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ABSTRACT

Existing energy-efficient approaches to in-network aggregation in sensor networks can be classified into two categories, tree-based and multi-path-based, with each having unique strengths and weaknesses. In this paper, we introduce Tributary-Delta, a novel approach that combines the advantages of the tree and multi-path approaches by running them simultaneously in different regions of the network. We present schemes for adjusting the regions in response to changes in network conditions, and show how many useful aggregates can be readily computed within this new framework. We then show how a difficult aggregate for this context---finding frequent items---can be efficiently computed within the framework. To this end, we devise the first algorithm for frequent items (and for quantiles) that provably minimizes the worst case total communication for non-regular trees. In addition, we give a multi-path algorithm for frequent items that is considerably more accurate than previous approaches. These algorithms form the basis for our efficient Tributary-Delta frequent items algorithm. Through extensive simulation with real-world and synthetic data, we show the significant advantages of our techniques. For example, in computing Count under realistic loss rates, our techniques reduce answer error by up to a factor of 3 compared to any previous technique.

REFERENCES

• 1: Atmel AVR Microcontroller Datasheet. http://www.atmel.com/dyn/resources/prod-documents/2467s.pdf.
• 2: Brian Babcock , Shivnath Babu , Mayur Datar , Rajeev Motwani , Jennifer Widom, Models and issues in data stream systems, Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, June 03-05, 2002, Madison, Wisconsin [doi>10.1145/543613.543615]
• 3: Ziv Bar-Yossef , T. S. Jayram , Ravi Kumar , D. Sivakumar , Luca Trevisan, Counting Distinct Elements in a Data Stream, Proceedings of the 6th International Workshop on Randomization and Approximation Techniques, p.1-10, September 13-15, 2002
• 4: Mayank Bawa , Aristides Gionis , Hector Garcia-Molina , Rajeev Motwani, The price of validity in dynamic networks, Proceedings of the 2004 ACM SIGMOD international conference on Management of data, June 13-18, 2004, Paris, France [doi>10.1145/1007568.1007627]
• 5: Jeffrey Considine , Feifei Li , George Kollios , John Byers, Approximate Aggregation Techniques for Sensor Databases, Proceedings of the 20th International Conference on Data Engineering, p.449, March 30-April 02, 2004
• 6: A. Deshpande, C. Guestrin, S. Madden, J. M. Hellerstein, and W. Hong. Model-driven data acquisition in sensor networks. In VLDB, 2004.
• 7: Philippe Flajolet , G. Nigel Martin, Probabilistic counting algorithms for data base applications, Journal of Computer and System Sciences, v.31 n.2, p.182-209, Sept. 1985 [doi>10.1016/0022-0000(85)90041-8]
• 8: Michael B. Greenwald , Sanjeev Khanna, Power-conserving computation of order-statistics over sensor networks, Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, June 14-16, 2004, Paris, France [doi>10.1145/1055558.1055597]
• 9: Intel Lab Data. http://berkeley.intel-research.net/labdata/.
• 10: Samuel Madden , Michael J. Franklin , Joseph M. Hellerstein , Wei Hong, TAG: a Tiny AGgregation service for Ad-Hoc sensor networks, Proceedings of the 5th symposium on Operating systems design and implementation

  Due to copyright restrictions we are not able to make the PDFs for this conference available for downloading

  , December 09-11, 2002, Boston, Massachusetts [doi>10.1145/1060289.1060303]
• 11: Samuel Madden , Michael J. Franklin , Joseph M. Hellerstein , Wei Hong, The design of an acquisitional query processor for sensor networks, Proceedings of the 2003 ACM SIGMOD international conference on Management of data, June 09-12, 2003, San Diego, California [doi>10.1145/872757.872817]
• 12: A. Manjhi, S. Nath, and P. B. Gibbons. Tributaries and deltas: Efficient and robust aggregation in sensor network streams. Technical Report IRP-TR-05-01, Intel Research Pittsburgh, PA, March 2005.
• 13: Amit Manjhi , Vladislav Shkapenyuk , Kedar Dhamdhere , Christopher Olston, Finding (Recently) Frequent Items in Distributed Data Streams, Proceedings of the 21st International Conference on Data Engineering (ICDE'05), p.767-778, April 05-08, 2005 [doi>10.1109/ICDE.2005.68]
• 14: G. Manku and R. Motwani. Approximate frequency counts over data streams. In VLDB, 2002.
• 15: S. Muthukrishnan. Data streams: Algorithms and applications. Technical report, Rutgers University, Piscataway, NJ, 2003.
• 16: Suman Nath , Phillip B. Gibbons , Srinivasan Seshan , Zachary R. Anderson, Synopsis diffusion for robust aggregation in sensor networks, Proceedings of the 2nd international conference on Embedded networked sensor systems, November 03-05, 2004, Baltimore, MD, USA [doi>10.1145/1031495.1031525]
• 17: Christopher R. Palmer , Phillip B. Gibbons , Christos Faloutsos, ANF: a fast and scalable tool for data mining in massive graphs, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, July 23-26, 2002, Edmonton, Alberta, Canada [doi>10.1145/775047.775059]
• 18: Victor Shnayder , Mark Hempstead , Bor-rong Chen , Geoff Werner Allen , Matt Welsh, Simulating the power consumption of large-scale sensor network applications, Proceedings of the 2nd international conference on Embedded networked sensor systems, November 03-05, 2004, Baltimore, MD, USA [doi>10.1145/1031495.1031518]
• 19: Nisheeth Shrivastava , Chiranjeeb Buragohain , Divyakant Agrawal , Subhash Suri, Medians and beyond: new aggregation techniques for sensor networks, Proceedings of the 2nd international conference on Embedded networked sensor systems, November 03-05, 2004, Baltimore, MD, USA [doi>10.1145/1031495.1031524]
• 20: Gyula Simon , Miklós Maróti , Ákos Lédeczi , György Balogh , Branislav Kusy , András Nádas , Gábor Pap , János Sallai , Ken Frampton, Sensor network-based countersniper system, Proceedings of the 2nd international conference on Embedded networked sensor systems, November 03-05, 2004, Baltimore, MD, USA [doi>10.1145/1031495.1031497]
• 21: Yufei Tao , George Kollios , Jeffrey Considine , Feifei Li , Dimitris Papadias, Spatio-Temporal Aggregation Using Sketches, Proceedings of the 20th International Conference on Data Engineering, p.214, March 30-April 02, 2004
• 22: Y. Yao and J. Gehrke. Query processing in sensor networks. In CIDR, 2003.
• 23: Jerry Zhao , Ramesh Govindan, Understanding packet delivery performance in dense wireless sensor networks, Proceedings of the 1st international conference on Embedded networked sensor systems, November 05-07, 2003, Los Angeles, California, USA [doi>10.1145/958491.958493]
• 24: J. Zhao, R. Govindan, and D. Estrin. Computing aggregates for monitoring wireless sensor networks. In SPNA, 2003.