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.

翻译：复杂的物联网生态系统通常需要为其物理资产使用数字孪生（DT），以执行预测性分析并模拟假设场景。数字孪生能够复制物联网设备并随时间适应其行为变化。然而，物联网中的数字孪生通常针对特定用例定制，无法无缝适应不同场景。此外，物联网的碎片化对如何在以多种数据格式和物联网网络协议为特征的异构场景中部署数字孪生提出了额外挑战。本文提出了相对论数字孪生（RDT）框架，通过该框架，我们自动生成物联网实体的通用型数字孪生，并通过持续观测其真实对应体来调整其行为模型。该框架依赖通过Web of Things（WoT）进行的对象表示，为每个物联网设备及其数字孪生提供标准化接口。为此，我们扩展了W3C WoT标准，以包含行为模型的概念，并通过新词汇在Thing Description（TD）中对其进行定义。最后，我们在两个不相关的用例上评估了RDT框架的正确性与学习性能，即具有室内温度预测能力的模拟智能家居场景的数字孪生，以及具有室外场景轨迹预测能力的真实无人机数字孪生。实验表明，无论考虑何种场景，生成的数字孪生在观测阶段后均可估计其真实对应体的行为。