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Deep Neural Networks for Spatial-Temporal Cyber-Physical Systems: A Survey

Author

Listed:
  • Abubakar Ahmad Musa

    (Department of Computer and Information Sciences, Towson University, Towson, MD 21252, USA)

  • Adamu Hussaini

    (Department of Computer and Information Sciences, Towson University, Towson, MD 21252, USA)

  • Weixian Liao

    (Department of Computer and Information Sciences, Towson University, Towson, MD 21252, USA)

  • Fan Liang

    (Department of Computer Science, Sam Houston State University, Huntsville, TX 77340, USA)

  • Wei Yu

    (Department of Computer and Information Sciences, Towson University, Towson, MD 21252, USA)

Abstract

Cyber-physical systems (CPS) refer to systems that integrate communication, control, and computational elements into physical processes to facilitate the control of physical systems and effective monitoring. The systems are designed to interact with the physical world, monitor and control the physical processes while in operation, and generate data. Deep Neural Networks (DNN) comprise multiple layers of interconnected neurons that process input data to produce predictions. Spatial-temporal data represents the physical world and its evolution over time and space. The generated spatial-temporal data is used to make decisions and control the behavior of CPS. This paper systematically reviews the applications of DNNs, namely convolutional, recurrent, and graphs, in handling spatial-temporal data in CPS. An extensive literature survey is conducted to determine the areas in which DNNs have successfully captured spatial-temporal data in CPS and the emerging areas that require attention. The research proposes a three-dimensional framework that considers: CPS (transportation, manufacturing, and others), Target (spatial-temporal data processing, anomaly detection, predictive maintenance, resource allocation, real-time decisions, and multi-modal data fusion), and DNN schemes (CNNs, RNNs, and GNNs). Finally, research areas that need further investigation are identified, such as performance and security. Addressing data quality, strict performance assurance, reliability, safety, and security resilience challenges are the areas that are required for further research.

Suggested Citation

  • Abubakar Ahmad Musa & Adamu Hussaini & Weixian Liao & Fan Liang & Wei Yu, 2023. "Deep Neural Networks for Spatial-Temporal Cyber-Physical Systems: A Survey," Future Internet, MDPI, vol. 15(6), pages 1-24, May.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:6:p:199-:d:1160086
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    References listed on IDEAS

    as
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    2. Kim, Tae-Young & Cho, Sung-Bae, 2019. "Predicting residential energy consumption using CNN-LSTM neural networks," Energy, Elsevier, vol. 182(C), pages 72-81.
    3. Matthew Kupilik & Frank Witmer, 2018. "Spatio-temporal violent event prediction using Gaussian process regression," Journal of Computational Social Science, Springer, vol. 1(2), pages 437-451, September.
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