The objectives are to explore the advanced complex data analytics of autonomous transportation in smart cities, improve the industrial production efficiency and product quality, and optimize the industrial production processes. Aiming at the deployment problem of software-defined network (SDN) in distributed cloud data center, the study proposes a spectrum-based SDN deployment algorithm. An industrial complex data model is built. In addition, a complex event processing model is implemented based on the emerging stream data flow execution engine and the emerging Flink stream processing framework. The Flink framework not only performs real-time analysis of fault tolerance but also greatly simplifies the data processing process. Tests of multiple sets of industrial data from iron mine, chemical, and metallurgical plants have proved the operational advantages of the complex event model for the analysis of industrial complex data. Combining the advantages of online filtering, this study proposes a Generalized Robust Least Square (GRLS) data coordination algorithm. The results confirm that the GRLS has better robustness in correcting significant errors. The spectrum-based SDN deployment algorithm solves the problem of SDN domain partition and controller deployment. This proves that complex event processing technology can extract more valuable information from massive and complex data, thereby ensuring the stability of autonomous transportation.


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    Title :

    Advanced Complex Data Analysis of Autonomous Transportation for Smart City Industrial Environment


    Additional title:

    Internet of Things: Tech., Communicat., Computing


    Contributors:


    Publication date :

    2021-12-15


    Size :

    24 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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