Stuck-at Fault Analytics of IoT Devices Using Knowledge-based Data Processing Strategy in Smart Grid
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.
KeywordsWireless IoT smart meter Smart grid HBase Stuck-at Hadoop
This work was supported by the National Research Foundation of Korea through the Korean government (MSIP) under Grant NRF-2016R1C1B2008624.
- 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.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.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.Google Scholar
- 7.Compagnoni, Christian Monzio. et al. (2017). Reviewing the evolution of the NAND Flash technology. In Proceedings of the IEEE.Google Scholar
- 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
- 12.King, J., & Perry, C. (2017). Smart buildings: Using smart technology to save energy in existing buildings.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.Google Scholar
- 16.McCluskey, E. J., Tseng, C.-W. (2000). Stuck-fault tests vs. actual defects. Test conference. Proceedings. International (p. 2000). IEEE.Google Scholar
- 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.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.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.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.Nagvajara, P., & Karpovsky, M. G. (1991). Built-in self-diagnostic read-only-memories. In Test conference, 1991, Proceedings, international. IEEE.Google Scholar
- 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.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
- 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
- 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.Cooke, J. (2007). The inconvenient truths of NAND flash memory. Flash Memory Summit.Google Scholar
- 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.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.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.Jimenez, X., Novo, D., Ienne, P. (2014). Wear unleveling: Improving NAND flash lifetime by balancing page endurance. FAST. Vol. 14..Google Scholar
- 36.Lee, S. et al. (2009). FlexFS: A flexible flash file system for MLC NAND flash memory. In USENIX annual technical conference.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.Google Scholar
- 40.Mohan, V., et al. (2010). How I learned to stop worrying and love flash endurance. HotStorage, 10, 3–3.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.Google Scholar
- 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.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.
- 47.Ajit Singh, EM Algorithm, 2005.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.
- 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