Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon openaccess Software-defined dynamic QoS provisioning for smart metering in energy Internet using fog computing and network calculus

This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Received 25/07/2017, Accepted 03/10/2017, Revised 07/09/2017, Published 11/10/2017

Energy Internet (EI) is a revolution of power systems due to its flexible energy control. The smart meters are the key components in EI which perform real-time monitoring and require high demands on quality of services (QoSs). Besides, due to the great performance on resolving device-interconnecting and realising overall network control, software-defined networks (SDNs) are considered as a trend for future EI. However, most existing software-defined EI have all the data processed at SDN controller which makes it difficult to realise fine-grained and deep edge computation and control. Meanwhile, most existing QoS provisioning cannot provide satisfying service for EI because various metering data from smart meters require different time-variant QoS guarantees. To address above challenges, the authors propose a software-defined dynamic QoS provisioning for smart metering in EI using fog computing and network calculus (NC). First, a software-defined EI architecture, where fogs are deployed on smart meters and base stations to realise edge services, is proposed to perform deep edge control in EI. Second, the software-defined dynamic QoS provisioning based on NC theory is proposed to guarantee QoS. Simulations of latency as well as bandwidth utilisation show the advantages of the proposed scheme.

Inspec keywords: cloud computing; software defined networking; smart meters; metering; telecommunication power management; quality of service; Internet

Other keywords: base stations; smart meters; smart metering; NC; fog computing; quality of services; software-defined networks; energy Internet; SDN controller; network calculus; edge services; software-defined dynamic QoS provisioning; bandwidth utilisation

Subjects: Telecommunication systems (energy utilisation); Computer communications; Other computer networks

References

    1. 1)
      • 33. Li, H., Zhang, J., Hong, Q., et al: ‘QoS-aware channel-width adaptation in wireless mesh networks’. IEEE Int. Conf. Communications, Malaysia, 2016, pp. 1–6.
    2. 2)
      • 18. Aydeger, A., Akkaya, K., Uluagac, A.: ‘SDN-based resilience for smart grid communications’. Network Function Virtualization and Software Defined Network (NFV-SDN) IEEE Conf., San Francisco, USA, November 2015, pp. 31–33.
    3. 3)
      • 34. Phakathi, T., Lugayizi, F., Isong, B., et al: ‘Quality of service of video streaming in vehicular ad hoc networks’. Int. Conf. Computational Science and Computational Intelligence, Las Vegas, NV, USA, 2016, pp. 886–891.
    4. 4)
      • 38. Alam, M.M., Razzaque, M. A., Mamun-Or-Rashid, M., et al: ‘Energy-aware QoS provisioning for wireless sensor networks: analysis and protocol’, J. Commun. Netw., 2009, 11, (11), pp. 309–405.
    5. 5)
      • 21. Alamri, A., Ansari, S., et al: ‘A survey on sensor-cloud: architecture, applications, and approaches’, Int. J. Distrib. Sens. Netw., 2013, 2013, (6), p. 18.
    6. 6)
      • 23. Chatterjee, S., Misra, S.: ‘Target tracking using sensor-cloud: sensor-target mapping in presence of overlapping coverage’, IEEE Commun. Lett., 2014, 18, (8), pp. 1435–1438.
    7. 7)
      • 19. Dorsch, N., Kurtz, F., Georg, H., et al: ‘Software-defined networking for smart grid communications: applications, challenges and advantages’. IEEE Int. Conf. Smart Grid Communications, Venice, Italy, September 2014, pp. 422–427.
    8. 8)
      • 12. Huang, A.: ‘FREEDM system – a vision for the future grid’. IEEE Power and Energy Society General Meeting, RI, USA, July 2010.
    9. 9)
      • 22. Chatterjee, S.A., Ladia, R., Misra, S.: ‘Dynamic optimal pricing for heterogeneous service-oriented architecture of sensor-cloud infrastructure’, EEE Trans. Serv. Comput., 2017, 10, (2), pp. 203–216.
    10. 10)
      • 26. Huang, L., Li, G., Wu, J., et al: ‘Software-defined QoS provisioning for fog computing advanced wireless sensor networks’. IEEE Sensors 2016, Orlando, USA, October 2016.
    11. 11)
      • 9. Zhao, J., Qiao, C., Yoon, S.: ‘Improve efficiency and reliability in single-hop WSNs with transmit-only nodes’, IEEE Trans. Parallel Distrib. Syst., 2013, 24, (3), pp. 520–534.
    12. 12)
      • 16. Li, G., Wu, J., Li, J., et al: ‘Battery status sensing software-defined multicast for V2G regulation in smart grid’, IEEE Sens. J., 2017, unpublished.
    13. 13)
      • 17. Oliverira, B.T.D., Gabriel, L.B., Margi, C.B.: ‘TinySDN: enabling multiple controllers for software-defined wireless sensors network’, IEEE Latin Am. Trans., 2015, 13, (11), pp. 3690–3696.
    14. 14)
      • 2. Wang, K., Yu, J., Yu, Y., et al: ‘A survey on energy Internet: architecture, approach, and emerging technologies’, IEEE Syst. J., 2007, PP, (99), pp. 1–14.
    15. 15)
      • 36. Kader, M.E.E.D.A.E., Youssif, A.A.A., Ghalwash, A.Z.: ‘Energy aware and adaptive cross-layer scheme for video transmission over wireless sensor networks’, IEEE Sens. J., 2016, 16, (21), pp. 7792–7802.
    16. 16)
      • 41. Al-Zubaidy, H., Liebeherr, J., Burchard, A.: ‘Network-layer performance analysis of multihop fading channels’, IEEE/ACM Trans. Netw., 2016, 24, (1), pp. 204–217.
    17. 17)
      • 1. Wang, K., Hu, X., Li, H., et al: ‘A survey on energy Internet communications for sustainability’, IEEE Trans. Sustain. Comput., 2017, 2, (3), pp. 231–254.
    18. 18)
      • 20. Hou, W., Tian, G., Guo, L., et al: ‘Cooperative mechanism for energy transportation and storage in Internet of energy’, IEEE Access, 2017, 5, pp. 1363–1375.
    19. 19)
      • 14. Kreutz, D., Ramos, F., Esteves, P., et al: ‘Software-defined networking: a comprehensive survey’, Proc. IEEE, 2015, 103, (1), pp. 14–76.
    20. 20)
      • 7. Ramakrishnan, R., Gaurm, L.: ‘Smart electricity distribution in residential areas: Internet of things (IoT) based advanced metering infrastructure and cloud analytics’. Int. Conf. Internet of Things and Applications, Pune, India, September 2016, pp. 46–51.
    21. 21)
      • 11. Hu, Y., Niu, Y., Lam, J., et al: ‘An energy-efficient adaptive overlapping clustering method for dynamic continuous monitoring in WSNs’, IEEE Sens. J., 2017, 17, (3), pp. 824–847.
    22. 22)
      • 13. Sandoval, M., Grijalva, S.: ‘Future grid business model innovation: distributed energy resources services platform for renewable energy integration’. Asia-Pacific Conf. Computer Aided System Engineering, Quito, Ecuador, 2015, pp. 72–77.
    23. 23)
      • 39. Fidler, M., Rizk, A.: ‘A guide to the stochastic network calculus’, IEEE Commun. Surv. Tutor., 2017, 17, (1), p. 92–105.
    24. 24)
      • 10. Al-Anbagi, I., Erol-Kantarci, M., Mouftah, H. T.: ‘A survey on cross-layer quality-of-service approaches in WSNs for delay and reliability-aware applications’, IEEE Commun. Surv. Tutor., 2016, 18, (1), pp. 525–552.
    25. 25)
      • 30. Balen, J., Zagar, D., Martinovic, G.: ‘Quality of service in wireless sensor networks: a survey and related patents’, Recent Patents Comput. Sci., 2011, 4, (3), pp. 188–202.
    26. 26)
      • 32. Hui, Y., Su, Z., Guo, G.: ‘Utility based data computing scheme to provide sensing service in Internet of things’, IEEE Trans. Emerging Top. Comput., 2017, unpublished.
    27. 27)
      • 28. Zeng, J., Wang, T., Lai, Y., et al: ‘Data delivery from WSNs to cloud based on a fog structure’. 2016 Int. Conf. Advanced Cloud and Big Data, Chengdu, China, 2016, pp. 104–109.
    28. 28)
      • 35. Long, Z., Su, M.: ‘Research on quality of service in wireless sensor networks’. Int. Conf. Information Engineering and Computer Science, Beijing, China, 2011, pp. 312–315.
    29. 29)
      • 15. Xia, W., Wen, Y., Foh, C.H., et al: ‘A survey on software defined networking’, IEEE Commun. Surv. Tutor., 2014, 17, (1), pp. 27–51.
    30. 30)
      • 3. Guo, L., Dong, M., Kaoru, O., et al: ‘Event-oriented dynamic security service for demand response in smart grid employing mobile networks’, China Commun., 2015, 12, (12), pp. 63–75.
    31. 31)
      • 24. Ali, A.W., Parmanand, : ‘Energy efficiency in routing protocol and data collection approaches for WSN: a survey’. 2015 Int. Conf. Computing, Communication and Automation, Noida, India, May 2015, pp. 540–545.
    32. 32)
      • 8. Al-Anbagi, I., Erol-Kantarci, M., Mouftah, H. T.: ‘Delay-aware medium access schemes for-based discharge measurement’, IEEE Trans. Instrum. Meas., 2014, 63, (12), pp. 3045–3057.
    33. 33)
      • 6. Zhong, W., Yu, R., Xie, S., et al: ‘Software defined networking for flexible and green energy Internet’, IEEE Commun. Mag., 2016, 54, (12), pp. 68–75.
    34. 34)
      • 27. Su, Z., Xu, Q., Hou, F., et al: ‘Edge caching for layered video contents in mobile social networks’, IEEE Trans. Multimedia, 2017, 19, (10), pp. 2210–2221.
    35. 35)
      • 29. Naranjo, P.G.V., Shojafar, M., Abraham, A., et al: ‘A new stable election-based routing algorithm to preserve aliveness and energy in fog-supported wireless sensor networks’. 2016 IEEE Int. Conf. Systems, Man, and Cybernetics, Budapest, Hungary, October 2016, pp. 2413–2418.
    36. 36)
      • 5. Celenlioglu, M., Goger, S., Mantar, H.: ‘An SDN-based energy aware routing model for intra-domain networks software’. 22nd Int. Conf. Telecommunications and Computer Networks, Split, Croatia, September 2014, pp. 61–66.
    37. 37)
      • 31. Su, Z., Qi, Q., Xu, Q., et al: ‘Incentive scheme for cyber physical social systems based on user behaviors’, IEEE Trans. Emerging Top. Comput., 2017, unpublished.
    38. 38)
      • 37. Lemeshko, O.V., Yeremenko, O.S., Hailan, A.M.: ‘Investigation of multipath QoS-routing dynamic tensor model’. IEEE Int. Conf. Electronics and Information Technology, Odessa, Ukraine, May 2016.
    39. 39)
      • 25. Aazam, M., Huh, E.N.: ‘Fog computing: the cloud-IoTIoE middleware paradigm’, IEEE Potentials, 2016, 35, (3), pp. 30–44.
    40. 40)
      • 4. Zhang, G., Su, L., Wang, Y.: ‘Research on communication network architecture of energy Internet based on SDN’. Advanced Research and Technology in Industry Applications, Ottawa, Canada, September 2014, pp. 316–319.
    41. 41)
      • 40. Bben, R., Fidler, M., Liebehen, J.: ‘Stochastic bandwidth estimation in networks with random service’, IEEE/ACM Trans. Netw., 2014, 22, (2), pp. 484–487.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cps.2017.0100
Loading

Related content

content/journals/10.1049/iet-cps.2017.0100
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address