KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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Compressed Sensing-Based Multi-Layer Data Communication in Smart Grid Systems

  • Islam, Md. Tahidul (School of Electrical Engineering, University of Ulsan) ;
  • Koo, Insoo (School of Electrical Engineering, University of Ulsan)
  • Received : 2013.06.27
  • Accepted : 2013.09.03
  • Published : 2013.09.30
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Abstract

Compressed sensing is a novel technology used in the field of wireless communication and sensor networks for channel estimation, signal detection, data gathering, network monitoring, and other applications. It plays a significant role in highly secure, real-time, well organized, and cost-effective data communication in smart-grid (SG) systems, which consist of multi-tier network standards that make it challenging to synchronize in power management communication. In this paper, we present a multi-layer communication model for SG systems and propose compressed-sensing based data transmission at every layer of the SG system to improve data transmission performance. Our approach is to utilize the compressed-sensing procedure at every layer in a controlled manner. Simulation results demonstrate that the proposed monitoring devices need less transmission power than conventional systems. Additionally, secure, reliable, and real-time data transmission is possible with the compressed-sensing technique.

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References

  1. V. C. Gungor and G. P. Hancke, "Industrial wireless sensor networks: Challenges, design principles and technical approaches," IEEE Trans. Ind. Electron., vol. 56, pp. 4258-4265, 2009. https://doi.org/10.1109/TIE.2009.2015754
  2. Y. Yang, F. Lambert, and D. Divan, "A survey on technologies for implementing sensor networks for power delivery systems," in Proc. IEEE Power Eng. Soc. Gen. Meeting, Jun. 2007, pp. 1-8.
  3. D. Niyato, L. Xiao and P. Wang, "Machine to machine communication for Home energy management system in Smart Grid", IEEE Communications Magazine, vol. 49, pp. 53 - 59, Apr. 2011. https://doi.org/10.1109/MCOM.2011.5741146
  4. Z. Hanzalek and P. Jurcik, "Energy Efficient Scheduling for Cluster-Tree Wireless Sensor Networks With Time-Bounded Data Flows: Application to IEEE 802.15.4/ZigBee", IEEE Transactions on Industrial Informatics, vol. 6, Issue: 3, pp. 438 - 450, 2010. https://doi.org/10.1109/TII.2010.2050144
  5. Z. Hwang, B. Choi and S. Kang, "Enhanced Self-Configuration Scheme for a Robust Zigbee-based Home Automation", IEEE Transactions on Consumer Electronics, vol. 56, Issue: 2, pp. 583 - 590, July 2010. https://doi.org/10.1109/TCE.2010.5505974
  6. L. Jianpo, Z. Xuning, T. Ning and S. Jisheng, "Study on ZigBee Network Architecture and Routing Algorithm", 2nd International Conference on Signal Processing Systems (ICSPS), IEEE, vol. 2, pp. V2-389 - V2-393, Aug 2010.
  7. Y. Zhang, Y. Rong, X. Shengli, Y. Wenqing, X Yang and M Guizani, "Home M2M Networks: Architectures, Standards, and QoS Improvement", Communications Magazine, IEEE, vol. 49 Issue:4, pp. 44-52, Apr. 2011.
  8. M. Balouchestani, K. Raahemifar, and S. Krishnan, "COMPRESSED SENSING IN WIRELESS SENSOR NETWORKS: SURVEY", Canadian Journal on Multimedia and Wireless Networks, Vol. 2, No. 1, Feb. 2011.
  9. Z. Xiaoyan, W. Houjun, D. Zhijian, "Wireless sensor networks based on compressed sensing," in Proc. of 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT)", Vol.9, pp. 90-92, Sept. 2010.
  10. P. Zhang, C. Chen, M. Liu, "The application of compressed sensing in wireless sensor network," in Proc. of International Conference on Wireless Communications & Signal Processing, pp. 1-5, Dec. 2009.
  11. X. Wang, Z. Zhao, Y. Xia, H. Zhang, "Compressed sensing based random routing for multi-hop wireless sensor networks," in Proc. of International Symposium on Communications and Information Technologies (ISCIT), pp. 220-225, Dec. 2010.
  12. H. Li, R. Mao, L. Lai, R. Qiu, "Compressed Meter Reading for Delay-Sensitive and Secure Load Report in Smart Grid," in Proc. of First IEEE International Conference on Smart Grid Communications", pp. 114-119, Nov. 2010.
  13. V. Gungor, D. Sahin, T. Kocak, S. Ergut, C. Buccella, C. Cecati, G. Hancke, "Smart Grid Technologies: Communication technologies and standards," IEEE Transactions on Industrial Informatics, Issue: 99, Sept. 2011.
  14. E. Ayday, S. Rajagopal, Secure, "Intuitive and Low-Cost Device Authentication for Smart Grid Networks," in Proc. of IEEE Consumer Communications and Networking Conference (CCNC), pp. 1161 - 1165, may 2011.
  15. B. Kilbourne, K. Bender, "Spectrum for Smart Grid: Policy Recommendations Enabling Current and Future Applications," in Proc. of First IEEE International Conference on Smart Grid Communications, pp. 578 - 582, Nov. 2010.
  16. N. Ghasemi, S. M. Hosseini, "Comparison of smart grid with cognitive radio: Solutions to spectrum scarcity," in Proc. of the 12th International Conference on Advanced Communication Technology (ICACT), vol. 1, pp. 898-903, Apr. 2010.
  17. K. Sarah, P. Sakshi, S. Noel, S. Caterina, "A simulative analysis of the robustness of Smart Grid communication networks," in Proc. of North American Power Symposium (NAPS), pp. 1-7, Sept. 2011.
  18. H. Khurana, M. Hadley, L. Ning, D. A. Frincke, "Smart-Grid Security Issues," IEEE Security & Privacy, Vol. 8 , Issue:1, pp. 81-85, Feb. 2010. https://doi.org/10.1109/MSP.2010.49
  19. Y. Wang, D. Ruan D. Gu, J. Gao, D. Liu, J. Xu, F. Chen, F. Dai and J. Yang, "Analysis of Smart Grid security standards," in Proc. of 2011 IEEE International Conference on Computer Science and Automation Engineering (CSAE), Vol. 4, pp. 697-701, Jul. 2011.
  20. E. Pallotti, F. Mangiatordi, "Smart Grid Cyber Security Requirements," in Proc. of 10th International Conference on Environment and Electrical Engineering (EEEIC), pp.1-4, June 2011.
  21. D. Donoho, "Compressed sensing", IEEE Trans. on Information Theory, vol. 52, no. 4, pp. 4036-4048, 2006. https://doi.org/10.1109/TIT.2006.880031
  22. E. Cand'es and T. Tao, "Near optimal signal recovery from random projections: Universal encoding strategies-" IEEE Trans. on Information Theory, vol. 52, no. 12, pp. 5406-5425, 2006. https://doi.org/10.1109/TIT.2006.885507
  23. R. Baraniuk, "A lecture on compressive sensing", IEEE Signal Processing Magazine, 24(4):118-121. 2007.
  24. A. Ghassemi, S. Bavarian and L. Lampe, "Cognitive Radio for Smart Grid Communication," in Proc. of First IEEE International Conference on Smart Grid Communications", PP. 297-302, Nov. 2010.
  25. X. Wang, Z. Zhao, Y. Xia, H Zhang, "Compressed sensing based random routing for multi-hop wireless sensor networks," in Proc. of International Symposium on Communications and Information Technologies (ISCIT), pp.220-225, Dec. 2010.
  26. H. Zaixing, T. Ogawa, M. Haseyama, "The simplest measurement matrix for compressed sensing of natural images," in Proc. of 17th IEEE International Conference on Image Processing (ICIP), pp. 4301-4304, Dec. 2010.
  27. S. S. Chen, D. L. Donoho and M. A. Saunders, "Atomic Decomposition by Basis Pursuit," SIAM Journal on Scientific- Computing, vol. 20, p. 33, 1998. https://doi.org/10.1137/S1064827596304010
  28. A. C. Gilbert, M. J. Slrauss, J. A. Tropp, etc., "Algorithmic linear dimension reduction in the norm for sparse vectors," in Proc. of 44th Annual Allerton Conference on Communication, Control, and Computing, 2006.
  29. R. Nef, A. Zakhor, "Very low bit rate video coding based on matching pursuits," IEEE Transactions on Circuits and Systerns for Video Technology, 7(1): 158-171, 1997. https://doi.org/10.1109/76.554427
  30. J. A. Tropp and A. C. Gilbert, "Signal recovery from partial information by orthogonal matching pursuit," IEEE Trans. Inform. Theory, vol 53, pp. 4655-4666, 2007. https://doi.org/10.1109/TIT.2007.909108

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