downsides stemming from both traditional AI solutions and generative AI from those originating in the infrastructure layer, namely IIoT and the Edge-Cloud continuum. At each level, we investigate potential vulnerabilities, cyber threats, and unintended side effects. As industries continue to become smarter, understanding and addressing these downsides will be crucial to ensure secure and sustainable development of smart industrial systems.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Industrial Internet of Things
Edge-Cloud Continuum
Generative AI
Cyber Threats
AI Vulnerabilities
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