Edge AI System-of-Systems Reference Architecture Engineering Foundations and Multi-Dimensional Views

Abstract

Edge AI systems are emerging from the convergence of IoT, edge computing, AI, agentic AI, and embodied, physical generative edge AI delivering adaptive, autonomous behaviour under physical, cyber, and operational constraints while remaining trustworthy. This article frames edge AI as a complex system-of-systems in which hardware, software, models, and data continuously co-evolve across heterogeneous “multi-X” environments: multiple systems, modalities, and agents distributed from the edge to the cloud. The article argues that as edge AI technologies are maturing, there is a need for a standardised, application-agnostic reference architecture to provide a shared lexicon and taxonomy, reduce integration errors, and expose opportunities for reusable assets and productive interoperability and standardisation. The paper grounds this need in systems engineering and introduces a quad-optimisation paradigm for balancing competing objectives during design and operation. The article presents a design framework and a multi-dimensional architecture organised into three complementary views: quality properties for trustworthiness and dependability, a layered technology stack within each tier, and a processing continuum that partitions intelligence across edge-to-cloud tiers. Finally, the article discusses value creation, interoperability, and how a 2 common baseline supports the development of complex edge AI systems-of-systems and their verification, validation, testing and benchmarking.