edge computing

Ethics-driven governance of AI-enabled global data center infrastructure is pivotal for protecting security and privacy interests across borders. A multilayered compliance architecture—including risk assessment methodologies, technical mechanisms, established roles, and a set of policy instruments—supports ethical risk and sovereign compliance across the data center ecosystem. Ethical risk management comprises privacy-preserving data flows, lifecycle security, and AI governance. Because ethical risk spans the global ecosystem, fostering collaboration and commonality builds trust and reduces burden while buttressing legitimacy. Thus, private and public interests align. Sovereign AI governance is an essential enabler of the regionally sovereign world of such infrastructure. AI is reshaping the global digital landscape, including the infrastructure of its cloud and edge components. At the same time, the reliance on such AI-systems and the attendant cross-border exchange of data are raising concerns—notably, for security, privacy, democracy, and human rights. Addressing these ethical issues is a pressing requirement. However, the prevailing compliance frameworks for cross-border data flows and AI systems provide neither the tools nor the appropriate level of granularity for Ethical Risk Management in this context. A distinctive multilayered Compliance Architecture is thus put forward that is adapted specifically to the Ethical Risks associated with AI-enabled Global Data Center Infrastructure, namely, that of Operational Resilience, Legal Certainty, and the protection of Security, Privacy & Human Rights.

Quality is the driving factor of the manufacturing industry. With the advent of Industry 4.0, the integration of advanced sensors, edge computing, artificial intelligence, digital twins, and 3D printing has great potential to achieve resilient manufacturing. The first part of this study addresses real-time quality control with edge computing. A digital imaging system is introduced, equipped with an edge device for real-time defect detection in additive manufacturing. AI promises great potential for achieving zero-defect manufacturing, which was barely possible in the past. Then, as the main structure provider for Industry 4.0, edge computing is described. The computer is used in quality control features of edge computing by real-time cloud monitoring and real-time elasticity for cloud services. In the second part, a predictive maintenance framework is proposed by integrating the digital twin, advanced data processing, and AI algorithms. AI finally determines the degradation status of products with high confidence to maintain the resilience of the product life cycle. Expensive fault events on products can be avoided with the help of AI-based advanced planning of maintenance at an appropriate time. The approach could mitigate potential risks of shortening lifespan, increasing maintenance costs, triggering catastrophic events, or even causing social impact if combined with real-time edge computing and advanced sensors. Such an approach advances the deployment of the manufacturing industry in the era of Industry 4.0.