SEAIHCMar 2, 2019

Virtual Representations for Iterative IoT Deployment

arXiv:1903.00718v16 citations
Originality Incremental advance
AI Analysis

This addresses the problem of connecting non-internet-enabled systems in smart factories for incremental IoT deployment, though it is incremental in nature.

The paper tackles the challenge of high installation efforts and hardware adjustments needed for IoT representations in smart factories by proposing Virtual Representations that independently calculate a physical object's condition using existing data through RESTful Web APIs, enabling runtime adjustments without downtime.

A central vision of the Internet of Things is the representation of the physical world in a consistent virtual environment. Especially in the context of smart factories the connection of the different, heterogeneous production modules through a digital shop floor promises faster conversion rates, data-driven maintenance or automated machine configurations for use cases, which have not been known at design time. Nevertheless, these scenarios demand IoT representations of all participating machines and components, which requires high installation efforts and hardware adjustments. We propose an incremental process for bringing the shop floor closer to the IoT vision. Currently the majority of systems, components or parts are not yet connected with the internet and might not even provide the possibility to be technically equipped with sensors. However, those could be essential parts for a realistic digital shop floor representation. We, therefore, propose Virtual Representations, which are capable of independently calculating a physical object's condition by dynamically collecting and interpreting already available data through RESTful Web APIs. The internal logic of such Virtual Representations are further adjustable at runtime, since changes to its respective physical object, its environment or updates to the resource itself should not cause any downtime.

Code Implementations1 repo
Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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