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
Apache Hadoop, http://hadoop.apache.org
Apache, HBase, http://hbase.apache.org
Uschold, M., Gruninger, M.: Ontologies: Principles, Methods and Applications. Knowledge Engineering Review 11(2), 93–155 (1996)
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)
Dean, J., Ghemawat, S.: MapReduce: Simplified Data Processing on Large Clusters. Communications of the ACM 51(1), 107–113 (2008)
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)
Yu, B., Bailey, T.: Processing Partially Specified Queries over High-Dimensional Databases. Data & Knowledge Engineering 62(1), 177–197 (2007)
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)
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)
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
Hacigumus, H., Iyer, B., Mehrotra, S.: Providing Database as a Service. In: Proceedings of IEEE ICDE Int. Conf., pp. 29–38 (2002)
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)
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)
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)
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)
Yao, Y., Gehrke, J.E.: The Cougar Approach to In-Network Query Processing in Sensor Networks. SIGMOD Record 31(3), 9–18 (2002)
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)
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)
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)
Cuzzocrea, A.: Intelligent Techniques for Warehousing and Mining Sensor Network Data. IGI Global (2009)
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)
Cuzzocrea, A., Chakravarthy, S.: Event-Based Lossy Compression for Effective and Efficient OLAP over Data Streams. Data & Knowledge Enginering 69(7), 678–708 (2010)
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)
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)
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)
UDC Projects, http://informatics.udc.edu/projects.php
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
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