Year: 2026
Type: conference-paper
Abstract: Real-time artificial intelligence (AI) systems in cyber-physical and Internet-of-Things (IoT) environments face critical challenges due to fragmentation across sensing, computing, and networking layers. While advances in AIoT, edge intelligence, and distributed learning improve individual components, they lack coordinated system-level control. This paper introduces governance-as-code as a unifying paradigm that embeds executable policies directly into runtime, enabling continuous monitoring, adaptive decision-making, and cross-layer coordination. A unified taxonomy and policy-driven framework are proposed to improve reliability, scalability, and adaptability of real-time AI systems.
DOI: 10.65164/d112f679
Language: English
Topic: IoT and Edge/Fog Computing
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W7168157935