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The Wireless Access for Future Smart Cities as a Large Scale Complex Cyber Physical System

  • Vladimir PoulkovEmail author
Article
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Abstract

In future smart cities (SCs) highly developed and smart wireless communication access infrastructures will be needed for the connection of a huge number of different types of objects, sensors and user terminals. Such access networks must have the necessary autonomic and intelligent mechanisms to respond to the needs of an increasing variety of users (human and non-human), to cope with the high user density in SCs, their mobility, new and increasing service requirements, traffic dynamics, SC complex wireless channel conditions, etc. The wireless AN of a future SC must be a type of network which is able to offer revolutionary services, capabilities, and facilities that are hard to be provided via the heterogeneous network (HetNet) infrastructures that are implemented today. This paper introduces the concept of the unified wireless access (UWA) for SCs and considers some of the challenges related to its functional requirements and design. The structure of a sample UWA network illustrating the functional relations between its components is given. It is envisaged that such UWA architecture will perform and could be considered from the aspect of a large scale complex and intelligent cyber physical system (CPS) with control feedbacks and different types of users introducing stochasticity in the loop of the system. For the goal of analyzing the performance and functional relationships between the elements of such UWA a general modeling approach is introduced taking into consideration some of the basic approaches applied for CPS analysis.

Keywords

Access networks Cyber physical systems Heterogeneous networks Modelling cyber physical systems Smart city Wireless communication access 

Notes

Acknowledgements

This work was supported in part by the contract DN 07/22 15.12.2016 of the Bulgarian Research Fund.

References

  1. 1.
    Kyoseva, T., Poulkov, V., Mihaylov, M., & Mihovska, A. (2014). Disruptive innovations as a driving force for the change of wireless telecommunication infrastructures. Wireless Personal Communications, 78(3), 1683–1697.  https://doi.org/10.1007/s11277-014-1902-0.CrossRefGoogle Scholar
  2. 2.
    Asenov, O., & Poulkov, V. (2013). Multimedia and network quality of service. In L. P. Ligthart & R. Prasad (Eds.), Communications, navigation, sensing and services (pp. 115–139). Aalborg: River Publishers.Google Scholar
  3. 3.
    Asenov, O., Poulkov, V., Mihovska, A., & Prasad, R. (2015). An Approach to Resource Management in Future Internet. In R. Prasad (Ed.), Resource management in future internet (pp. 185–209). Aalborg: River Publishers.Google Scholar
  4. 4.
    Giust, F., et al. (2015). Distributed mobility management for future 5G networks: Overview and analysis of existing approaches. IEEE Communications Magazine, 53(1), 142–149.CrossRefGoogle Scholar
  5. 5.
    Cimmino, A., et al. (2014). The role of small cell technology in future smart city applications. Transactions on Emerging Telecommunications Technologies, 25(1), 11–20.CrossRefGoogle Scholar
  6. 6.
    Han, T., et al. (2015). Interference minimization in 5G heterogeneous networks. Mobile Networks and Applications, 20(6), 756–762.CrossRefGoogle Scholar
  7. 7.
    Han, T., et al. (2017). 5G converged cell-less communications in smart cities. IEEE Communications Magazine, 55(3), 44–50.CrossRefGoogle Scholar
  8. 8.
    Wang, Xiaofei, Li, Xiuhua, & Leung, Victor C. M. (2015). Artificial intelligence-based techniques for emerging heterogeneous network: State of the arts, opportunities, and challenges. IEEE Access, 3, 1379–1391.CrossRefGoogle Scholar
  9. 9.
    Semov, P., Al-Shatri, H., Tonchev, K., Poulkov, V., & Klein, A. (2017). Implementation of machine learning for autonomic capabilities in self-organizing heterogeneous networks. Wireless Personal Communications, 92(1), 149–168.  https://doi.org/10.1007/s11277-016-3843-2.CrossRefGoogle Scholar
  10. 10.
    Peng, M., Liang, D., Wei, Y., Li, J., & Chen, H.-H. (2013). Self-configuration and self-optimization in LTE-advanced heterogeneous networks. IEEE Communications Magazine, 51(5), 36–45.CrossRefGoogle Scholar
  11. 11.
    Asenov, O., & Poulkov, V. (2014). Towards a unified virtual mobile wireless architecture. Journal of Communication, Navigation, Sensing and Services (CONASENSE), 1(1), 93–104.CrossRefGoogle Scholar
  12. 12.
    Poulkov, V. (2016). Beyond the next generation access. In R. Prasad & S. Dixit (Eds.), Wireless world in 2050 and beyond: A window into the future! (pp. 17–39). Basel: Springer. ISBN 978-3-319-42140-7.CrossRefGoogle Scholar
  13. 13.
    Weldon, M. (2016). The future X network: A bell labs perspective. Boca Raton: Taylor & Francis Group. ISBN 978-1-4987-5927-4.CrossRefGoogle Scholar
  14. 14.
    Prasad, R. (2012). Future networks and technologies supporting innovative communications. In IEEE international conference on network infrastructure and digital content (IC-NIDC), Sept 21–23 (pp. 4–6).  https://doi.org/10.1109/icnidc.2012.6418846.
  15. 15.
    Gill, H. (2008). A continuing vision: Cyber-physical systems. In Annual Carnegie Mellon conference on the electricity industry future energy systems: Efficiency, security, control, March 10–11.Google Scholar
  16. 16.
    He, J. F. (2010). Cyber-physical systems. Communication China Computer Federation, 6(1), 25–29.MathSciNetGoogle Scholar
  17. 17.
    Engell, S. (2014). Cyber-physical systems of systems—Definition and core research and innovation areas. Working Paper of the Support Action CPSoS. http://www.cpsos.eu/wp-content/uploads/2015/07/CPSoS-Scope-paper-vOct-26-2014.pdf. Accessed 31 Jan 2018.
  18. 18.
    Liu, Y., Peng, Y., Wang, B., Yao, S., & Liu, Z. (2017). Review on cyber-physical systems. IEEE/CAA Journal of Automatica Sinica, 4(1), 27–40.CrossRefGoogle Scholar
  19. 19.
    Feng, S., Quivira, F., & Schirner, G. (2016). Framework for rapid development of embedded human-in-the-loop cyber-physical systems. In IEEE international conference on bioinformatics and bioengineering (BIBE), Oct 31–Nov 2 (pp. 208–215).  https://doi.org/10.1109/bibe.2016.24.
  20. 20.
    Romero, D., Bernus, P., Noran, O., Stahre, J., & Fast-Berglund, Å. (2016). The operator 4.0: Human cyber-physical systems and adaptive automation towards human-automation symbiosis work systems. In International conference advances in production management systems (APMS), Sept 3–7 (pp. 677–686).Google Scholar
  21. 21.
    Manolova, A., Poulkov, V., & Tonchev, K. (2017). Challenges in the design of smart vehicular cyber physical systems with human in the loop. In L. P. Ligthart & R. Prasad (Eds.), Breakthroughs in smart city implementation (pp. 165–186). Aalborg: River Publishers.Google Scholar
  22. 22.
    Vogel-Heuser, B., & Hess, D. (2016). Guest editorial industry 4.0—Prerequisites and visions. IEEE Transactions on Automation Science and Engineering, 13, 411.CrossRefGoogle Scholar
  23. 23.
    Böhmann, T., Leimeister, J. M., & Möslein, K. (2014). Service systems engineering: A field for future information systems research. Business & Information Systems Engineering, 6(3), 73–79.CrossRefGoogle Scholar
  24. 24.
    Matzner, M., & Scholta, H. (2014). Process mining approaches to detect organizational properties in cyber-physical systems. In European conference on information systems (ECIS), Tel Aviv, Israel.Google Scholar
  25. 25.
    Soeldner, C., Roth, A., Danzinger, F., & Moeslein, K. (2013). Towards open innovation in embedded systems. In Americas conference on information systems (AMCIS), Chicago, USA.Google Scholar
  26. 26.
    Zdravković, M., Noran, O., & Trajanović, M. (2014). Towards sensing information systems. In International conference on information systems development (ISD), Varazdin, Croatia.Google Scholar
  27. 27.
    Mikusz, M. (2014). Towards an understanding of cyber-physical systems as industrial software-product-service systems. Procedia CIRP, 16, 385–389.CrossRefGoogle Scholar
  28. 28.
    Heppelmann, J. E., & Porter, M. E. (2014). How SMART, CONNECTED PRODUCTS ARE TRANSFORMING COMPETITION. Harvard Business Review, 92, 64–86.Google Scholar
  29. 29.
    Acatech. (2011). Cyber-physical systems: Innovationsmotor für Mobilität, Gesundheit, Energie und Produktion. Heidelberg: Springer.Google Scholar
  30. 30.
    Herterich, M. M., Uebernickel, F., & Brenner, W. (2015). The impact of cyber physical systems on industrial services in manufacturing. Procedia CIRP, 30, 323–328.CrossRefGoogle Scholar
  31. 31.
    Brettel, M., Friedrichsen, N., Keller, M., & Rosenberg, M. (2014). How virtualization, decentralization and network building change the manufacturing landscape: An Industry 4.0 perspective. Periodical, 8(1), 37–44.Google Scholar
  32. 32.
    Kolberg, D., & Zühlke, D. (2015). Lean Automation enabled by Industry 4.0 Technologies. IFAC-PapersOnLine, 48(3), 1870–1875.CrossRefGoogle Scholar
  33. 33.
    Cao, G., Duan, Y., & Li, G. (2015). Linking business analytics to decision making effectiveness: A path model analysis. IEEE Transactions on Engineering Management, 62(3), 384–395.CrossRefGoogle Scholar
  34. 34.
    Geissbauer, R., Schrauf, S., Koch, V., & Kuge, S. (2014). Industry 4.0—Opportunities and challenges of the industrial internet. https://www.pwc.nl/en/assets/documents/pwc-industrie-4-0.pdf. Accessed 31 Jan 2018.
  35. 35.
    Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165–1188.CrossRefGoogle Scholar
  36. 36.
    Alpaydin, E. (2014). Introduction to machine learning. Cambridge: MIT Press.zbMATHGoogle Scholar
  37. 37.
    Iliev, I., Bonev, B., Angelov, K., Petkov, P., & Poulkov, V. (2016). Interference identification based on long term spectrum monitoring and cluster analysis. In IEEE international conference “BlackSeaCom”, Varna, Bulgaria, June 6–9 (pp. 1–6).Google Scholar
  38. 38.
    Ni, F., Zang, Y., & Feng, Z. (2015). A study on cellular wireless traffic modeling and prediction using Elman Neural Networks. In International conference on computer science and network technology (ICCSNT), December 19–20 (pp. 490–494).Google Scholar
  39. 39.
    Khan, F. H., Ali, M. E., & Dev, H. (2015). A hierarchical approach for identifying user activity patterns from mobile phone call detail records. In International conference on networking systems and security (NSysS), January 5–7 (pp. 1–6).Google Scholar
  40. 40.
    Hehenberger, P., Vogel-Heuser, B., et al. (2016). Design, modelling, simulation and integration of cyber physical systems: Methods and applications. Computers in Industry, 82, 273–289.CrossRefGoogle Scholar
  41. 41.
    Lee, E. A. (2015). The past, present and future of cyber-physical systems: A focus on models. Sensors, 15(3), 4837–4869.  https://doi.org/10.3390/s150304837.CrossRefGoogle Scholar
  42. 42.
    Sharma, A. B., et al. (2014). Modeling and analytics for cyber-physical systems in the age of big data. ACM SIGMETRICS Performance Evaluation Review, 41(4), 74–77.CrossRefGoogle Scholar
  43. 43.
    Koleva, P., Poulkov, V., & Asenov, O. (2014). Resource management based on dynamic users association for future heterogeneous telecommunication access infrastructures. Wireless Personal Communications, 78(3), 1595–1611.  https://doi.org/10.1007/s11277-014-1911-z.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of TelecommunicationsTechnical University of SofiaSofiaBulgaria

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