Mathematical system model for acoustics based telematic micro services in iot for transportation setting

• Autorzy: Kannan J. R. , Rai A. , Szpytko J. , Pandya J. C.

Identyfikatory

DOI

10.5604/01.3001.0010.3056

• Języki publikacji: EN

Abstrakty

EN

Despite the wide adoption of Internet of things (IoT) with several webs standards and cloud technologies, building of city wide IoT based smart city platform for solving transportation problem remains a daunting task. Owing to the dynamic nature of IoT and components of transportation systems, smart city architecture would require development of a scalable, distributed and evolving architecture on the web. With the advancement in autonomous transportation system there is a need for in adaptive telematic system for communicating with other vehicles, sensor nodes etc. As transport, services have special requirements of which are related to the size and type of information to be exchanged between vehicles (vehicle-to-vehicle communication) and the control centre. . By the time the data makes its way to the cloud for analysis, the opportunity to act on it might be gone. Thus handling such huge streams of data on the fly is a daunting task. In the study we present an interoperable swarm, logic based mobile terminals running multimedia micro services based telematic system.

Słowa kluczowe

EN

telematic micro services; acoustics based internet of things; telematic; smart city

• Wydawca: Łukasiewicz Research Network - Institute of Aviation ; European Science Society of Powertrain and Transport KONES Poland ; Wydawnictwo Instytutu Technicznego Wojsk Lotniczych
• Czasopismo: Journal of KONES
• Rocznik: 2017
• Tom: Vol. 24, No. 3
• Strony: 111--118
• Opis fizyczny: Bibliogr. 10 poz., rys.

Twórcy

autor

Kannan J. R.

• VIT University School of Computing Science & Engineering, India

autor

Rai A.

• VIT University School of Computing Science & Engineering, India

autor

Szpytko J.

• AGH University of Science and Technology, Poland Adama Mickiewicza Av. 30, 30-059 Krakow

autor

Pandya J. C.

• AGH University of Science and Technology, Poland Adama Mickiewicza Av. 30, 30-059 Krakow

Bibliografia

• [1] National Academy of Science and Engineering, Recommendations for implementing the strategic initiative INDUSTRIE 4.0., Final report of the Industrie 4.0 Working Group, pp. 261-271, 2013.
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• [5] Abbasi-Yadkori, Y., Bartlett, P., Malek, A., Linear programming for large-scale Markov decision problems, Proceedings of the 31st International Conference on Machine Learning, pp. 124-132, 2014.
• [6] Ammar, H. B., Eaton, E., Ruvolo, P., Taylor, M. E., Online multi-task learning for policy gradient methods, Proceedings of the 31 st International Conference on Machine Learning, pp. 124-132, 2014.
• [7] Bratukhin, A., Sauter, T., Functional analysis of manufacturing execution system distribution, IEEE Trans. Ind. Informat., Vol. 7, No. 4, pp. 740-749, 2013.
• [8] Wilson, A., Fern, A., Ray, S., Tadepalli, P., Multi-task reinforcement learning: a hierarchicalBayesian approach, Proceedings of the 24th International Conference on Machine Learning(ICML), pp. 1015-1022, 2007.
• [9] Li, H., Liao, X., Carin, L., Multi-task reinforcement learning in partially observable stochasticenvironments, Journal of Machine Learning Research, Vol. 10, pp. 1131-1186, 2009.
• [10] Fernandez, F., Veloso, M., Learning domain structure through probabilistic policy reuse inreinforcement learning, Progress in AI, Vol. 2, No. 1, pp. 13-27, 2013.

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).