SS-ITS: secure scalable intelligent transportation systems
Peer reviewed, Journal article
Accepted version
Permanent lenke
https://hdl.handle.net/11250/2992884Utgivelsesdato
2021Metadata
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Originalversjon
Belhadi, A., Djenouri, Y., Srivastava, G., & Lin, J. C.-W. (2021). SS-ITS: secure scalable intelligent transportation systems. The Journal of Supercomputing, 77(7), 7253-7269. 10.1007/s11227-020-03582-7Sammendrag
This paper introduces a secure and scalable intelligent transportation and human behavior system to accurately discover knowledge from urban traffic data. The data is 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 wellknown intelligent transportation and human behavior data. Our results show that our proposed framework outperforms the baseline solutions for the outlier detection use case.
Beskrivelse
This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s11227-020-03582-7