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.

互联需求的剧增导致物联网（IoT）传感器过量。为了满足大规模网络的管理需求，如准确的监测和学习能力，数字孪生是关键驱动因素。然而，由于物联网网络的连续连接要求，目前对数字孪生的实施尝试仍然不足。为了应对这些挑战，我们提出了一种数字孪生本地 AI 驱动的服务架构，以支持物联网网络概念。在提出的数字孪生本地架构中，我们实现了基于 TCP 的数据流水线和基于强化学习的学习模型。我们将该架构应用于物联网网络的一个广泛概念，即车联网。我们测量了我们提出的架构的效率，并注意到由于基于 TCP 的数据流水线而节约了约 30％的处理时间。此外，我们通过应用多种学习速率组合来测试学习模型的性能，并强调最成功的模型。