Abstract
Efficient data collection in wireless sensor networks (SNs) plays a key role in power conservation. It has spurred a number of research projects focusing on effective algorithms that reduce power consumption with effective in-network aggregation techniques. Up to now, most approaches are based on the assumption that data collection involves all nodes of a network. There is a large number of queries that in fact select only a subset of the nodes in a SN. Thus, we concentrate on selective queries, i.e., queries that request data from a subset of a SN. The task of optimal data collection in such queries is an instance of the NP-hard minimal Steiner tree problem. We argue that selective queries are an important class of queries that can benefit from algorithms that are tailored for partial node participation of a SN. We present an algorithm, called Pocket Driven Trajectories (PDT), that optimizes the data collection paths by approximating the global minimal Steiner tree using solely local spatial knowledge. We identify a number of spatial factors that play an important role for efficient data collection, such as the distribution of participating nodes over the network, the location and dispersion of the data clusters, the location of the sink issuing a query, as well as the location and size of communication holes. In a series of experiments, we compare performance of well-known algorithms for aggregate query processing against the PDT algorithm in partial node participation scenarios. To measure the efficiency of all algorithms, we also compute a near-optimal solution, the globally approximated minimal Steiner tree. We outline future research directions for selective queries with varying node participation levels, in particular scenarios in which node participation is the result of changing physical phenomena as well as reconfigurations of the SN itself.
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References
Madden, S.R., Franklin, M.J., Hellerstein, J.M., Hong, W.: TinyDB: an acquisitional query processing system for sensor networks. ACM Trans. Database Syst. 30(1), 122–173 (2005)
Yao, Y., Gehrke, J.: Query processing for sensor networks. In: Proceedings of the Conference on Innovative Data Systems, pp. 233–244 (2003)
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)
Nath, S., Gibbons, P.B., Seshan, S., Anderson, Z.R.: Synopsis diffusion for robust aggregation in sensor networks. In: Proceedings of SenSys, pp. 250–262 (2004)
Madden, S., Franklin, M.J., Hellerstein, J.M., Hong, W.: The design of an acquisitional query processor for sensor networks. In: Proceedings of SIGMOD, pp. 491–502 (2003)
Oliveira, C.A.S., Pardalos, P.M.: A survey of combinatorial optimization problems in multicast routing. Comput. Oper. Res. 32(8), 1953–1981 (2005)
Kou, L., Markowsky, G., Berman, L.: A fast algorithm for Steiner trees. Acta Informatica 15, 141–145 (1981)
Takahashi, H., Matsuyama, A.: An approximate solution for the Steiner problem in graphs. Math Japonica 24, 573–577 (1980)
Silberstein, A., Braynard, R., Yang, J.: Constraint chaining: on energy-efficient continuous monitoring in sensor networks. In: Proceedings of SIGMOD, pp. 157–168 (2006)
Chou, J., Petrovic, D., Ramachandran, K.: A distributed and adaptive signal processing approach to reducing energy consumption in sensor networks. In: Proceedings of INFOCOM, vol. 2, pp. 1054–1062 (2003)
Bonfils, B.J., Bonnet, P.: Adaptive and Decentralized Operator Placement for In-Network Query Processing. In: Zhao, F., Guibas, L.J. (eds.) IPSN 2003. LNCS, vol. 2634, pp. 47–62. Springer, Heidelberg (2003)
Bawa, M., Gionis, A., Garcia-Molina, H., Motwani, R.: The price of validity in dynamic networks. In: Proceedings of the SIGMOD, pp. 515–526 (2004)
Considine, J., Li, F., Kollios, G., Byers, J.: Approximate aggregation techniques for sensor databases. In: Proceedings of ICDE, pp. 449–460 (2004)
Cardei, M., Wu, J.: Energy-efficient coverage problems in wireless ad hoc sensor networks. Computer Communications 29(4), 413–420 (2006)
Manjhi, A., Nath, S., Gibbons, P.B.: Tributaries and deltas: efficient and robust aggregation in sensor network streams. In: Proceedings of SIGMOD, pp. 287–298 (2005)
Chu, D., Deshpande, A., Hellerstein, J., Hong, W.: Approximate data collection in sensor networks using probabilistic models. In: Proceedings of ICDE, p. 48 (2006)
Pattem, S., Krishnamachari, B., Govindan, R.: The impact of spatial correlation on routing with compression in wireless sensor networks. In: Proceedings of IPSN, pp. 28–35 (2004)
Xu, Y., Heidemann, J., Estrin, D.: Geography-informed energy conservation for ad hoc routing. In: Proceedings of MobiCom, pp. 70–84 (2001)
Yoon, S., Shahabi, C.: Exploiting spatial correlation towards an energy efficient clustered aggregation technique (CAG). In: Proceedings of the ICC, pp. 82–98 (2005)
Gupta, H., Navda, V., Das, S.R., Chowdhary, V.: Efficient gathering of correlated data in sensor networks. In: Proceedings of MobiHoc, pp. 402–413 (2005)
Krishnamachari, B., Estrin, D., Wicker, S.B.: The impact of data aggregation in wireless sensor networks. In: Proceedings of ICDCSW, pp. 575–578 (2002)
Robins, G., Zelikovsky, A.: Improved Steiner tree approximation in graphs. In: Proceedings of SODA, pp. 770–779 (2000)
Doar, M., Leslie, I.M.: How bad is naive multicast routing? In: Proceedings of INFOCOM, pp. 82–89 (1993)
NS-2: The network simulator NS-2 documentation, http://www.isi.edu/nsnam/ns/ns-documentation.html
Yu, Y., Govindan, R., Estrin, D.: Geographical and energy aware routing: A recursive data dissemination protocol for wireless sensor networks. Technical Report TR-01-0023, University of California, Los Angeles, Computer Science Department (2001)
Somasundara, A.A., Jea, D.D., Estrin, D., Srivastava, M.B.: Controllably mobile infrastructure for low energy embedded networks. IEEE Transactions on Mobile Computing 5(8), 958–973 (2006)
Wang, G., Cao, G., Porta, T.F.L.: Movement-assisted sensor deployment. IEEE Transactions on Mobile Computing 5(6), 640–652 (2006)
Hull, B., Bychkovsky, V., Zhang, Y., Chen, K., Goraczko, M., Miu, A., Shih, E., Balakrishnan, H., Madden, S.: CarTel: a distributed mobile sensor computing system. In: Proceedings of SenSys, pp. 125–138 (2006)
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Kulik, L., Tanin, E., Umer, M. (2008). Efficient Data Collection and Selective Queries in Sensor Networks. In: Nittel, S., Labrinidis, A., Stefanidis, A. (eds) GeoSensor Networks. GSN 2006. Lecture Notes in Computer Science, vol 4540. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79996-2_3
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DOI: https://doi.org/10.1007/978-3-540-79996-2_3
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