Advertisement

Mobile Networks and Applications

, Volume 23, Issue 4, pp 696–708 | Cite as

Data Aggregation Point Placement Problem in Neighborhood Area Networks of Smart Grid

  • Guodong Wang
  • Yanxiao Zhao
  • Yulong Ying
  • Jun Huang
  • Robb M. Winter
Article
  • 132 Downloads

Abstract

A smart meter neighborhood area network is usually regarded as the last mile network, which plays a significant role for communications in smart grid. A neighborhood area network typically consists of smart meters and Data Aggregation Points (DAPs), which collect energy consumption or billing information from smart meters and forward the information to wide area network gateways via wireless communications. The location of DAPs significantly affects the distance and associated transmission routes between DAPs and smart meters. In this paper, we investigate the DAP placement problem and propose solutions to reduce the distance between DAPs and smart meters. Specifically, the DAP placement problem is formulated with two objectives, e.g., the average distance minimization and the maximum distance minimization. The concept of network partition is introduced in this paper and two associated algorithms are developed to address the DAP placement problem. Extensive simulations are conducted based on a real suburban neighborhood topology. The simulation results verify that the proposed solutions are able to remarkably reduce the communication distance between DAPs and their associated smart meters.

Keywords

Smart meter DAP placement Network partition Transmission routes 

References

  1. 1.
    Utility-scale smart meter deployments:building block of the evolving power grid, IEI Report (2014)Google Scholar
  2. 2.
    Wenpeng L (2009) Advanced metering infrastructure. Southern Power Syst Technol 3(2):6–10Google Scholar
  3. 3.
    Aalamifar F, Shirazi GN, Noori M, Lampe L (2014) Cost-efficient data aggregation point placement for advanced metering infrastructure. In: 2014 IEEE International conference on smart grid communications (SmartGridComm), pp 344–349Google Scholar
  4. 4.
    Rolim G, Passos D, Moraes I, Albuquerque C (2015) Modelling the data aggregator positioning problem in smart grids. In: 2015 IEEE International conference on computer and information technology; ubiquitous computing and communications; dependable, autonomic and secure computing; pervasive intelligence and computing (CIT/IUCC/DASC/PICOM), pp 632–639Google Scholar
  5. 5.
    Yan Y, Qian Y, Sharif H, Tipper D (2013) A survey on smart grid communication infrastructures: motivations, requirements and challenges. Commun Surveys Tutor IEEE 15(1):5–20CrossRefGoogle Scholar
  6. 6.
    Amin M (2008) Challenges in reliability, security, efficiency, and resilience of energy infrastructure: toward smart self-healing electric power grid. In: 2008 IEEE Power and energy society general meeting-conversion and delivery of electrical energy in the 21st century, pp 1–5Google Scholar
  7. 7.
    Bennett C, Wicker SB (2010) Decreased time delay and security enhancement recommendations for AMI smart meter networks. Innovative Smart Grid Technologies (ISGT), pp 1–6Google Scholar
  8. 8.
    Sood VK, Fischer D, Eklund J, Brown T (2009) Developing a communication infrastructure for the smart grid. In: 2009 IEEE Electrical power & energy conference (EPEC), pp 1–7Google Scholar
  9. 9.
    Aggarwa A, Kunta S, Verma PK (2010) A proposed communications infrastructure for the smart grid. Innovative Smart Grid Technologies (ISGT), pp 1–5Google Scholar
  10. 10.
    Liu S, Zhang Z, Qi L, Ma M (2016) A fractal image encoding method based on statistical loss used in agricultural image compression. Multimed Tools Appl 75(23):15525–15536CrossRefGoogle Scholar
  11. 11.
    Liu S, Lu M, Liu G, Pan Z (2017) A novel distance metric: generalized relative entropy. Entropy 19 (6):269CrossRefGoogle Scholar
  12. 12.
    Liu S, Fu W, He L, Zhou J, Ma M (2017) Distribution of primary additional errors in fractal encoding method. Multimed Tools Appl 76(4):5787–5802CrossRefGoogle Scholar
  13. 13.
    Wang G, Wu Y, Dou K, Ren Y, Li J (2014) AppTCP: the design and evaluation of application-based TCP for e-VLBI in fast long distance networks. Futur Gener Comput Syst 39:67–74CrossRefGoogle Scholar
  14. 14.
    Wang G, Ren Y, Li J (2014) An effective approach to alleviating the challenges of transmission control protocol. IET Commun.  https://doi.org/10.1049/iet-com.2013.0154
  15. 15.
    Krishnamachari L, Estrin D, Wicker S (2002) The impact of data aggregation in wireless sensor networks. In: 22nd International conference on distributed computing systems workshops, pp 575–578Google Scholar
  16. 16.
    Yilmaz O, Demirci S, Kaymak Y, Ergun S, Yildirim A (2012) Shortest hop multipath algorithm for wireless sensor networks. Comput Math Appl 63(1):48–59CrossRefMATHGoogle Scholar
  17. 17.
    Bondy J, Murty U (2008) Graph theory (graduate texts in mathematics)Google Scholar
  18. 18.
    Ganesan D, Govindan R, Shenker S, Estrin D (2001) Highly-resilient, energy-efficient multipath routing in wireless sensor networks. ACM SIGMOBILE Mob Comput Commun Rev 5(4):11–25CrossRefGoogle Scholar
  19. 19.
    Muruganathan SD, Ma DC, Bhasin RI, Fapojuwo AO (2005) A centralized energy-efficient routing protocol for wireless sensor networks. IEEE Commun Mag 43(3):S8–13CrossRefGoogle Scholar
  20. 20.
    Goyal D, Tripathy MR (2012) Routing protocols in wireless sensor networks: a survey. In: 2012 Second international conference on advanced computing & communication technologies (ACCT), pp 474–480Google Scholar
  21. 21.
    Pantazis NA, Nikolidakis SA, Vergados DD (2013) Energy-efficient routing protocols in wireless sensor networks: a survey. IEEE Commun Surv Tutor 15(2):551–591CrossRefGoogle Scholar
  22. 22.
    Drezner Z, Hamacher HW (1995) Facility location. Springer-Verlag, New YorkCrossRefMATHGoogle Scholar
  23. 23.
    Farahani RZ, Hekmatfar M, Fahimnia B, Kazemzadeh N (2014) Hierarchical facility location problem: models, classifications, techniques, and applications. Comput Indus Eng 68:104–117CrossRefGoogle Scholar
  24. 24.
    Gellert W (2012) The VNR concise encyclopedia of mathematics. Springer Science & Business MediaGoogle Scholar
  25. 25.
    Aini A, Salehipour A (2012) Speeding up the Floyd–Warshall algorithm for the cycled shortest path problem. Appl Math Lett 25(1):1–5MathSciNetCrossRefMATHGoogle Scholar
  26. 26.
    Pallottino S, methods Shortest-path (1984) Complexity, interrelations and new propositions. Networks 14 (2):257–267CrossRefMATHGoogle Scholar
  27. 27.
    Magzhan K, Jani HM (2013) A review and evaluations of shortest path algorithms. Int J Sci Technol Res 2(6):99–104Google Scholar
  28. 28.
    Mahmood A, Javaid N, Razzaq S (2015) A review of wireless communications for smart grid. Renewable Sustain Energy Rev 41:248–260CrossRefGoogle Scholar
  29. 29.
    Luan S -W, Teng J -H, Chan S -Y, Hwang L -C (2010) Development of an automatic reliability calculation system for advanced metering infrastructure. In: 2010 8th IEEE International conference on industrial informatics (INDIN), pp 342–347Google Scholar
  30. 30.
    Heller B, Sherwood R, McKeown N (2012) The controller placement problem. In: The first workshop on Hot topics in software defined networks, pp 7–12Google Scholar
  31. 31.
    Yao G, Bi J, Li Y, Guo L (2014) On the capacitated controller placement problem in software defined networks. IEEE Commun Lett 18(8):1339–1342CrossRefGoogle Scholar
  32. 32.
    Wang G, Zhao Y, Huang J, Duan Q, Li J (2016) A K-means-based network partition algorithm for controller placement in software defined network international conference on communicationsGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringSouth Dakota School of Mines and TechnologyRapid CityUSA
  2. 2.School of Energy and Mechanical EngineeringShanghai University of Electric PowerShanghaiChina
  3. 3.School of Computer Science and TechnologyChongqing University of Posts and TelecomChongqingChina
  4. 4.Department of Chemical and Biological EngineeringSouth Dakota School of Mines and TechnologyRapid CityUSA

Personalised recommendations