School of Intelligent Manufacturing and Information, Jiangsu Shipping College, Nantong 226010, China.
Comput Intell Neurosci. 2022 Apr 10;2022:8322029. doi: 10.1155/2022/8322029. eCollection 2022.
The Internet of vehicles (IoV) is an important research area of the intelligent transportation systems using Internet of things theory. The complex event processing technology is a basic issue for processing the data stream in IoV. In recent years, many researchers process the temporal and spatial data flow by complex event processing technology. Spatial Temporal Event Processing (STEP) is a complex event query language focusing on the temporal and spatial data flow in Internet of vehicles. There are four processing models of the event stream processing system based on the complex event query language: finite automata model, matching tree model, directed acyclic graph model, and Petri net model. In addition, the worst-case response time of the event stream processing system is an important indicator of evaluating the performance of the system. Firstly, this paper proposed a core algorithm of the temporal and spatial event stream processing program based on STEP by Petri net model. Secondly, we proposed a novel method to estimate the worst-case response time of the event stream processing system, which is based on stochastic Petri net and queuing theory. Finally, through the simulation experiment based on queuing theory, this paper proves that the data stream processing system based on STEP has good dynamic performance in processing the spatiotemporal data stream in Internet of vehicles.
车联网（IoV）是利用物联网理论的智能交通系统的一个重要研究领域。复杂事件处理技术是处理 IoV 中数据流的基本问题。近年来，许多研究人员通过复杂事件处理技术处理时空数据流。时空事件处理（STEP）是一种专注于车联网中时空数据流的复杂事件查询语言。基于复杂事件查询语言的事件流处理系统有四种处理模型：有限自动机模型、匹配树模型、有向无环图模型和 Petri 网模型。此外，事件流处理系统的最坏情况响应时间是评估系统性能的一个重要指标。首先，本文提出了一种基于 Petri 网模型的 STEP 时空事件流处理程序的核心算法。其次，我们提出了一种基于随机 Petri 网和排队论的估计事件流处理系统最坏情况响应时间的新方法。最后，通过基于排队论的仿真实验，本文证明了基于 STEP 的数据流处理系统在处理车联网中的时空数据流时具有良好的动态性能。
