The dramatic increase in the connectivity demand results in an excessive amount of Internet of Things (IoT) sensors. To meet the management needs of these large-scale networks, such as accurate monitoring and learning capabilities, Digital Twin (DT) is the key enabler. However, current attempts regarding DT implementations remain insufficient due to the perpetual connectivity requirements of IoT networks. Furthermore, the sensor data streaming in IoT networks cause higher processing time than traditional methods. In addition to these, the current intelligent mechanisms cannot perform well due to the spatiotemporal changes in the implemented IoT network scenario. To handle these challenges, we propose a DT-native AI-driven service architecture in support of the concept of IoT networks. Within the proposed DT-native architecture, we implement a TCP-based data flow pipeline and a Reinforcement Learning (RL)-based learner model. We apply the proposed architecture to one of the broad concepts of IoT networks, the Internet of Vehicles (IoV). We measure the efficiency of our proposed architecture and note ~30% processing time-saving thanks to the TCP-based data flow pipeline. Moreover, we test the performance of the learner model by applying several learning rate combinations for actor and critic networks and highlight the most successive model.

翻译：连接需求的急剧增长导致物联网（IoT）传感器数量过剩。为满足大规模网络的管理需求（如精确监控与学习能力），数字孪生（DT）技术成为关键使能者。然而，由于物联网网络对持续性连接的要求，当前数字孪生实现方案的尝试仍显不足。此外，物联网网络中的传感器数据流处理时间高于传统方法。同时，由于所部署物联网网络场景的时空变化，现有智能机制难以有效运行。为应对这些挑战，我们提出一种支持物联网网络概念的数字孪生原生AI驱动服务架构。在该数字孪生原生架构中，我们实现了基于TCP的数据流管道和强化学习（RL）学习模型。我们将所提架构应用于物联网的广泛概念之一——车联网（IoV），并通过测量架构效率发现：得益于TCP数据流管道，处理时间节省约30%。此外，通过为演员网络与评论家网络配置多种学习率组合来测试学习模型性能，并突出展示了最优模型。