Relativistic Digital Twin: Bringing the IoT to the Future

TL;DR

This paper introduces the Relativistic Digital Twin framework that automatically creates adaptable, general-purpose digital twins for IoT devices using Web of Things standards, enabling accurate behavior prediction across diverse scenarios.

Contribution

The paper extends the W3C WoT standard with a new vocabulary for behavioral models and demonstrates a framework for generating and tuning digital twins in heterogeneous IoT environments.

Findings

DTs accurately forecast indoor temperature in smart home scenarios

DTs successfully predict drone trajectories in outdoor scenarios

Framework adapts to different IoT use cases with reliable performance

Abstract

Complex IoT ecosystems often require the usage of Digital Twins (DTs) of their physical assets in order to perform predictive analytics and simulate what-if scenarios. DTs are able to replicate IoT devices and adapt over time to their behavioral changes. However, DTs in IoT are typically tailored to a specific use case, without the possibility to seamlessly adapt to different scenarios. Further, the fragmentation of IoT poses additional challenges on how to deploy DTs in heterogeneous scenarios characterized by the usage of multiple data formats and IoT network protocols. In this paper, we propose the Relativistic Digital Twin (RDT) framework, through which we automatically generate general-purpose DTs of IoT entities and tune their behavioral models over time by constantly observing their real counterparts. The framework relies on the object representation via the Web of Things (WoT), to offer a standardized interface to each of the IoT devices as well as to their DTs. To this purpose, we extended the W3C WoT standard in order to encompass the concept of behavioral model and define it in the Thing Description (TD) through a new vocabulary. Finally, we evaluated the RDT framework over two disjoint use cases to assess its correctness and learning performance, i.e., the DT of a simulated smart home scenario with the capability of forecasting the indoor temperature, and the DT of a real-world drone with the capability of forecasting its trajectory in an outdoor scenario. Experiments show that the generated DT can estimate the behavior of its real counterpart after an observation stage, regardless of the considered scenario.