Stuck-at Fault Analytics of IoT Devices Using Knowledge-based Data Processing Strategy in Smart Grid

  • Isma Farah Siddiqui
  • Nawab Muhammad Faseeh Qureshi
  • Muhammad Akram Shaikh
  • Bhawani Shankar Chowdhry
  • Asad Abbas
  • Ali Kashif Bashir
  • Scott Uk-Jin Lee
Article

Abstract

Smart grid addresses traditional electricity generation issues by integrating ambient intelligence in actions of connected devices and production processing units. The grid infrastructure uses sensory IoT devices such as smart meter that records electric energy consumption and production information into the end units and stores sensor data through semantic technology in the central grid repository. The grid uses sensor data for various analytics such as production analysis of distribution units and health checkup of involved IoT devices and also observes functional profile of IoT equipment that includes service time, remaining lifespan, power consumption along with its functional error percentile. In a typical grid infrastructure, AMI meters process continuous streaming of data with Nand flash memory that stores dataset in the form of charges such as 0 and 1 in memory cell. Although, a flash memory is tested through rigorous testing profile but the grid environment impacts its cell endurance capacity diversely. Thus, a cell gets stuck-at fault before the end of endurance and can not be used to override a new tuple into it. In this paper, we perform a knowledge-based analytics to observe these stuck-at faults by detecting the abnormal variation among stored data tuples and predicts the going-to-be stuck-at cells of AMI meter. The simulation results show that the proposed approach rigorously maintain a knowledge-based track of AMI devices’ data production with an average error percentile of 0.06% in scanning blocks and performed prediction analytics according to the scanning percentile functional health and presents a work-flow to balance the load among healthy and unhealthy IoT devices in smart grid.

Keywords

Wireless IoT smart meter Smart grid HBase Stuck-at Hadoop 

Notes

Acknowledgements

This work was supported by the National Research Foundation of Korea through the Korean government (MSIP) under Grant NRF-2016R1C1B2008624.

References

  1. 1.
    Tuballa, M. L., & Abundo, M. L. (2016). A review of the development of smart grid technologies. Renewable and Sustainable Energy Reviews, 59, 710–725.CrossRefGoogle Scholar
  2. 2.
    Bera, S., Misra, S., & Rodrigues, J. J. P. C. (2015). Cloud computing applications for smart grid: A survey. IEEE Transactions on Parallel and Distributed Systems, 26(5), 1477–1494.CrossRefGoogle Scholar
  3. 3.
    Stojkoska, B. L. R., & Trivodaliev, K. V. (2016). A review of Internet of things for smart home: Challenges and solutions. Journal of Cleaner Production.Google Scholar
  4. 4.
    Chren, S., Rossi, B., & Pitner, T. (2016). Smart grids deployments within EU projects: The role of smart meters. In 2016 Smart cities symposium Prague (SCSP).Google Scholar
  5. 5.
    G. KG, Toshiba Smart meter MCUs, Glyn.de, 2017. [Online]. Available: http://www.glyn.de.Lastaccessed. 27 April 2017.
  6. 6.
    He, J. et al. (2017). The unwritten contract of solid state drives. In Proceedings of the twelfth European conference on computer systems. ACM.Google Scholar
  7. 7.
    Compagnoni, Christian Monzio. et al. (2017). Reviewing the evolution of the NAND Flash technology. In Proceedings of the IEEE.Google Scholar
  8. 8.
    Chaudhry, A. A., Kui, C., & Guan, Y. L. (2017). Mitigating stuck cell failures in MLC NAND flash memory via inferred erasure decoding. IEEE Transactions on Very Large-Scale Integration (VLSI) Systems.Google Scholar
  9. 9.
    Gungor, V. C., et al. (2011). Smart grid technologies: Communication technologies and standards. IEEE Transactions on Industrial Informatics, 7(4), 529–539.CrossRefGoogle Scholar
  10. 10.
    Gungor, V. C., et al. (2013). A survey on smart grid potential applications and communication requirements. IEEE Transactions on Industrial Informatics, 9(1), 28–42.CrossRefGoogle Scholar
  11. 11.
    Alahakoon, D., & Yu, X. (2016). Smart electricity meter data intelligence for future energy systems: A survey. IEEE Transactions on Industrial Informatics, 12(1), 425–436.CrossRefGoogle Scholar
  12. 12.
    King, J., & Perry, C. (2017). Smart buildings: Using smart technology to save energy in existing buildings.Google Scholar
  13. 13.
    Williams, T. W., & Brown, N. C. (1981). Defect level as a function of fault coverage. IEEE Transactions on Computers, 30(12), 987–988.CrossRefGoogle Scholar
  14. 14.
    Millman, S. D., McCluskey, E. J., Acken, J. M. (1990). Diagnosing CMOS bridging faults with stuck-at fault dictionaries. In Test conference, 1990. Proceedings, International. IEEE.Google Scholar
  15. 15.
    Dekker, R., Beenker, F., & Thijssen, L. (1990). A realistic fault model and test algorithms for static random access memories. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 9(6), 567–572.CrossRefGoogle Scholar
  16. 16.
    McCluskey, E. J., Tseng, C.-W. (2000). Stuck-fault tests vs. actual defects. Test conference. Proceedings. International (p. 2000). IEEE.Google Scholar
  17. 17.
    Lima, F., Carro, L., & Reis, R. (2003). Designing fault tolerant systems into SRAM-based FPGAs. In Proceedings of the 40th annual design automation conference. ACM.Google Scholar
  18. 18.
    Van De Goor, A. J.., & Al-Ars, Z. (2000) Functional memory faults: A formal notation and a taxonomy. In VLSI test symposium, 2000. Proceedings. 18th IEEE. IEEE.Google Scholar
  19. 19.
    Sachdev, M., & Verstraelen, M. (1993). Development of a fault model and test algorithms for embedded DRAMs. In Test conference, 1993. Proceedings., International. IEEE.Google Scholar
  20. 20.
    Wiscombe, P. C. (1993). A comparison of stuck-at fault coverage and I/sub DDQ/testing on defect levels. In Test conference, 1993. Proceedings, International. IEEE.Google Scholar
  21. 21.
    Nagvajara, P., & Karpovsky, M. G. (1991). Built-in self-diagnostic read-only-memories. In Test conference, 1991, Proceedings, international. IEEE.Google Scholar
  22. 22.
    Fan, X., et al. (2005). A novel stuck-at based method for transistor stuck-open fault diagnosis. In Test conference, 2005. Proceedings. ITC 2005. IEEE International. IEEE.Google Scholar
  23. 23.
    Mikitjuk, V. G., V. N. Yarmolik, Van De Goor, A. J. (1996). Ram testing algorithms for detection multiple linked faults. In European design and test conference, 1996. ED&TC 96. Proceedings. IEEE.Google Scholar
  24. 24.
    Soden, J. M., et al. (1992). IDDQ testing: A review. Journal of Electronic Testing, 3(4), 291–303.CrossRefGoogle Scholar
  25. 25.
    Gai, S., Mezzalama, M., & Prinetto, P. (1983). A review of fault models for LSI/VLSI devices. Software & Microsystems, 2(2), 44–53.CrossRefGoogle Scholar
  26. 26.
    Kim, H. et al. (2001). Design of dual-duplex system and evaluation of RAM. In Intelligent transportation systems, 2001. Proceedings. 2001 IEEE. IEEE.Google Scholar
  27. 27.
    Corsi, A., & Morandi, C. (1983). A review of RAM testing methodologies. Microelectronics Journal, 14(2), 55–71.CrossRefGoogle Scholar
  28. 28.
    Kuo, T.-W., et al. (2011). An efficient fault detection algorithm for NAND flash memory. ACM SIGAPP Applied Computing Review, 11(2), 8–16.CrossRefGoogle Scholar
  29. 29.
    Chaudhry, A. A., Kui, C., & Guan Y. L.. (2017). Mitigating stuck cell failures in MLC NAND flash memory via inferred erasure decoding. IEEE Transactions on Very Large Scale Integration (VLSI) Systems.Google Scholar
  30. 30.
    Cooke, J. (2007). The inconvenient truths of NAND flash memory. Flash Memory Summit.Google Scholar
  31. 31.
    Kgil, T., Roberts, D., Mudge, T. (2008) Improving NAND flash based disk caches. In Computer Architecture, 2008. ISCA’08. 35th International Symposium on. IEEE.Google Scholar
  32. 32.
    Grupp, L. M., Davis, J. D., Swanson, S. (2012). The bleak future of NAND flash memory. In Proceedings of the 10th USENIX conference on file and storage technologies. USENIX Association.Google Scholar
  33. 33.
    Kgil, T., & Mudge, T. (2006). FlashCache: A NAND flash memory file cache for low power web servers. In Proceedings of the 2006 international conference on Compilers, architecture and synthesis for embedded systems. ACM.Google Scholar
  34. 34.
    Jimenez, X., Novo, D., Ienne, P. (2014). Wear unleveling: Improving NAND flash lifetime by balancing page endurance. FAST. Vol. 14..Google Scholar
  35. 35.
    Bez, R., et al. (2003). Introduction to flash memory. Proceedings of the IEEE, 91(4), 489–502.CrossRefGoogle Scholar
  36. 36.
    Lee, S. et al. (2009). FlexFS: A flexible flash file system for MLC NAND flash memory. In USENIX annual technical conference.Google Scholar
  37. 37.
    Desnoyers, P. (2010). Empirical evaluation of NAND flash memory performance. ACM SIGOPS Operating Systems Review, 44(1), 50–54.CrossRefGoogle Scholar
  38. 38.
    Bez, R., & Pirovano, A. (2004). Non-volatile memory technologies: Emerging concepts and new materials. Materials Science in Semiconductor Processing, 7(4), 349–355.CrossRefGoogle Scholar
  39. 39.
    Cho, S., & Lee, H. (2009). Flip-N-write: A simple deterministic technique to improve PRAM write performance, energy and endurance. In Microarchitecture, 2009. MICRO-42. 42nd annual IEEE/ACM international symposium on. IEEE.Google Scholar
  40. 40.
    Mohan, V., et al. (2010). How I learned to stop worrying and love flash endurance. HotStorage, 10, 3–3.Google Scholar
  41. 41.
    Park, M., et al. (2007). The effect of trapped charge distributions on data retention characteristics of NAND flash memory cells. IEEE Electron Device Letters, 28(8), 750–752.CrossRefGoogle Scholar
  42. 42.
    Kim, W., et al. (2009). Multi-layered vertical gate NAND flash overcoming stacking limit for terabit density storage. In VLSI Technology, 2009 Symposium on. IEEE.Google Scholar
  43. 43.
    Zubair, M., Wahab, F., Hussain, I., Zaffar, J. (2010). Improved text scanning approach for exact string matching. In Proceedings of international conference on information and emerging technologies.Google Scholar
  44. 44.
    A. S. Foundation, “Text output format API,” 2016. [Online]. Available:https://hadoop.apache.org/docs/r2.7.2/api/org/apache/hadoop/mapreduce/lib/output/TextOutputFormat.html. Last Accessed 27 Apr 2017.
  45. 45.
    “Welcome to Apache Hadoop,” 2014. [Online]. Available: http://hadoop.apache.org/. Last accessed 27 Apr 2017.
  46. 46.
    Ghahramani, Z. (2001). An introduction to hidden Markov models and Bayesian networks. International Journal of Pattern Recognition and Artificial Intelligence, 15(1), 9–42.CrossRefGoogle Scholar
  47. 47.
    Ajit Singh, EM Algorithm, 2005.Google Scholar
  48. 48.
    Forney, G. D. (1973). The Viterbi algorithm. Proceedings of the IEEE, 61(3), 268–278.MathSciNetCrossRefGoogle Scholar
  49. 49.
    C.-L. N. Revolution, Smart Meter Dataset, Customer-Led Network Revolution, 2016. [Online]. Available http://www.networkrevolution.co.uk/project-library/dataset-tc1a-basic-profiling-domestic-smart-meter-customers/. Last accessed: 27 Apr 2017.
  50. 50.
    Musaddiq, A., Zikria, Y. B., Hahm, O., Yu, H., Bashir, A. K., & Kim, S. W. (2018). A survey on resource management in IoT operating systems. IEEE Access, 6, 8459–8482.CrossRefGoogle Scholar
  51. 51.
    Qureshi, N. M. F., Shin, D. R., Siddiqui, I. F., & Chowdhry, B. S. (2017). Storage-tag-aware scheduler for hadoop cluster. IEEE Access, 5, 13742–13755.CrossRefGoogle Scholar
  52. 52.
    Qureshi, N. M. F. & Shin, D. R. (2016). RDP: A storage-tier-aware robust data placement strategy for hadoop in a cloud-based heterogeneous environment. KSII Transactions on Internet and Information Systems, 10(9), 4063–4086.Google Scholar
  53. 53.
    Siddiqui, I. F., Lee, S. U. J., Abbas, A., & Bashir, A. K. (2017). Optimizing lifespan and energy consumption by smart meters in green-cloud-based smart grids. IEEE Access, 5, 20934–20945.CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  • Isma Farah Siddiqui
    • 1
  • Nawab Muhammad Faseeh Qureshi
    • 2
  • Muhammad Akram Shaikh
    • 1
  • Bhawani Shankar Chowdhry
    • 3
  • Asad Abbas
    • 4
  • Ali Kashif Bashir
    • 5
  • Scott Uk-Jin Lee
    • 4
  1. 1.Department of Software EngineeringMehran University of Engineering and TechnologyJamshoroPakistan
  2. 2.Department of Computer EducationSungkyunkwan UniversitySeoulSouth Korea
  3. 3.Faculty of Electrical, Electronics, and Computer EngineeringMehran University of Engineering and TechnologyJamshoroPakistan
  4. 4.Department of Computer Science and EngineeringHanyang University ERICAAnsanSouth Korea
  5. 5.Faculty of Science and TechnologyUniversity of the Faroe IslandsTórshavnFaroe Islands

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