Advertisement

Computing Issues in the Edge

  • Yuchao Zhang
  • Ke Xu
Chapter
  • 59 Downloads

Abstract

Along with the development of IoT and mobile edge computing in recent years, everything can be connected into the network at anytime, resulting in quite dynamic networks with time-varying connections. Controllability has long been recognized as one of the fundamental properties of such temporal networks, which can provide valuable insights for the construction of new infrastructures, and thus is in urgent need to be explored. In this chapter, we take smart transportation as an example, first disclose the controllability problem in IoV (Internet of Vehicles) and then design a DND (driver node) algorithm based on Kalman’s rank condition to analyze the controllability of dynamic temporal network and also to calculate the minimum number of driver nodes. At last, we conduct a series of experiments to analyze the controllability of IoV network, and the results show the effects from vehicle density, speed, and connection radius on network controllability. These insights are critical for varieties of applications in the future smart connected living.

References

  1. 1.
    Xiao, Z., Moore, C., Newman, M.E.J.: Random graph models for dynamic networks. Eur. Phys. J. B 90(10), 200 (2016)MathSciNetGoogle Scholar
  2. 2.
    Casteigts, A., Flocchini, P., Quattrociocchi, W., Santoro, N.: Time-varying graphs and dynamic networks. Int. J. Parallel Emergent Distrib. Syst. 27(5), 387–408 (2012)CrossRefGoogle Scholar
  3. 3.
    Gerla, M., Lee, E.K., Pau, G., Lee, U.: Internet of vehicles: from intelligent grid to autonomous cars and vehicular clouds. In: Greengard, S. (ed.) Internet of Things. MIT Press, Cambridge (2016)Google Scholar
  4. 4.
    Alam, K.M., Saini, M., Saddik, A.E.: Toward social internet of vehicles: concept, architecture, and applications. IEEE Access 3, 343–357 (2015)CrossRefGoogle Scholar
  5. 5.
    Kaiwartya, O., Abdullah, A.H., Cao, Y., Altameem, A., Liu, X.: Internet of vehicles: motivation, layered architecture network model challenges and future aspects. IEEE Access 4, 5356–5373 (2017)CrossRefGoogle Scholar
  6. 6.
    Wang, W.X., Ni, X., Lai, Y.C., Grebogi, C.: Optimizing controllability of complex networks by minimum structural perturbations. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 85(2) Pt 2, 026115 (2012)Google Scholar
  7. 7.
    Francesco, S., Mario, D.B., Franco, G., Guanrong, C.: Controllability of complex networks via pinning. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 75(2), 046103 (2007)Google Scholar
  8. 8.
    Pasqualetti, F., Zampieri, S., Bullo, F.: Controllability metrics, limitations and algorithms for complex networks. IEEE Trans. Control Netw. Syst. 1(1), 40–52 (2014)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Cornelius, S.P., Kath, W.L., Motter, A.E.: Realistic control of network dynamics. Nat. Commun. 4(3), 1942 (2013)CrossRefGoogle Scholar
  10. 10.
    Yuan, Z., Zhao, C., Di, Z., Wang, W.X., Lai, Y.C.: Exact controllability of complex networks. Nat. Commun. 4(2447), 2447 (2013)CrossRefGoogle Scholar
  11. 11.
    Lombardi, A., Hörnquist, M.: Controllability analysis of networks. Phys. Rev. E 75(5) Pt 2, 056110 (2007)Google Scholar
  12. 12.
    Mauve, M., Vogel, J., Hilt, V., Effelsberg, W.: Local-lag and timewarp: providing consistency for replicated continuous applications. IEEE Trans. Multimedia 6(1), 47–57 (2004)CrossRefGoogle Scholar
  13. 13.
    Wang, H., Shea, R., Ma, X., Wang, F., Liu, J.: On design and performance of cloud-based distributed interactive applications. In: 2014 IEEE 22nd International Conference on Network Protocols (ICNP), pp. 37–46. IEEE (2014)Google Scholar
  14. 14.
    Pujol, E., Richter, P., Chandrasekaran, B., Smaragdakis, G., Feldmann, A., Maggs, B.M., Ng, K.-C.: Back-office web traffic on the internet. In: Proceedings of the 2014 Conference on Internet Measurement Conference, pp. 257–270. ACM (2014)Google Scholar
  15. 15.
    Zaki, Y., Chen, J., Potsch, T., Ahmad, T., Subramanian, L.: Dissecting web latency in ghana. In: Proceedings of the 2014 Conference on Internet Measurement Conference, pp. 241–248. ACM (2014)Google Scholar
  16. 16.
    Yue, K., Wang, X.-L., Zhou, A.-Y., et al.: Underlying techniques for web services: a survey. J. Softw. 15(3), 428–442 (2004)zbMATHGoogle Scholar
  17. 17.
    Li, X., Wang, X., Wan, P.-J., Han, Z., Leung, V.C.: Hierarchical edge caching in device-to-device aided mobile networks: modeling, optimization, and design. IEEE J. Sel. Areas Commun. 36(8), 1768–1785 (2018)CrossRefGoogle Scholar
  18. 18.
    Sadeghi, A., Sheikholeslami, F., Giannakis, G.B.: Optimal and scalable caching for 5G using reinforcement learning of space-time popularities. IEEE J. Sel. Top. Signal Process. 12(1), 180–190 (2018)CrossRefGoogle Scholar
  19. 19.
    Mao, H., Netravali, R., Alizadeh, M.: Neural adaptive video streaming with pensieve. In: Proceedings of the Conference of the ACM Special Interest Group on Data Communication, pp. 197–210. ACM (2017)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Yuchao Zhang
    • 1
  • Ke Xu
    • 2
  1. 1.Beijing University of Posts and TelecommBeijingChina
  2. 2.Tsinghua UniversityBeijingChina

Personalised recommendations