Abstract
In-network data aggregation is considered an effective technique for conserving energy communication in wireless sensor networks. It consists in eliminating the inherent redundancy in raw data collected from the sensor nodes. Prior works on data aggregation protocols have focused on the measurement data redundancy. In this paper, our goal in addition of reducing measures redundancy is to identify near duplicate nodes that generate similar data sets. We consider a tree based bi-level periodic data aggregation approach implemented on the source node and on the aggregator levels. We investigate the problem of finding all pairs of nodes generating similar data sets such that similarity between each pair of sets is above a threshold t. We propose a new frequency filtering approach and several optimizations using sets similarity functions to solve this problem. To evaluate the performance of the proposed filtering method, experiments on real sensor data have been conducted. The obtained results show that our approach offers significant data reduction by eliminating in network redundancy and outperforms existing filtering techniques.
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References
Yu, B., Li, J., Li, Y.: Distributed data aggregation scheduling in wireless sensor networks. In: IEEE INFOCOM 2009 (2009)
Zheng, Y., Chen, K., Qiu, W.: Building representative-based data aggregation tree in wireless sensor networks. Mathematical Problems in Engineering, 11 pages (2010)
Bahi, J., Makhoul, A., Medlej, M.: Data aggregation for periodic sensor networks using sets similarity functions. In: IWCMC 2011, 7th IEEE Int. Wireless Communications and Mobile Computing Conference, pp. 559–564 (July 2011)
Sharaf, M.A., Beaver, J., Labrinidis, A., Chrysanthis, P.K.: Tina: A scheme for temporal coherency-aware in-network aggregation. In: 3rd ACM International Workshop on Data Engineering for Wireless and Mobile Access, pp. 69–76 (2003)
Xu, Y., Lee, W.-C., Xu, J., Mitchell, G.: Processing window queries in wireless sensor networks. In: 22nd Int. Conf. on Data Engineering, ICDE, p. 70 (2006)
Madden, S., Franklin, M.J., Hellerstein, J.M., Hong, W.: Tag: A tiny aggregation service for ad-hoc sensor networks. SIGOPS Oper. Syst. Rev. 36(SI), 131–146 (2002)
Cormode, G., Garofalakis, M., Muthukrishnan, S., Rastogi, R.: Holistic aggregates in a networked world: Distributed tracking of approximate quantiles. In: 2005 ACM SIGMOD International Conference on Management of Data, pp. 25–36 (2005)
Cormode, G., Garofalakis, M., Muthukrishnan, S., Rastogi, R.: Prolonging the lifetime of wireless sensor networks via unequal clustering. In: Proceedings of the 5th International Workshop on Algorithms for Wireless, Mobile, Ad Hoc and Sensor Networks (2005)
Lee, S., Chung, T.: Data Aggregation for Wireless Sensor Networks Using Self-organizing Map. In: Kim, T.G. (ed.) AIS 2004. LNCS (LNAI), vol. 3397, pp. 508–517. Springer, Heidelberg (2005)
Chen, H., Mineno, H., Mizuno, T.: Adaptive data aggregation scheme in clustered wireless sensor networks. Computer Communications 31(15), 3579–3585 (2009)
Shah, R.C., Rabaey, J.M.: Energy aware routing for low energy ad hoc sensor networks. In: IEEE Wireless Communications and Networking Conf. WCNC, pp. 350–355 (2002)
Younis, O., Fahmy, S.: An experimental study of routing and data aggregation in sensor networks. In: IEEE International Conference on Mobile Adhoc and Sensor Systems Conference, 8 pages (2005)
Prakash, G.L., Thejaswini, M., Manjula, S.H., Venugopal, K.R., Patnaik, L.M.: Tree-on-dag for data aggregation in sensor networks. World Academy of Science, Engineering and Technology 37 (2009)
Fan, K.-W., Liu, S., Sinha, P.: Dynamic forwarding over tree-on-dag for scalable data aggregation in sensor networks. IEEE Trans. on Mobile Computing 7(10), 1271–1284 (2008)
Bayardo, R.J., Ma, Y., Srikant, R.: Scaling up all pairs similarity search. In: 16th International Conference on World Wide Web, WWW 2007, pp. 131–140 (2007)
Sarawag, S., Kirpal, A.: Efficient exact set-similarity joins. In: 32nd international Conference on Very large Data Bases, VLDB 2006, pp. 918–929 (2006)
Chaudhuri, S., Ganti, V., Kaushik, R.: A primitive operator for similarity joins in data cleaning. In: 22nd International Conference on Data Engineering (ICDE 2006), p. 5 (2006)
Xiao, C., Wang, W., Lin, X., Yu, J.X.: Efficient similarity joins for near duplicate detection. In: Proceeding of the 17th International Conference on World Wide Web, pp. 131–140. ACM (2008)
Xiao, C., Wang, W., Lin, X., Shang, H.: Top-k set similarity joins. In: Proceedings of the 2009 IEEE International Conference on Data Engineering, pp. 916–927 (2009)
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Madden, S.: http://db.csail.mit.edu/labdata/labdata.html
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Bahi, J.M., Makhoul, A., Medlej, M. (2012). An Optimized In-Network Aggregation Scheme for Data Collection in Periodic Sensor Networks. In: Li, XY., Papavassiliou, S., Ruehrup, S. (eds) Ad-hoc, Mobile, and Wireless Networks. ADHOC-NOW 2012. Lecture Notes in Computer Science, vol 7363. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31638-8_12
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DOI: https://doi.org/10.1007/978-3-642-31638-8_12
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