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Graph-based fog computing network model

Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
IoT networks generate numerous amounts of data that is then transferred to the cloud for processing. Transferring data cleansing and parts of calculations towards these edge-level networks improves system’s, latency, energy consumption, network bandwidth and computational resources utilization, fault tolerance and thus operational costs. On the other hand, these fog nodes are resource-constrained, have extremely distributed and heterogeneous nature, lack horizontal scalability, and, thus, the vanilla SOA approach is not applicable to them. Utilization of Software Defined Network (SDN) with task distribution capabilities advocated in this paper addresses these issues. Suggested framework may utilize various routing and data distribution algorithms allowing to build flexible system most relevant for particular use-case. Advocated architecture was evaluated in agent-based simulation environment and proved its’ feasibility and performance gains compared to conventional event-stream approach.
Rocznik
Strony
5--20
Opis fizyczny
Bibliogr. 32 poz., fig.
Twórcy
  • National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Institute of Applied Systems Analysis, Department of System Design, 37, Peremohy ave., Kyiv, Ukraine
  • National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Institute of Applied Systems Analysis, Department of System Design, 37, Peremohy ave., Kyiv, Ukraine
autor
  • National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Institute of Applied Systems Analysis, Department of System Design, 37, Peremohy ave., Kyiv, Ukraine
Bibliografia
  • [1] Agarwal, S., Kodialam, M., & Lakshman, T. V. (2013). Traffic engineering in software defined networks. 2013 Proceedings IEEE INFOCOM, 2211–2219. https://doi.org/10.1109/INFCOM.2013.6567024
  • [2] Al Ameen, M., Liu, J., & Kwak, K. (2012). Security and privacy issues in wireless sensor networks for healthcare applications. Journal of Medical Systems, 36(1), 93–101. https://doi.org/10.1007/s10916-010-9449-4
  • [3] Castro-Jul, F., Conan, D., Chabridon, S., Díaz Redondo, R. P., Fernández Vilas, A., & Taconet, C. (2017). Combining Fog Architectures and Distributed Event-Based Systems for Mobile Sensor Location Certification. Lecture Notes in Computer Science, 10586, 27–33. https://doi.org/10.1007/978-3-319-67585-5_3
  • [4] Chan, M., Estève, D., Escriba, C., & Campo, E. (2008). A review of smart homes-Present state and future challenges. Computer Methods and Programs in Biomedicine, 91(1), 55–81. https://doi.org/10.1016/j.cmpb.2008.02.001
  • [5] Dias, L. M. S., Vieira, A. A. C., Pereira, G. A. B., & Oliveira, J. A. (2016). Discrete simulation software ranking — A top list of the worldwide most popular and used tools. 2016 Winter Simulation Conference (WSC), 1060–1071. https://doi.org/10.1109/WSC.2016.7822165
  • [6] Diogenes, Y. (2017). Internet Of Things Security Architecture. Retrieved December 31, 2018, from Microsoft website: https://docs.microsoft.com/en-us/azure/iot-fundamentals/iot-security-architecture
  • [7] Gope, P., & Hwang, T. (2016). BSN-Care: A Secure IoT-Based Modern Healthcare System Using Body Sensor Network. IEEE Sensors Journal, 16(5), 1368–1376. https://doi.org/10.1109/JSEN.2015.2502401
  • [8] Hussain, R., & Zeadally, S. (2019). Autonomous Cars: Research Results, Issues, and Future Challenges. IEEE Communications Surveys and Tutorials, 21(2), 1275–1313. https://doi.org/10.1109/COMST.2018.2869360
  • [9] IEEE Communications Society. (2018). IEEE Standard for Adoption of OpenFog Reference Architecture for Fog Computing. In The Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IEEESTD.2018.8423800
  • [10] Joshi, N. (n.d.). Fog vs Edge vs Mist computing. Which one is the most suitable for your business? Retrieved June 21, 2020, from https://www.allerin.com/blog/fog-vs-edge-vs-mist-computing-which-one-is-the-most-suitable-for-your-business
  • [11] Kharchenko, K., & Beznosyk, O. (2018). The input file format for IoT management systems based on a data flow virtual machine. 2018 IEEE 9th International Conference on Dependable Systems, Services and Technologies (DESSERT) (139–142). IEEE. https://doi.org/10.1109/DESSERT.2018.8409115
  • [12] Kirkpatrick, K. (2013). Software-defined networking. Communications of the ACM, 56(9), 16–19. https://doi.org/10.1145/2500468.2500473
  • [13] Klügl, F., & Bazzan, A. L. C. (2012). Agent-Based Modeling and Simulation. AI Magazine, 33(3), 29. https://doi.org/10.1609/aimag.v33i3.2425
  • [14] Laghari, S., & Niazi, M. A. (2016). Modeling the Internet of Things, Self-Organizing and Other Complex Adaptive Communication Networks: A Cognitive Agent-Based Computing Approach. PLOS ONE, 11(1), e0146760. https://doi.org/10.1371/journal.pone.0146760
  • [15] Lewis, P. R., Platzner, M., Rinner, B., Tørresen, J., & Yao, X. (2016). Self-aware Computing Systems. In P. R. Lewis, M. Platzner, B. Rinner, J. Tørresen, & X. Yao (Eds.), Natural Computing Series. https://doi.org/10.1007/978-3-319-39675-0
  • [16] Marz, N., & Warren, J. (2015). Big Data: Principles and best practices of scalable realtime data systems (1st ed.). Manning Publication.
  • [17] Mostafaei, H., & Menth, M. (2018). Software-defined wireless sensor networks: A survey. Journal of Network and Computer Applications, 119(June), 42–56. https://doi.org/10.1016/j.jnca.2018.06.016
  • [18] Multimethod Simulation Modeling for Business Applications – AnyLogic Simulation Software. (n.d.). Retrieved October 5, 2020, from https://www.anylogic.com/resources/white-papers/multimethod-simulation-modeling-for-business-applications/
  • [19] Petrenko, A., Kyslyi, R., & Pysmennyi, I. (2018a). Designing security of personal data in distributed health care platform. Technology Audit and …, 2(42). https://doi.org/10.15587/2312-8372.2018.141299
  • [20] Petrenko, A., Kyslyi, R., & Pysmennyi, I. (2018b). Detection of human respiration patterns using deep convolution neural networks. Eastern-European Journal of Enterprise Technologies, 4(9(94)), 6–13. https://doi.org/10.15587/1729-4061.2018.139997
  • [21] Pysmennyi, I., Kyslyi, R., & Petrenko, A. (2019). Edge computing in multi-scope service-oriented mobile healthcare systems. System Research and Information Technologies, (1), 118–127. https://doi.org/10.20535/SRIT.2308-8893.2019.1.09
  • [22] Rahmani, A. M., Liljeberg, P., Preden, J.-S., & Jantsch, A. (2018). Fog Computing in the Internet of Things. Springer. https://doi.org/10.1007/978-3-319-57639-8
  • [23] Ray, P. P. (2018). A survey on Internet of Things architectures. Journal of King Saud University - Computer and Information Sciences, 30(3), 291–319. https://doi.org/10.1016/j.jksuci.2016.10.003
  • [24] Oma, R., Nakamura, S., & Duolikun, D. (2019). A fault-tolerant tree-based fog computing model. International Journal of Web and Grid Services, 15(3), 219. https://doi.org/10.1504/IJWGS.2019.10022420
  • [25] Satyanarayanan, M. (2017). Edge Computing. Computer, 50(10), 36–38. https://doi.org/10.1109/MC.2017.3641639
  • [26] Sedgewick, R., & Wayne, K. (2011). Algorithms. In Foreign Affairs (4th ed.). Westford: Addison-Wesley.
  • [27] Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016). Edge Computing: Vision and Challenges. IEEE Internet of Things Journal, 3(5), 637–646. https://doi.org/10.1109/JIOT.2016.2579198
  • [28] Spot Instances – Amazon Elastic Compute Cloud. (n.d.). Retrieved July 7, 2020, from https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/using-spot-instances.html
  • [29] Stojmenovic, I., & Wen, S. (2014). The Fog Computing Paradigm: Scenarios and Security Issues. 2, 1–8. https://doi.org/10.15439/2014F503
  • [30] World Health Organization. (2010). Telemedicine Opportunities and developments in Member States. In World Health Organization (Vol. 2).
  • [31] Xiao, Y., & Zhu, Ch. (2017). Vehicular fog computing: Vision and challenges. 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), 6–9. https://doi.org/10.1109/PERCOMW.2017.7917508
  • [32] Yogi, M. K., Sekhar, K. C., & Kumar, G. V. (2017). Mist Computing: Principles, Trends and Future Direction. International Journal of Computer Science and Engineering, 4(7), 19–21. https://doi.org/10.14445/23488387/IJCSE-V4I7P104
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d3ef9da3-1ede-45a9-8ead-eac2b575a1d7
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