Artificial Intelligence of Things (AIoT): Technologies, Applications, and Challenges

Abstract

Artificial Intelligence of Things (AIoT) refers to the deliberate pairing of artificial intelligence with the sensing and connectivity that the Internet of Things (IoT) already provides, so that the data streaming in from distributed devices turns into insight and, ultimately, action [1]. A properly designed AIoT system does not push every decision to one place — some things are decided instantly on the device, some at a nearby gateway, and others only once data from many devices has been pooled centrally. This layered approach already underpins predictive maintenance on factory floors, continuous patient monitoring in hospitals, precision irrigation on farms, adaptive traffic signals in cities, and the everyday automation found in connected homes. None of this comes for free: sensor readings are frequently noisy or even tampered with [6], edge hardware is squeezed for memory, compute, and battery life [8], distributed training runs into communication bottlenecks [4], [5], and questions around security, privacy, and explainability remain largely unresolved [9], [10]. This report walks through the core ideas, the layered architecture, and the key enabling technologies behind AIoT — deep learning, graph neural networks, federated learning, and edge computing — before closing with the technical hurdles that still stand between today's research prototypes and dependable, large-scale AIoT deployment.