Skip to main content
Log in

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

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

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.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Utility-scale smart meter deployments:building block of the evolving power grid, IEI Report (2014)

  2. Wenpeng L (2009) Advanced metering infrastructure. Southern Power Syst Technol 3(2):6–10

    Google Scholar 

  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 344–349

  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 632–639

  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):5–20

    Article  Google Scholar 

  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 1–5

  7. Bennett C, Wicker SB (2010) Decreased time delay and security enhancement recommendations for AMI smart meter networks. Innovative Smart Grid Technologies (ISGT), pp 1–6

  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 1–7

  9. Aggarwa A, Kunta S, Verma PK (2010) A proposed communications infrastructure for the smart grid. Innovative Smart Grid Technologies (ISGT), pp 1–5

  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):15525–15536

    Article  Google Scholar 

  11. Liu S, Lu M, Liu G, Pan Z (2017) A novel distance metric: generalized relative entropy. Entropy 19 (6):269

    Article  Google Scholar 

  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):5787–5802

    Article  Google Scholar 

  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 39:67–74

    Article  Google Scholar 

  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. 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 575–578

  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):48–59

    Article  MATH  Google Scholar 

  17. Bondy J, Murty U (2008) Graph theory (graduate texts in mathematics)

  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):11–25

    Article  Google Scholar 

  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):S8–13

    Article  Google Scholar 

  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 474–480

  21. Pantazis NA, Nikolidakis SA, Vergados DD (2013) Energy-efficient routing protocols in wireless sensor networks: a survey. IEEE Commun Surv Tutor 15(2):551–591

    Article  Google Scholar 

  22. Drezner Z, Hamacher HW (1995) Facility location. Springer-Verlag, New York

    Book  MATH  Google Scholar 

  23. Farahani RZ, Hekmatfar M, Fahimnia B, Kazemzadeh N (2014) Hierarchical facility location problem: models, classifications, techniques, and applications. Comput Indus Eng 68:104–117

    Article  Google Scholar 

  24. Gellert W (2012) The VNR concise encyclopedia of mathematics. Springer Science & Business Media

  25. Aini A, Salehipour A (2012) Speeding up the Floyd–Warshall algorithm for the cycled shortest path problem. Appl Math Lett 25(1):1–5

    Article  MathSciNet  MATH  Google Scholar 

  26. Pallottino S, methods Shortest-path (1984) Complexity, interrelations and new propositions. Networks 14 (2):257–267

    Article  MATH  Google Scholar 

  27. Magzhan K, Jani HM (2013) A review and evaluations of shortest path algorithms. Int J Sci Technol Res 2(6):99–104

    Google Scholar 

  28. Mahmood A, Javaid N, Razzaq S (2015) A review of wireless communications for smart grid. Renewable Sustain Energy Rev 41:248–260

    Article  Google Scholar 

  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 342–347

  30. Heller B, Sherwood R, McKeown N (2012) The controller placement problem. In: The first workshop on Hot topics in software defined networks, pp 7–12

  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):1339–1342

    Article  Google Scholar 

  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 communications

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Guodong Wang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, G., Zhao, Y., Ying, Y. et al. Data Aggregation Point Placement Problem in Neighborhood Area Networks of Smart Grid. Mobile Netw Appl 23, 696–708 (2018). https://doi.org/10.1007/s11036-018-1002-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-018-1002-6

Keywords

Navigation