5 Introduction

The rapid growth of the Internet of Things (IoT) continues to drive the generation of massive volumes of diverse data, originating from a wide array of connected devices and systems. In parallel, Artificial Intelligence (AI) and Machine Learning (ML) are increasingly being adopted to derive meaningful insights and enable autonomous decision-making based on such data. The convergence of IoT and AI/ML presents significant opportunities for the development of intelligent services across various domains, including smart cities, healthcare, manufacturing, and transportation.

Recognizing this trend, oneM2M initiated Technical Report TR-0068 (Stage 1) [i.2] to analyze the current landscape of AI/ML technologies and explore use cases where AI-enabled IoT services could be realized. TR-0068 identified the architectural and functional gaps within the oneM2M system in supporting AI/ML capabilities and introduced high-level requirements for AI/ML integration.

Building upon the findings of TR-0068, this Technical Report (TR-0071, Stage 2) aims to identify and evaluate concrete technical solutions for enabling AI data and model management within the oneM2M framework. This includes examining mechanisms for the ingestion, processing, distribution, and lifecycle management of AI data and models. The intention is to assess the feasibility, scalability, and interoperability of these solutions within the existing oneM2M architecture.