AIOTI Report on DLT-IoT-AI Technological Convergence R2

AIOTI Focus Group on DLT and Web3 has prepared a report on DLT-IoT-AI Technological Convergence Release 2.

The full report can be found here.

The integration of disruptive technologies is propelling society into a rapid and unparalleled digital transformation, erasing the boundaries between the physical and digital realms. To effectively navigate the intricacies of this transition, it is essential to possess a thorough understanding of the involved technologies and a comprehensive grasp of how these foundational components can synergize to create innovative platforms and practical applications.

This document is an advancement of the “Report on DLT-IoT Technological Convergence,” published in May 2022, which offers the AIOTI’s insights on the fusion of the Internet of Things (IoT) and Decentralized Ledger Technologies (DLT). It delves into the potentialities at the nexus of these technologies, aiming to delineate the overlapping layers of their respective tech stacks, thereby pinpointing promising zones for amalgamation and corresponding use case scenarios. The current report aims to further explore the potentialities at the intersection of Artificial Intelligence (AI), Distributed Ledger Technologies (DLTs), and the Internet of Things (IoT).

Our analysis commences with the articulation of three high-level technological stacks, serving as a foundational framework to decode the attributes of the constituent elements. This discourse then progresses to pinpointing domains and themes of significance at the technological intersections (DLT-AI and DTL-IoT), utilizing a Convergence Matrix. This matrix is designed to establish a unified platform for discussing open research topics and potential application opportunities.

Subsequently, we introduce the concept of the Convergence Prism, a tool designed to highlight opportunities at the tripartite intersection of DLT, IoT, and AI.

The final phase of this report seeks to bridge the theoretical analysis with tangible applications. This is achieved by selecting the most promising topics of convergence and associating them with extant applications, as identified within the AIOTI DLT Test Beds. This approach not only underscores the practical implications of our findings but also provides a tangible link to real-world implementations.