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SS-ITS: secure scalable intelligent transportation systems

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Abstract

This paper introduces a secure and scalable intelligent transportation and human behavior system to accurately discover knowledge from urban traffic data. The data are secured using blockchain learning technology, where the scalability is ensured by a threaded GPU. In addition, different optimizations are provided to efficiently process data on the GPU. A reinforcement deep learning algorithm is also established to merge local knowledge discovered on each site into global knowledge. To demonstrate the applicability of the proposed framework, intensive experiments have been carried out on well-known intelligent transportation and human behavior data. Our results show that our proposed framework outperforms the baseline solutions for the outlier detection use case.

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  1. http://www.geolink.pt/ecmlpkdd2015-challenge/dataset.html.

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Authors and Affiliations

  1. Kristiania University College, Oslo, Norway

    Asma Belhadi

  2. SINTEF Digital, Mathematics and Cybernetics Department, Oslo, Norway

    Youcef Djenouri

  3. Brandon University, Brandon, Canada

    Gautam Srivastava

  4. China Medical University, Taichung, Taiwan

    Gautam Srivastava

  5. Western Norway University of Applied Sciences, Bergen, Norway

    Jerry Chun-Wei Lin

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  1. Asma Belhadi
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  2. Youcef Djenouri
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  4. Jerry Chun-Wei Lin
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Correspondence to Jerry Chun-Wei Lin.

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Belhadi, A., Djenouri, Y., Srivastava, G. et al. SS-ITS: secure scalable intelligent transportation systems. J Supercomput 77, 7253–7269 (2021). https://doi.org/10.1007/s11227-020-03582-7

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