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

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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.

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References

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

  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).

  5. G. KG, Toshiba Smart meter MCUs, Glyn.de, 2017. [Online]. Available: http://www.glyn.de.Lastaccessed. 27 April 2017.

  6. He, J. et al. (2017). The unwritten contract of solid state drives. In Proceedings of the twelfth European conference on computer systems. ACM.

  7. Compagnoni, Christian Monzio. et al. (2017). Reviewing the evolution of the NAND Flash technology. In Proceedings of the IEEE.

  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.

  9. Gungor, V. C., et al. (2011). Smart grid technologies: Communication technologies and standards. IEEE Transactions on Industrial Informatics, 7(4), 529–539.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  12. King, J., & Perry, C. (2017). Smart buildings: Using smart technology to save energy in existing buildings.

  13. Williams, T. W., & Brown, N. C. (1981). Defect level as a function of fault coverage. IEEE Transactions on Computers, 30(12), 987–988.

    Article  Google Scholar 

  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.

  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.

    Article  Google Scholar 

  16. McCluskey, E. J., Tseng, C.-W. (2000). Stuck-fault tests vs. actual defects. Test conference. Proceedings. International (p. 2000). IEEE.

  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.

  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.

  19. Sachdev, M., & Verstraelen, M. (1993). Development of a fault model and test algorithms for embedded DRAMs. In Test conference, 1993. Proceedings., International. IEEE.

  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.

  21. Nagvajara, P., & Karpovsky, M. G. (1991). Built-in self-diagnostic read-only-memories. In Test conference, 1991, Proceedings, international. IEEE.

  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.

  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.

  24. Soden, J. M., et al. (1992). IDDQ testing: A review. Journal of Electronic Testing, 3(4), 291–303.

    Article  Google Scholar 

  25. Gai, S., Mezzalama, M., & Prinetto, P. (1983). A review of fault models for LSI/VLSI devices. Software & Microsystems, 2(2), 44–53.

    Article  Google Scholar 

  26. Kim, H. et al. (2001). Design of dual-duplex system and evaluation of RAM. In Intelligent transportation systems, 2001. Proceedings. 2001 IEEE. IEEE.

  27. Corsi, A., & Morandi, C. (1983). A review of RAM testing methodologies. Microelectronics Journal, 14(2), 55–71.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

  30. Cooke, J. (2007). The inconvenient truths of NAND flash memory. Flash Memory Summit.

  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.

  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.

  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.

  34. Jimenez, X., Novo, D., Ienne, P. (2014). Wear unleveling: Improving NAND flash lifetime by balancing page endurance. FAST. Vol. 14..

  35. Bez, R., et al. (2003). Introduction to flash memory. Proceedings of the IEEE, 91(4), 489–502.

    Article  Google Scholar 

  36. Lee, S. et al. (2009). FlexFS: A flexible flash file system for MLC NAND flash memory. In USENIX annual technical conference.

  37. Desnoyers, P. (2010). Empirical evaluation of NAND flash memory performance. ACM SIGOPS Operating Systems Review, 44(1), 50–54.

    Article  Google Scholar 

  38. Bez, R., & Pirovano, A. (2004). Non-volatile memory technologies: Emerging concepts and new materials. Materials Science in Semiconductor Processing, 7(4), 349–355.

    Article  Google Scholar 

  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.

  40. Mohan, V., et al. (2010). How I learned to stop worrying and love flash endurance. HotStorage, 10, 3–3.

    Google Scholar 

  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.

    Article  Google Scholar 

  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.

  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.

  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. “Welcome to Apache Hadoop,” 2014. [Online]. Available: http://hadoop.apache.org/. Last accessed 27 Apr 2017.

  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.

    Article  Google Scholar 

  47. Ajit Singh, EM Algorithm, 2005.

  48. Forney, G. D. (1973). The Viterbi algorithm. Proceedings of the IEEE, 61(3), 268–278.

    Article  MathSciNet  Google Scholar 

  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. 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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. 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.

    Article  Google Scholar 

Download references

Acknowledgements

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

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Authors and Affiliations

  1. Department of Software Engineering, Mehran University of Engineering and Technology, Jamshoro, Pakistan

    Isma Farah Siddiqui & Muhammad Akram Shaikh

  2. Department of Computer Education, Sungkyunkwan University, Seoul, South Korea

    Nawab Muhammad Faseeh Qureshi

  3. Faculty of Electrical, Electronics, and Computer Engineering, Mehran University of Engineering and Technology, Jamshoro, Pakistan

    Bhawani Shankar Chowdhry

  4. Department of Computer Science and Engineering, Hanyang University ERICA, Ansan, South Korea

    Asad Abbas & Scott Uk-Jin Lee

  5. Faculty of Science and Technology, University of the Faroe Islands, Tórshavn, Faroe Islands

    Ali Kashif Bashir

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  1. Isma Farah Siddiqui
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Correspondence to Scott Uk-Jin Lee.

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Siddiqui, I.F., Qureshi, N.M.F., Shaikh, M.A. et al. Stuck-at Fault Analytics of IoT Devices Using Knowledge-based Data Processing Strategy in Smart Grid. Wireless Pers Commun 106, 1969–1983 (2019). https://doi.org/10.1007/s11277-018-5739-9

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