Skip to main content

Advertisement

SpringerLink
Log in

Resource-Optimal Heterogeneous Machine-to-Machine Communications in Software Defined Networking Cyber-Physical Systems

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Cyber-physical systems (CPS), emerging as the most promising approach for extensive computing, processing, and remote control to physical entities, rely on reliable and heterogeneous (real-time and non-real-time) data exchanges among machines. However, for CPS exploiting shared network infrastructures, communication links may suffer from various vulnerabilities to harm real-time data exchanges. Providing robustness against link vulnerabilities consequently becomes the most critical requirement. By installing software defined networking (SDN) functions in existing network infrastructures, multiple communication paths can be practically formed to provide robustness if replicates of real-time data are simultaneously forwarded via multiple paths. Furthermore, if each non-real-time data packet is forwarded via distinct paths, packet congestion in certain paths can be avoided. However, the key to practice such SDN robust communications relies on an efficient resource utilization both in the time and the spatial domains. To further support high mobile machines, the efficient resource utilization shall be achieved by a low complexity scheme to rapidly adapt to varying environments. In this paper, we shall consequently develop mathematical foundations of a resource-optimal design for CPS. Achieving the minimum resource usage to support real-time data exchanges, our design further avoids the worst case packet congestion to trace the performance of non-real-time data exchanges to the optimum. By providing the most efficient and low complexity resource management, our design successfully practice robust big data exchanges in CPS.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. 3GPP TR 23.898 V7.0.0. (2005). Access class barring and overload protection (ACBOP).

  2. 3GPP TR 36.814 V9.0.0. (2010). Further advancements for E-UTRA physical layer aspects.

  3. Abad, F.A.T., Caccamo, M., & Robbins, B. (2012). A fault resilient architecture for distributed cyber-physical systems. In Proceedings of IEEE RTCSA.

  4. Arumugam, S., Kalle, R. K., & Prasad, A. R. (2013). Wireless robotics: Opportunities and challenges. Wireless Personal Communications, 70(3), 1033–1058.

    Article  Google Scholar 

  5. Braginsky, D., & Estrin, D. (2002). Rumor routing algorithms for sensor networks. In Proceedings of ACM MobiCom Workshops.

  6. Calhoun, B., Lach, J., Stankovic, J., Wentzloff, D., Whitehouse, K., Barth, A., et al. (2012). Body sensor networks: A holistic approach from silicon to users. Proceedings of the IEEE, 100(1), 91–106.

    Article  Google Scholar 

  7. Chang, C. S., Chen, K. C., You, M. Y., & Chang, J. F. (1997). Guaranteed quality-of-service wireless access to ATM networks. IEEE Journal on Selected Areas in Communications, 15(1), 106–118.

    Article  Google Scholar 

  8. Chen, K.C., & Lien, S.Y. (2013). Machine-to-machine communications: Technologies and challenges. Ad Hoc Networks.

  9. Chen, P. Y., Ao, W. C., & Chen, K. C. (2012). Rate-delay enhanced multipath transmission sheme via network coding in multihop networks. IEEE Communications Letters, 16(3), 281–283.

    Article  MathSciNet  Google Scholar 

  10. Chiang, M., Low, S. H., Calderbank, A. R., & Doyle, J. C. (2007). Layering as optimization decomposition: A mathematical theory of network architectures. Proceedings of the IEEE, 95(1), 255–312.

    Article  Google Scholar 

  11. Dely, P., Kassler, A., & Bayer, N. (2011). Openflow for wireless mesh networks. In Proceedings of IEEE ICCCN.

  12. Dixon, C., & Frew, E. W. (2009). Maintaining optimal communication chains in robotic sensor networks using mobility control. Mobile Networks and Applications, 14(3), 281–291.

    Article  Google Scholar 

  13. Fink, J., Ribeiro, A., & Kumar, V. (2012). Robust control for mobility and wireless communication in cyber-physical systems with application to robot teams. Proceedings of the IEEE, 100(1), 164–178.

    Article  Google Scholar 

  14. Grossglauser, M., & Tse, D. (2002). Mobility increases the capacity of ad hoc wireless networks. IEEE/ACM Transactions on Networking, 10(4), 477–486.

    Article  Google Scholar 

  15. Han, H., Shakkottai, S., Hollot, C. V., Srikant, R., & Towsley, D. (2006). Multi-path TCP: A joint congestion control and routing scheme to exploit path divrsity in the internet. IEEE/ACM Transactions on Networking, 14(6), 1260–1271.

    Article  Google Scholar 

  16. He, Y., Zhu, W., & Guan, L. (2011). Optimal resource allocation for pervasive health monitoring systems with body sensor networks. IEEE Transactions on Mobile Computing, 10(11), 1558–1575.

    Article  Google Scholar 

  17. Jarraya, Y., Madi, T., & Debbabi, M. (2014). A survey and a layered taxonomy of software-defined networking. IEEE Communications Surveys and Tutorials, 16(4), 1955–1980.

    Article  Google Scholar 

  18. Julia, B. J., Angermann, M., Schwager, M., & Rus, D. (2012). Maintaining optimal communication chains in robotic sensor networks using mobility control. International Journal of Robotics Research, 31(10), 1134–1154.

    Article  Google Scholar 

  19. Kelly, F. P., & Voice, T. (2005). Stability of end-to-end algorithms for joint routing and rate control. In Proceedings of ACM SIGCOMM.

  20. Kim, H., & Feamster, N. (2013). Improving network management with software defined networking. IEEE Communications Magazine, 51(2), 114–119.

    Article  Google Scholar 

  21. Kim, Y. J., Kolesnikov, V., & Thottan, M. (2012). Resilient end-to-end message protection for large-scale cyber-physical system communications. In Proceedings of IEEE SmartGridComm.

  22. Knoll, A., & Prasad, R. (2012). Wireless robotics: A highly promising case for standardization. Wireelss Personal Communications, 64(3), 611–617.

    Article  Google Scholar 

  23. Kreutz, D., Ramos, F. M. V., Verissimo, P. E., Rothenberg, C. E., Azodolmolky, S., & Uhlig, S. (2015). Software-defined networking: A comprehensive survey. Proceedings of the IEEE, 103(1), 14–76.

    Article  Google Scholar 

  24. Kumar, P. R. S., & Jenkins, L. (2012). Stability analysis of aperiodic messages scheduled in the dynamic segment of flexray protocol. In Proceedings of ICCCNT.

  25. Li, H., Lai, L., & Poor, H. V. (2012). Multicast routing for decentralized control cyber-physical systems with an application in smart grid. IEEE Journal on Selected Areas in Communications, 30(6), 1097–1107.

    Article  Google Scholar 

  26. Li, X., Lille, I., Falcon, R., Nayak, A., & Stojmenovic, I. (2012). Serving wireless sensor networks by mobile robots. IEEE Communications Magazine, 50(7), 147–154.

    Article  Google Scholar 

  27. Li, X., Lu, R., Liang, X., Shen, X. S., Chen, J., & Lin, X. (2011). Smart community: An internet of things application. IEEE Communications Magazine, 49(11), 68–75.

    Article  Google Scholar 

  28. Li, X., Qiao, C., Yu, X., & Wagh, A. (2012). Toward effective service scheduling for human drivers in vehicular cyber-physical systems. IEEE Transactions on Parallel and Distributed Systems, 23(9), 1775–1789.

    Article  Google Scholar 

  29. Lien, S. Y., & Chen, K. C. (2011). Massive access management for QoS guarantees in 3GPP machine-to-machine communications. IEEE Communications Letters, 15(3), 311–313.

    Article  Google Scholar 

  30. Lien, S. Y., Chen, K. C., & Lin, Y. (2011). Toward ubiquitous massive accesses in 3GPP machine-to-machine communications. IEEE Communications Magazine, 49(4), 66–74.

    Article  Google Scholar 

  31. Lien, S. Y., Cheng, S. M., Shih, S. Y., & Chen, K. C. (2012). Radio resource management for QoS guarantees in cyber-physical systems. IEEE Transactions on Parallel and Distributed Systems, 23(9), 1752–1761.

    Article  Google Scholar 

  32. Lin, S. C., Gu, L., & Chen, K. C. (2012). Providing statistical QoS guarantees in large cognitive machine-to-machine networks. In Proceedingd of IEEE GLOBECOM Workshops.

  33. Liu, E., Zhang, Q., & Leung, K. K. (2012). Relay-assisted transmission with fairness constraint for cellular networks. IEEE Transactions on Mobile Computing, 11(2), 230–239.

    Article  Google Scholar 

  34. McKeown, N., Anderson, T., Balakrishnan, H., Prulkar, G., Peterson, L., Rexford, J., Shenker, S., & Turner, J. (2008). Openflow: Enabling innovation in campus networks. In Proceedings of ACM SIGCOMM.

  35. Movsichoff, B. A., Lagoa, C., & Che, H. (2007). End-to-end optimal algorithms for integrated QoS, traffic engineering, and failure recovery. IEEE/ACM Transactions on Networking, 15(4), 813–823.

    Article  Google Scholar 

  36. Niyato, D., Hossain, E., & Kim, D. I. (2009). Joint admission control and antenna assignment for multiclass QoS in spatial multiplexing MIMO wireless networks. IEEE Transactions on Wireless Communications, 8(9), 4855–4865.

    Article  Google Scholar 

  37. Nunes, B. A. A., Mendonca, M., Nguyen, X. N., Obraczka, K., & Turletti, T. (2014). A survey of software-defined networking: Past, present, and future of programmable networks. IEEE Communications Surveys and Tutorials, 16(3), 1617–1634.

    Article  Google Scholar 

  38. Park, K. J., Kim, J., Lim, H., & Eun, Y. (2014). Robust path diversity for network quality of service in cyber-physical systems. IEEE Transactions on Industrial Informatics, 10(4), 2204–2215.

    Article  Google Scholar 

  39. Srinivasan, R., Zhuang, J., Jalloul, L., Novak, R., & Park, J. (2008). IEEE 802.16m evaluation methodology document (EMD). http://www.ieee802.org/16/tgm/docs/80216m-08_004r2.pdf

  40. Stojmenovic, I. (2014). Machine-to-machine communications with in-network data aggregation, processing, and actuation for large-scale cyber-physical systems. IEEE Internet of Things Journal, 1(2), 122–128.

    Article  Google Scholar 

  41. Tabuada, P., Caliskan, S. Y., Rungger, M., & Majumdar, R. (2014). Towards robustness for cyber-physical systems. IEEE Transactions on Automatic Control, 59(12), 3151–3163.

    Article  MathSciNet  Google Scholar 

  42. Taleb, T., & Kunz, A. (2012). Machine type communications in 3GPP networks: Potential, challenges, and solutions. IEEE Communications Magazine, 50(3), 178–184.

    Article  Google Scholar 

  43. Tizghadam, A., & Leon-Garcia, A. (2010). Autonomic traffic engineering for network robustness. IEEE Journal on Selected Areas in Communications, 28(1), 39–50.

    Article  Google Scholar 

  44. Wisitpongphan, N., Bai, F., Mudalige, P., Sadekar, V., & Tonguz, O. (2007). Routing in sparse vehicular ad hoc wireless networks. IEEE Journal on Selected Areas in Communications, 25(8), 1538–1556.

    Article  Google Scholar 

  45. Xia, F., Ma, L., Dong, J., & Sun, Y. (2008). Network QoS management in cyber-physical systems. In Proceedings of ICESS.

  46. Xing, G., Jia, W., Du, Y., Tso, P., Sha, M., & Liu, X. (2008). Toward ubiquitous video-based cyber-physical systems. In Proceedings of IEEE SMC.

  47. Yagan, O., Qian, D., Zhang, J., & Cochran, D. (2012). Optimal allocation of interconnecting links in cyber-physical systems: Interdependence, cascading failures, and robustness. IEEE Transactions on Parallel and Distributed Systems, 23(9), 1708–1720.

    Article  Google Scholar 

  48. Yeganeh, S. H., Tootoonchian, A., & Ganjali, Y. (2013). On scalability of software-defined networking. IEEE Communications Magazine, 51(2), 136–141.

    Article  Google Scholar 

  49. Zhang, H., Ma, H., Li, X. Y., & Tang, S. (2013). In-network estimation with delay constraints in wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 24(2), 368–380.

    Article  Google Scholar 

  50. Zhang, J., Shan, L., Hu, H., & Yang, Y. (2012). Mobile cellular networks and wireless sensor networks: Toward convergence. IEEE Communications Magazine, 50(3), 164–169.

    Article  Google Scholar 

  51. Zheng, K., Hu, F., Wang, W., Xiang, W., & Dohler, M. (2012). Radio resource allocation in LTE-Advanced cellular networks with M2M communications. IEEE Communications Magazine, 50(7), 184–192.

    Article  Google Scholar 

Download references

Acknowledgments

This research is supported by the Ministry of Science and Technology under the contract MOST 103-2218-E-009-029.

Author information

Authors and Affiliations

  1. Department of Electronic Engineering, National Formosa University, No. 64, Wunhua Rd., Huwei Township, Yulin County, 63201, Taiwan

    Shao-Yu Lien

Authors
  1. Shao-Yu Lien
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shao-Yu Lien.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lien, SY. Resource-Optimal Heterogeneous Machine-to-Machine Communications in Software Defined Networking Cyber-Physical Systems. Wireless Pers Commun 84, 2215–2239 (2015). https://doi.org/10.1007/s11277-015-2560-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-015-2560-6

Keywords

Search

Navigation