The symbiosis of distributed ledger and machine learning as a relevance for autonomy in the internet of things

Abstract

The Internet of Things (IoT) describes the fusion of the physical and digital world which enables assets on the edge to send data to a platform where it gets analyzed. Defined actions are then triggered to influence cross-functional edge activities. Furthermore, on the platform tier functionalities and relations need to be identified and implemented to realize assets operating autonomously and ubiquitously. The exploration of this paper results in the identification of autonomous characteristics and shows functional components to implement autonomous assets on the edge. Distributed Ledger Technology (DLT) and its fusion with Machine Learning (ML) as an area of Artificial Intelligence (AI) provides an integral part to realize the described outline. Thus, the recognition of DLT's and ML's usage in the IoT and the evaluation of the relevance as well as the synergies build the main focus of this paper.