Abstract
In-network data aggregation is widely recognized as an acceptable means to reduce the amount of transmitted data without adversely affecting the quality of the results. To date, most aggregation protocols assume that data from localized regions is correlated, thus they tend to identify aggregation points within these regions. Our work, instead, targets systems where the data sources are largely independent, and over time, the sink requests different combinations of data sources. The combinations are essentially aggregation functions. This problem is significantly different from the localized one because the functions are initially known only by the sink, and the data sources to be combined may be located in any part of the network, not necessarily near one another. This paper describes MVSink, a protocol that lowers the network cost by incrementally pushing the aggregation function as close to the sources as possible, aggregating early the raw data. Our results show between 20% and 30% savings over a simplistic approach in large networks, and demonstrate that a data request needs to be active only for a reasonably short period of time to overcome the cost of identifying the aggregation tree.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Krishnamachari, B., Estrin, D., Wicker, S.B.: The impact of data aggregation in wireless sensor networks. In: Proceedings of the 22nd International Conference on Distributed Computing Systems (ICDCS), Washington, DC, USA, pp. 575–578. IEEE Computer Society Press, Los Alamitos (2002)
Heinzelman, W., Murphy, A.L., Carvalho, H., Perillo, M.: Middleware to support sensor network applications. IEEE Network Magazine Special Issue (2004)
Gröpl, C., Hougardy, S., Nierhoff, T., Prömel, H.J.: Lower bounds for approximation algorithms for the steiner tree problem. In: Brandstädt, A., Le, V.B. (eds.) WG 2001. LNCS, vol. 2204, pp. 217–228. Springer, Heidelberg (2001)
Nikolopoulos, S.D., Palios, L.: Hole and antihole detection in graphs. In: SODA 2004: Proceedings of the 15th Annual ACM-SIAM Symposium on Discrete Algorithms, Philadelphia, PA, USA, pp. 850–859. Society for Industrial and Applied Mathematics (2004)
Takahashi, H., Matsuyama, A.: An approximate solution for the steiner problem in graphs. Math. Japonica 24, 573–577 (1980)
Distributed Computing Group at ETH-Zürich: Sinalgo - simulator for network algorithms, http://dcg.ethz.ch/projects/sinalgo/
Intanagonwiwat, C., Estrin, D., Govindan, R., Heidemann, J.: Impact of network density on data aggregation in wireless sensor networks (2001)
Kansal, A., Srivastava, M.B.: An environmental energy harvesting framework for sensor networks. In: Proceedings of the International Symposium on Low power Electronics and Design (ISLPED), pp. 481–486. ACM Press, New York (2003)
Ramachandran, U., Kumar, R., Wolenetz, M., Cooper, B., Agarwalla, B., Shin, J., Hutto, P., Paul, A.: Dynamic data fusion for future sensor networks. ACM Transactions on Sensor Networks 2, 404–443 (2006)
Intanagonwiwat, C., Govindan, R., Estrin, D.: Directed diffusion: a scalable and robust communication paradigm for sensor networks. In: Proceedings of the 6th International Conference on Mobile computing and Networking (MobiCom), pp. 56–67. ACM Press, New York (2000)
Ding, M., Cheng, X., Xue, G.: Aggregation tree construction in sensor networks. In: IEEE 58th Vehicular Technology Conference, VTC 2003-Fall, vol. 4, pp. 2168–2172 (2003)
Khan, M., Pandurangan, G., Vullikanti, A.: Distributed algorithms for constructing approximate minimum spanning trees in wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems (2008)
Cheng, H., Liu, Q., Jia, X.: Heuristic algorithms for real-time data aggregation in wireless sensor networks. In: IWCMC 2006: Proceedings of the 2006 International Conference on Wireless Communications and Mobile Computing, pp. 1123–1128. ACM, New York (2006)
Heinzelman, W., Chandrakasan, A., Balakrishnan, H.: An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications 1(4), 660–670 (2002)
Manjeshwar, A., Agrawal, D.P.: TEEN: A routing protocol for enhanced efficiency in wireless sensor networks. In: Proceedings of the 15th International Parallel & Distributed Processing Symposium (IPDPS), Washington, DC, USA, p. 189. IEEE Computer Society Press, Los Alamitos (2001)
Wen, Y.F., Lin, F.Y.S.: Energy-efficient data aggregation routing and duty-cycle scheduling in cluster-based sensor networks. In: 4th IEEE Consumer Communications and Networking Conference, 2007, CCNC 2007, pp. 95–99 (2007)
Lindsey, S., Raghavendra, C.S.: Pegasis: Power-efficient gathering in sensor information systems. In: Aerospace Conference Proceedings, 2002, vol. 3, pp. 1125–1130. IEEE, Los Alamitos (2002)
Gao, J., Guibas, L., Milosavljevic, N., Hershberger, J.: Sparse data aggregation in sensor networks. In: IPSN 2007: Proceedings of the 6th International Conference on Information Processing in Sensor Networks, pp. 430–439. ACM, New York (2007)
Fan, K.W., Liu, S., Sinha, P.: Structure-free data aggregation in sensor networks. IEEE Transactions on Mobile Computing 6, 929–942 (2007)
Zhu, J., Papavassiliou, S., Yang, J.: Adaptive localized qos-constrained data aggregation and processing in distributed sensor networks. IEEE Transactions on Parallel and Distributed Systems 17, 923–933 (2006)
Chan, H., Perrig, A., Song, D.: Secure hierarchical in-network aggregation in sensor networks. In: Proceedings of the 13th ACM Conference on Computer and Communications security (CCS), pp. 278–287. ACM Press, New York (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fernandes, L.L., Murphy, A.L. (2009). MVSink: Incrementally Building In-Network Aggregation Trees. In: Roedig, U., Sreenan, C.J. (eds) Wireless Sensor Networks. EWSN 2009. Lecture Notes in Computer Science, vol 5432. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00224-3_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-00224-3_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-00223-6
Online ISBN: 978-3-642-00224-3
eBook Packages: Computer ScienceComputer Science (R0)