Artificial Intelligence of Things-Based Smart System Architecture for Sustainable Industrial Transformation - Keemo Gan; Chung Huang (International Journal of Smart Systems, 2023)
The integration of Artificial Intelligence of Things (AIoT) has become a strategic enabler for sustainable industrial transformation by combining intelligent data processing, connected sensing, autonomous decision-making, and real-time system optimization. This article proposes an AIoT-based smart system architecture designed to support sustainable industrial operations through the integration of Internet of Things devices, edge computing, cloud platforms, artificial intelligence models, and decision-support mechanisms. The proposed architecture emphasizes four main layers: data acquisition, intelligent processing, system integration, and sustainability-oriented decision support. By enabling predictive maintenance, energy optimization, resource efficiency, production monitoring, and adaptive process control, the architecture provides a foundation for industries seeking to improve operational performance while reducing environmental impact. The study also discusses key implementation challenges, including data interoperability, cybersecurity risks, infrastructure readiness, model explainability, and organizational capability. Furthermore, the proposed framework highlights the role of AIoT in supporting Industry 4.0 and Industry 5.0 transitions by balancing automation, human-centered intelligence, and sustainable value creation. The findings suggest that AIoT-based smart systems can serve as a transformative approach for achieving more resilient, efficient, and environmentally responsible industrial ecosystems. This article contributes to the development of sustainable industrial digitalization by offering a conceptual architecture that can be adapted across manufacturing, energy, logistics, and process industries.