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Issue No.11 - November (2010 vol.21)
pp: 1675-1691
Yao-Chung Fan , National Tsing Hua University, Hsinchu
Arbee L.P. Chen , National Chengchi University, Taipei
Sensor networks have received considerable attention in recent years, and are often employed in the applications where data are difficult or expensive to collect. In these applications, in addition to individual sensor readings, statistical aggregates such as Min and Count over the readings of a group of sensor nodes are often needed. To conserve resources for sensor nodes, in-network strategies are adopted to process the aggregates. One primitive in-network aggregation strategy is the tree-based aggregation, where the aggregates are computed from leaves to the root of a spanning tree over a sensor network. However, a shortcoming with the tree-based aggregation is that it is not robust against communication failures, which are common in sensor networks. One of the solutions to overcome this shortcoming is to enable multipath routing, by which each node broadcasts its reading or a partial aggregate to multiple neighbors. However, multipath routing-based aggregation typically suffers from the problem of overcounting sensor readings. In this study, we propose two schemes based on the linear counting technique to deal with the overcounting problem. These two schemes process aggregates by statically and dynamically, respectively, allocating space for the use of the linear counting technique. Both schemes provide the same accuracy guarantee but involve different communication costs. Through extensive experiments with real-world and synthetic data, we demonstrate the efficiency and effectiveness of using these two schemes as solutions for processing aggregates in a sensor network. The experiments also show that the scheme that dynamically allocates the space often outperforms the other one in terms of energy conservation since it requires less space to satisfy an accuracy constraint.
Wireless sensor networks, query processing, distributed data structures, reliability and robustness.
Yao-Chung Fan, Arbee L.P. Chen, "Efficient and Robust Schemes for Sensor Data Aggregation Based on Linear Counting", IEEE Transactions on Parallel & Distributed Systems, vol.21, no. 11, pp. 1675-1691, November 2010, doi:10.1109/TPDS.2010.33
[1] A. Arora et al., "ExScal: Elements of an Extreme Scale Wireless Sensor Network," Proc. IEEE Conf. Real-Time and Embedded Computing Systems and Applications, pp. 102-108, 2005.
[2] S. Boyd, A. Ghosh, B. Prabhakar, and D. Shah, "Gossip Algorithm: Design, Analysis, Applications," Proc. IEEE Conf. Computer Comm., pp. 1653-1664, 2005.
[3] M. Charikar, S. Chaudhuri, R. Motwani, and V.R. Narasayya, "Towards Estimation Error Guarantees for Distinct Values," Proc. ACM Symp. Principles of Database Systems, pp. 268-279, 2000.
[4] G. Cormode, S. Muthukrishnan, and W. Zhuang, "What's Different: Distributed, Continuous Monitoring of Duplicate-Resilient Aggregates on Data Streams," Proc. IEEE Conf. Data Eng., pp. 57-66, 2006.
[5] G. Corliss, "Which Root Does the Bisection Algorithm Find," SIAM Rev., vol. 19, no. 2, pp. 325-327, 1977.
[6] Intel Lab Data, , 2009.
[7] J.Y. Chen, G. Pandurangan, and D. Xu, "Robust Computation of Aggregates in Wireless Sensor Networks: Distributed Randomized Algorithms and Analysis," IEEE Trans. Parallel and Distributed Systems, vol. 17, no. 9, pp. 987-1000, Sept. 2006.
[8] J. Considine, F. Li, G. Kollios, and J. Byers, "Approximate Aggregation Techniques for Sensor Database," ACM Trans. Database Systems, vol. 34, no. 1, pp. 1-35, 2009.
[9] P. Flajolet and G.N. Martin, "Probabilistic Counting Algorithms for Database Applications," J. Computer and System Science, vol. 31, pp. 182-209, 1985.
[10] Y.C. Fan and A.L.P. Chen, "Efficient and Robust Sensor Data Aggregation Using Linear Counting Sketches," Proc. IEEE Symp. Parallel and Distributed Processing, pp. 1-12, 2008.
[11] S. Ganguly, M.N. Garofalakis, and R. Rastogi, "Tracking Set-Expression Cardinalities over Continuous Update Streams," VLDB J., vol. 13, pp. 354-369, 2004.
[12] O. Gnawali et al., "The Tenet Architecture for Tiered Sensor Networks," Proc. ACM Conf. Embedded Networked Sensor System, pp. 153-166, 2006.
[13] A. Manjhi, S. Nath, and P.B. Gibbons, "Tributaries and Deltas: Efficient and Robust Aggregation in Sensor Network Streams," Proc. ACM SIGMOD, pp. 287-298, 2005.
[14] S. Madden, M.J. Franklin, and J.M. Hellerstein, and W. Hong, "TAG: A Tiny Aggregation Service for Ad-Hoc Sensor Networks," Proc. Symp. Operating System Design and Implementation, pp. 131-146, 2002.
[15] S. Madden, M.J. Franklin, and J.M. Hellerstein, and W. Wang, "TinyDB: An Acquisitional Query Processing System for Sensor Networks," ACM Trans. Database Systems, vol. 30, no. 1, pp. 122-173, 2005.
[16] S. Nath, P.B. Gibbons, S. Seshan, and Z.R. Anderson, "Synopsis Diffusion for Robust Aggregation in Sensor Networks," ACM Trans. Sensor Networks, vol. 4, no. 2, pp. 1-40, 2008.
[17] A. Pavan and S. Tirthapura, "Range-Efficient Computation of ${\rm F}_0$ over Massive Data Streams," Proc. IEEE Conf. Data Eng., pp. 32-43, 2005.
[18] N. Shrivastava, C. Buragohain, D. Agrawal, and S. Suri, "Medians and Beyond: New Aggregation Techniques for Sensor Networks," Proc. ACM Conf. Embedded Networked Sensor Systems, pp. 239-249, 2004.
[19] F. Stann and J. Heidemann, "RMST: Reliable Data Transport in Sensor Networks," Proc. IEEE Workshop Sensor Net Protocols and Applications, pp. 102-112, 2003.
[20] V. Shnayder et al., "Simulating the Power Consumption of Large-Scale Sensor Network Applications," Proc. ACM Conf. Embedded Networked Sensor Systems, pp. 188-200, 2004.
[21] Y. Yao and J. Gehrke, "The Cougar Approach to In-Network Query Processing in Sensor Networks," ACM SIGMOD Record, vol. 31, no. 3, pp. 9-18, 2003.
[22] K.Y. Whang, B.T. Vander-Zanden, and H.M. Taylor, "A Linear Time Probabilistic Counting Algorithm for Database Applications," ACM Trans. Database Systems, vol. 15, no. 2, pp. 208-229, 1990.
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