Distributed Intelligence in the Artificial Intelligence of Things: A Review of Artificial Intelligence Workload Placement Across the Device-Edge-Fog-Cloud Continuum

Artificial Intelligence of Things (AIoT) is transforming Internet of Things (IoT) systems from cloud-centric data processing into distributed intelligence across device, edge, fog, and cloud tiers. However, existing reviews often emphasize specific computational layers, learning paradigms, or application domains rather than the cross-domain problem of Artificial Intelligence (AI) workload placement under real deployment constraints. This paper presents a structured integrative review of AI workload placement in AIoT, based on a multi-stage literature search, two-stage screening process, and thematic synthesis of 132 sources. The review does not propose a new physical architecture; instead, it develops a terminology-harmonized and AI-centric perspective for assessing where AI functions should reside according to latency, privacy, bandwidth, power, scalability, resilience, and model complexity. Evidence is synthesized across Industrial Internet of Things (IIoT), smart cities, Internet of Medical Things (IoMT), and smart agriculture. The findings show that placement drivers are domain-dependent: deterministic response and reliability dominate IIoT, interoperability and scale shape smart cities, privacy and human oversight constrain IoMT, and energy scarcity and intermittent connectivity define agriculture. The review concludes that robust AIoT requires hybrid multi-layer architectures combining Tiny Machine Learning (TinyML), edge/fog coordination, cloud-scale optimization, and Federated Learning (FL) where appropriate.