Secure and Reliable Power Consumption Monitoring in Untrustworthy Micro-grids

  • Pacome L. AmbassaEmail author
  • Anne V. D. M. Kayem
  • Stephen D. Wolthusen
  • Christoph Meinel
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 523)


Micro-grid architectures based on renewable energy sources offer a viable solution to electricity provision in regions that are not connected to the national power grid or that are severely affected by load shedding. The limited power generated in micro-grids however makes monitoring power consumption an important consideration in guaranteeing efficient and fair energy sharing. A further caveat is that adversarial data tampering poses a major impediment to fair energy sharing on small scale energy systems, like micro-grids, and can result in a complete breakdown of the system. In this paper, we present an innovative approach to monitoring home power consumption in smart micro-grids. This is done by taking into account power consumption measurement on a per appliance and/or device basis. Our approach works by employing a distributed snapshot algorithm to asynchronously collect the power consumption data reported by the appliances and devices. In addition, we provide a characterization of noise that affects the quality of the data making it difficult to differentiate measurement errors and power fluctuations from deliberate attempts to misreport consumption.


Micro-grid Power consumption monitoring Noisy data Distributed snapshot algorithm Wireless sensor networks 


  1. 1.
    Chowdhury, S., Crossley, P.: Microgrids and Active Distribution Networks. IET Renewable Energy Series. Institution of Engineering and Technology, UK (2009)CrossRefGoogle Scholar
  2. 2.
    Considine, T., Cox, W., Cazalet, E.G.: Understanding microgrids as the essential architecture of smart energy. Grid Inerop Forum, December 2012Google Scholar
  3. 3.
    Erbato, T.T., Hartkopf, T.: Smarter micro grid for energy solution to rural Ethiopia. In: Innovative Smart Grid Technologies (ISGT), pp. 1–7. IEEE (2012)Google Scholar
  4. 4.
    Mariam, L., Basu, M., Conlon, M.: Community microgrid based on micro-wind generation system. In: 2013 48th International Universities’ Power Engineering Conference (UPEC), pp. 1–6, Sept 2013Google Scholar
  5. 5.
    Darby, S.: The effectiveness of feedback on energy consumption: a review for defra of the literature on metering, billing and direct displays. Technical report, Environmental Change Institute, University of Oxford (2006)Google Scholar
  6. 6.
    Erol-Kantarci, M., Mouftah, H.T.: Wireless sensor networks for smart grid applications. In: 2011 Saudi International Electronics, Communications and Photonics Conference (SIECPC), pp. 1–6, April 2011Google Scholar
  7. 7.
    Porcarelli, D., Balsamo, D., Brunelli, D., Paci, G.: Perpetual and low-cost power meter for monitoring residential and industrial appliances. In: Design, Automation Test in Europe Conference Exhibition (DATE), 2013, pp. 1155–1160, March 2013Google Scholar
  8. 8.
    Yerra, R.V.P., Bharathi, A.K., Rajalakshmi, P., Desai, U.: WSN based power monitoring in smart grids. In: 2011 Seventh International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), pp. 401–406, Dec 2011Google Scholar
  9. 9.
    Han, P., Wang, J., Han, Y., Zhao, Q.: Novel WSN-based residential energy management scheme in smart grid. In: 2012 International Conference on Information Science and Technology (ICIST), pp. 393–396, March 2012Google Scholar
  10. 10.
    Chandy, K.M., Lamport, L.: Distributed snapshots: determining global states of distributed systems. ACM Trans. Comput. Syst. 3(1), 63–75 (1985)CrossRefGoogle Scholar
  11. 11.
    Lai, T.H., Yang, T.H.: On distributed snapshots. Inf. Process. Lett. 25(3), 153–158 (1987)CrossRefzbMATHMathSciNetGoogle Scholar
  12. 12.
    Kshemkalyani, A.D., Raynal, M., Singhal, M.: An introduction to snapshot algorithms in distributed computing. Distrib. Syst. Eng. 2(4), 224–233 (1995)CrossRefGoogle Scholar
  13. 13.
    Agbaria, A., Sanders, W.H.: Distributed snapshots for mobile computing systems. In: Proceedings of the Second IEEE Annual Conference on Pervasive Computing and Communications, 2004, PerCom 2004, pp. 177–186, March 2004Google Scholar
  14. 14.
    da Silva, A.P.R., Teixeira, F.A., Lage, R.K.V., Ruiz, L.B., Loureiro, A.A.F., Nogueira, J.M.S.: Using a distributed snapshot algorithm in wireless sensor networks. In: Proceedings, The Ninth IEEE Workshop on Future Trends of Distributed Computing Systems, 2003, FTDCS 2003, pp. 31–37, May 2003Google Scholar
  15. 15.
    Segall, A.: Distributed network protocols. IEEE Trans. Inf. Theory 29(1), 23–35 (1983)CrossRefzbMATHMathSciNetGoogle Scholar
  16. 16.
    Wu, W., Liu, H., Wu, H.: Res: a robust and efficient snapshot algorithm for wireless sensor networks. In: 2012 32nd International Conference on Distributed Computing Systems Workshops (ICDCSW), pp. 231–236, June 2012Google Scholar
  17. 17.
    Gamze, U., Kemal Cagri, S., Sebnem, B.: DS+: reliable distributed snapshot algorithm for wireless sensor networks. J. Comput. Netw. Commun. 2013, 9 (2013)Google Scholar
  18. 18.
    Backes, M., Meiser, S.: Differentially private smart metering with battery recharging. In: Garcia-Alfaro, J., Lioudakis, G., Cuppens-Boulahia, N., Foley, S., Fitzgerald, W.M. (eds.) DPM 2013 and SETOP 2013. LNCS, vol. 8247, pp. 194–212. Springer, Heidelberg (2014) CrossRefGoogle Scholar
  19. 19.
    Liang, O., Sekercioglu, Y.A., Mani, N.: A low-cost flooding algorithm for wireless sensor networks. In: Wireless Communications and Networking Conference, WCNC 2007, pp. 3495–3500. IEEE, March 2007Google Scholar
  20. 20.
    Li, N., Hou, J.C., Sha, L.: Design and analysis of an MST-based topology control algorithm. IEEE Trans. Wirel. Commun. 4(3), 1195–1206 (2005)CrossRefGoogle Scholar
  21. 21.
    Wang, Z., Yi, D., Duan, X., Yao, J., Gu, D.: Measurement Data Modeling and Parameter Estimation. CRC Press, Boca Raton (2011) Google Scholar
  22. 22.
    Hughes, I.G., Hase, T.P.A.: Measurements and Their Uncertainties: A Practical Guide to Modern Error Analysis. Oxford University Press, New York (2010) Google Scholar
  23. 23.
    Liu, Y., Ning, P., Reiter, M.K.: False data injection attacks against state estimation in electric power grids. ACM Trans. Inf. Syst. Secur. 14(1), 13:1–13:33 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Pacome L. Ambassa
    • 1
    Email author
  • Anne V. D. M. Kayem
    • 1
  • Stephen D. Wolthusen
    • 2
    • 3
  • Christoph Meinel
    • 4
  1. 1.Department of Computer ScienceUniversity of Cape TownCape TownSouth Africa
  2. 2.Norwegian Information Security Laboratory, Faculty of Computer ScienceGjøvik University CollegeGjøvikNorway
  3. 3.Department of Mathematics, Information Security GroupRoyal Holloway, University of LondonEghamUK
  4. 4.Hasso Plattner Institute at University of PotsdamPotsdamGermany

Personalised recommendations