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IoT as a Service. 9th EAI International Conference, IoTaaS 2023, Nanjing, China, October 27-29, 2023, Proceedings

Research Article

Research on Big Data Information Big Model Processing System of IoT Under Computer Artificial Intelligence Technology

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-70507-6_23,
        author={Ren Qiong and Xi Hu and Junming Chang},
        title={Research on Big Data Information Big Model Processing System of IoT Under Computer Artificial Intelligence Technology},
        proceedings={IoT as a Service. 9th EAI International Conference, IoTaaS 2023, Nanjing, China, October 27-29, 2023, Proceedings},
        proceedings_a={IOTAAS},
        year={2024},
        month={10},
        keywords={Cloud Computing Internet of Things Intensive Scene Big Data Mining},
        doi={10.1007/978-3-031-70507-6_23}
    }
    
  • Ren Qiong
    Xi Hu
    Junming Chang
    Year: 2024
    Research on Big Data Information Big Model Processing System of IoT Under Computer Artificial Intelligence Technology
    IOTAAS
    Springer
    DOI: 10.1007/978-3-031-70507-6_23
Ren Qiong1, Xi Hu1, Junming Chang1,*
  • 1: School of Artificial Intelligence, Jianghan University, Wuhan
*Contact email: cjm72@163.com

Abstract

This paper intends to use cloud computing technology combined with multi-source heterogeneous data to study extensive data analysis and modeling methods for dense environments. This paper aims to improve the mining and detection of massive Internet of Things (IoT) big data in the cloud environment. Firstly, relevant statistical characteristics and correlation rules are extracted from massive IoT big data. Secondly, a multi-source heterogeneous network model based on block is proposed. This paper uses a multi-source isomer model to process the collected data, and it proposes a semantic ontology decomposition method for big data in dense IoT scenarios in a cloud environment, and establishes its association rule knowledge base. Meanwhile, the multi-source heterogeneous information transmission mechanism, and then the dense IoT in the cloud environment is analyzed and mined with big data. Experiments show the proposed algorithm performs better anti-jamming when applied in dense IoT environments. This method has better mining accuracy and less time cost.

Keywords
Cloud Computing Internet of Things Intensive Scene Big Data Mining
Published
2024-10-29
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-70507-6_23
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