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A Bigtable/MapReduce-Based Cloud Infrastructure for Effectively and Efficiently Managing Large-Scale Sensor Networks

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 7450)

Abstract

This paper proposes a novel approach for effectively and efficiently managing large-scale sensor networks defining a Cloud infrastructure that makes use of Bigtable at the data layer and MapReduce at the processing layer. We provide principles and architecture of our proposed infrastructure along with its experimental evaluation on a real-life computational platform. Experiments clearly confirm the effectiveness and the efficiency of the proposed research.

Keywords

  • Bigtable
  • MapReduce
  • Cloud computing
  • Large-scale sensor networks

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References

  1. Apache Hadoop, http://hadoop.apache.org

  2. Apache, HBase, http://hbase.apache.org

  3. Uschold, M., Gruninger, M.: Ontologies: Principles, Methods and Applications. Knowledge Engineering Review 11(2), 93–155 (1996)

    CrossRef  Google Scholar 

  4. Chang, F., Dean, J., Ghemawat, S., Hsieh, W.C., Wallach, D.A., Burrows, M., Chandra, T., Fikes, A., Gruber, R.E.: Bigtable: A Distributed Storage System for Structured Data. ACM Transactions on Computer Systems 26(2), Art. 4 (2008)

    Google Scholar 

  5. Dean, J., Ghemawat, S.: MapReduce: Simplified Data Processing on Large Clusters. Communications of the ACM 51(1), 107–113 (2008)

    CrossRef  Google Scholar 

  6. Yu, B.: A Spatiotemporal Uncertainty Model of Degree 1.5 for Continuously Changing Data Objects. In: Proceedings of ACM SAC Int. Conf., pp. 1150–1155 (2006)

    Google Scholar 

  7. Yu, B., Bailey, T.: Processing Partially Specified Queries over High-Dimensional Databases. Data & Knowledge Engineering 62(1), 177–197 (2007)

    CrossRef  Google Scholar 

  8. Yu, B., Kim, S.H.: Interpolating and Using Most Likely Trajectories in Moving-Objects Databases. In: Bressan, S., Küng, J., Wagner, R. (eds.) DEXA 2006. LNCS, vol. 4080, pp. 718–727. Springer, Heidelberg (2006)

    CrossRef  Google Scholar 

  9. Yu, B., Kim, S.H., Alkobaisi, S., Bae, W.D., Bailey, T.: The Tornado Model: Uncertainty Model for Continuously Changing Data. In: Kotagiri, R., Radha Krishna, P., Mohania, M., Nantajeewarawat, E. (eds.) DASFAA 2007. LNCS, vol. 4443, pp. 624–636. Springer, Heidelberg (2007)

    CrossRef  Google Scholar 

  10. Yu, B., Sen, R., Jeong, D.H.: An Integrated Framework for Managing Sensor Data Uncertainty using Cloud Computing. Information Systems (2012), doi:10.1126/ j.is.2011.12.003

    Google Scholar 

  11. Hacigumus, H., Iyer, B., Mehrotra, S.: Providing Database as a Service. In: Proceedings of IEEE ICDE Int. Conf., pp. 29–38 (2002)

    Google Scholar 

  12. Agrawal, D., Das, D., El Abbadi, A.: Big Data and Cloud Computing: Current State and Future Opportunities. In: Proceedings of EDBT Int. Conf., pp. 530–533 (2011)

    Google Scholar 

  13. Balazinska, M., Deshpande, A., Franklin, M.J., Gibbons, P.B., Gray, J., Hansen, M.H., Liebhold, M., Nath, S., Szalay, A.S., Tao, V.: Data Management in the Worldwide Sensor Web. IEEE Pervasive Computing 6(2), 30–40 (2007)

    CrossRef  Google Scholar 

  14. Diao, Y., Ganesan, D., Mathur, G., Shenoy, P.J.: Rethinking Data Management for Storage-centric Sensor Networks. In: Proceedings of CIDR Int. Conf., pp. 22–31 (2007)

    Google Scholar 

  15. Li, M., Ganesan, D., Shenoy, P.J.: PRESTO: Deedback-Driven Data Management in Sensor Networks. IEEE/ACM Transactions on Networking 17(4), 1256–1269 (2009)

    CrossRef  Google Scholar 

  16. Yao, Y., Gehrke, J.E.: The Cougar Approach to In-Network Query Processing in Sensor Networks. SIGMOD Record 31(3), 9–18 (2002)

    CrossRef  Google Scholar 

  17. Madden, S., Franklin, M., Hellerstein, J., Hong, W.: TinyDB: An Acqusitional Query Processing System for Sensor Networks. ACM Transactions on Database Systems 30(1), 122–173 (2005)

    CrossRef  Google Scholar 

  18. Deshpande, A., Guestrin, C., Madden, S., Hellerstein, J.M., Hong, W.: Model-Driven Data Acquisition in Sensor Networks. In: Proceedings of VLDB Int. Conf., pp. 588–599 (2004)

    Google Scholar 

  19. Ganesan, D., Greenstein, B., Perelyubskiy, D., Estrin, D., Heidemann, J., Govindan, R.: Multi-Resolution Storage in Sensor Networks. ACM Tranasctions on Storage 1(3), 277–315 (2005)

    CrossRef  Google Scholar 

  20. Cuzzocrea, A.: Intelligent Techniques for Warehousing and Mining Sensor Network Data. IGI Global (2009)

    Google Scholar 

  21. Cuzzocrea, A., Furfaro, F., Mazzeo, G.M., Saccà, D.: A Grid Framework for Approximate Aggregate Query Answering on Summarized Sensor Network Readings. In: Proceedings of GADA Int. Conf., pp. 144–153 (2004)

    Google Scholar 

  22. Cuzzocrea, A., Chakravarthy, S.: Event-Based Lossy Compression for Effective and Efficient OLAP over Data Streams. Data & Knowledge Enginering 69(7), 678–708 (2010)

    CrossRef  Google Scholar 

  23. Gray, J., Chaudhuri, S., Bosworth, A., Layman, A., Reichart, D., Venkatrao, M., Pellow, F., Pirahesh, H.: Data Cube: A Relational Aggregation Operator Generalizing Group-by, Cross-Tab, and Sub Totals. Data Mining and Knowledge Discovery 1(1), 29–53 (1997)

    CrossRef  Google Scholar 

  24. Cuzzocrea, A.: CAMS: OLAPing Multidimensional Data Streams Efficiently. In: Pedersen, T.B., Mohania, M.K., Tjoa, A.M. (eds.) DaWaK 2009. LNCS, vol. 5691, pp. 48–62. Springer, Heidelberg (2009)

    CrossRef  Google Scholar 

  25. Cuzzocrea, A.: Retrieving Accurate Estimates to OLAP Queries over Uncertain and Imprecise Multidimensional Data Streams. In: Bayard Cushing, J., French, J., Bowers, S. (eds.) SSDBM 2011. LNCS, vol. 6809, pp. 575–576. Springer, Heidelberg (2011)

    CrossRef  Google Scholar 

  26. UDC Projects, http://informatics.udc.edu/projects.php

  27. Apache ZooKeeper, http://zookeeper.apache.org/

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Yu, B., Cuzzocrea, A., Jeong, D., Maydebura, S. (2012). A Bigtable/MapReduce-Based Cloud Infrastructure for Effectively and Efficiently Managing Large-Scale Sensor Networks. In: Hameurlain, A., Hussain, F.K., Morvan, F., Tjoa, A.M. (eds) Data Management in Cloud, Grid and P2P Systems. Globe 2012. Lecture Notes in Computer Science, vol 7450. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32344-7_3

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  • DOI: https://doi.org/10.1007/978-3-642-32344-7_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32343-0

  • Online ISBN: 978-3-642-32344-7

  • eBook Packages: Computer ScienceComputer Science (R0)