Abstract
Declarative queries are proving to be an attractive paradigm for interacting with networks of wireless sensors. The metaphor that “the sensornet is a database” is problematic, however, because sensors do not exhaustively represent the data in the real world. In order to map the raw sensor readings onto physical reality, a model of that reality is required to complement the readings. In this article, we enrich interactive sensor querying with statistical modeling techniques. We demonstrate that such models can help provide answers that are both more meaningful, and, by introducing approximations with probabilistic confidences, significantly more efficient to compute in both time and energy. Utilizing the combination of a model and live data acquisition raises the challenging optimization problem of selecting the best sensor readings to acquire, balancing the increase in the confidence of our answer against the communication and data acquisition costs in the network. We describe an exponential time algorithm for finding the optimal solution to this optimization problem, and a polynomial-time heuristic for identifying solutions that perform well in practice. We evaluate our approach on several real-world sensor-network datasets, taking into account the real measured data and communication quality, demonstrating that our model-based approach provides a high-fidelity representation of the real phenomena and leads to significant performance gains versus traditional data acquisition techniques.
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References
SenSys: Call for Papers. http://www.cis.ohio-state.edu/sensys04/ (2004)
IPSN: Call for Papers. http://ipsn04.cs.uiuc.edu/call_for_papers.html (2004)
Madden, S., Franklin, M.J., Hellerstein, J.M., Hong, W.: The design of an acquisitional query processor for sensor networks. In: ACM SIGMOD (2003)
Yao, Y., Gehrke, J.: Query processing in sensor networks. In: Conference on Innovative Data Systems Research (CIDR) (2003)
Kollios, G., Considine, J., Li, F., Byers, J.: Approximate aggregation techniques for sensor databases. In: ICDE (2004)
Mitchell, T.: Machine Learning. McGraw-Hill, New York (1997)
Crossbow, Inc.: Wireless sensor networks. http://www.xbow.com/Products/Wireless_Sensor_Networks.htm
Madden, S., Hong, W., Hellerstein, J.M., Franklin, M.: TinyDB web page. http://telegraph.cs.berkeley.edu/tinydb
Madden, S.: The design and evaluation of a query processing architecture for sensor networks. Master’s thesis, UC Berkeley (2003)
TAOS, Inc.: Tsl2550 ambient light sensor. Technical report. http://www.taosinc.com/pdf/tsl2550-E39.pdf (2002)
Intersema: Ms5534a barometer module. Technical report. http://www.intersema.com/pro/module/file/da5534.pdf (2002)
Sensirion: Sht11/15 relative humidity sensor. Technical report. http://www.sensirion.com/en/pdf/Datasheet_SHT1x_SHT7x_0206.pdf (2002)
Intanagonwiwat, C., Govindan, R., Estrin, D.: Directed diffusion: A scalable and robust communication paradigm for sensor networks. In: MobiCOM, Boston, MA (2000)
Pottie, G., Kaiser, W.: Wireless integrated network sensors. Commun. ACM 43(5), 51–58 (2000)
Polastre, J.: Design and implementation of wireless sensor networks for habitat monitoring. Master’s thesis, UC Berkeley (2003)
Lin, S., Kernighan, B.: An effective heuristic algorithm for the tsp. Oper. Res. 21, 498–516 (1971)
Bahar, R., Frohm, E., Gaona, C., Hachtel, G., Macii, E., Pardo, A., Somenzi, F.: Algebraic decision diagrams and their applications. In: IEEE Internation Conference on Computer-Aided Design, pp. 188–191 (1993)
Deshpande, A., Guestrin, C., Madden, S., Hong, W.: Exploiting correlated attributes in acquisitional query processing. In: ICDE (2005)
Olston, C., Widom, J.: Best effort cache sychronization with source cooperation. In: SIGMOD (2002)
Sharaf, A., Beaver, J., Labrinidis, A., Chrysanthis, K.: Balancing energy efficiency and quality of aggregate data in sensor networks. VLDB J. 13(4), 384–403 (2004)
Deshpande, A., Garofalakis, M., Rastogi, R.: Independence is good: Dependency-based histogram synopses for high-dimensional data. In: SIGMOD (2001)
Friedman, N.: Learning belief networks in the presence of missing values and hidden variables. In: Proceedings of the 14th International Conference on Machine Learning, pp. 125–133 (1997)
Ganguly, S.: Design and analysis of parametric query optimization algorithms. In: VLDB’98, Proceedings of 24rd International Conference on Very Large Data Bases (1998)
Getoor, L., Taskar, B., Koller, D.: Selectivity estimation using probabilistic models. In: SIGMOD (2001)
Gibbons, P.B.: Distinct sampling for highly-accurate answers to distinct values queries and event reports. In: Proceedings of Conference on Very Large Data Bases (2001)
Desphande, A., Guestrin, C., Madden, S., Hellerstein, J.M., Hong, W.: Model-driven data acquisition in sensor networks. In: Proceedings of Conference on Very Large Data Bases (2004)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, San Mateo, CA (1988)
Cowell, R., Dawid, P., Lauritzen, S., Spiegelhalter, D.: Probabilistic Networks and Expert Systems. Springer, New York (1999)
Paskin, M.A., Guestrin, C.E.: Robust probabilistic inference in distributed systems. In: UAI, the 20th International Conference on Uncertainty in Artificial Intelligence (2004)
Heckerman, D.: A tutorial on learning with bayesian networks, Microsoft, MSR-TR-95-06, March (1995)
Lerner, U., Moses, B., Scott, M., McIlraith, S., Koller, D.: Monitoring a complex physical system using a hybrid dynamic bayes net. In: Proceedings of UAI (2002)
Boyen, X., Koller, D.: Tractable inference for complex stochastic processes. In: Proceedings of UAI (1998)
Dean, T., Kanazawa, K.: A model for reasoning about persistence and causation. Comput. Intell. 5(3), 142–150 (1989)
Pearl, J.: Causality : Models, Reasoning, and Inference. Cambridge University Press, Cambridge (2000)
Bernardo, J., Smith, A.: Bayesian Theory. Wiley, New York (1994)
Bellman, R.E.: Dynamic Programming. Princeton, Princeton, NJ (1957)
Puterman, M.L.: Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, New York (1994)
Boutilier, C., Dearden, R., Goldszmidt, M.: Exploiting structure in policy construction. In: Proceedings of IJCAI, pp. 1104–1111 (1995)
Guestrin, C.E., Koller, D., Parr, R.: Multiagent planning with factored MDPs. In: 14th Neural Information Processing Systems (NIPS-14), Vancouver, Canada, pp. 1523–1530 (2001)
Hellerstein, J., Hong, W., Madden, S., Stanek, K.: Beyond average: Towards sophisticated sensing with queries. In: Proceedings of the First Workshop on Information Processing in Sensor Networks (IPSN) (2003)
Guestrin, C., Bodik, P., Thibaux, R., Paskin, M., Madden, S.: Distributed regression: An efficient framework for modeling sensor network data. In: Proceedings of Information Processing in Sensor Networks (IPSN) (2004)
Gibbons, P.B., Matias, Y.: New sampling-based summary statistics for improving approximate query answers. In: SIGMOD (1998)
Acharya, S., Gibbons, P., Poosala, V., Ramaswamy, S.: Join synopses for approximate query answering, SIGMOD’99. In: Proceedings of the 1999 ACM SIGMOD International Conference on Management of Data, 275–286. ACM Press, New York, NY, USA, Philadelphia, Pennsylvania, United States (1999) ISBN: 1-58113-084-8, DOI: (http://doi.acm.org/10.1145/304182.304207)
Nath, S., Gibbons, P., Seshan, S., Anderson, Z.: Synopsis diffusion for robust aggregation in sensor networks. In: Proceedings of SenSys (2004)
Olston, C., Loo, B.T., Widom, J.: Adaptive precision setting for cached approximate values. In: ACM SIGMOD (2001)
Cheng, R., Kalashnikov, D.V., Prabhakar, S.: Evaluating probabilistic queries over imprecise data. In: SIGMOD (2003)
Chu, M., Haussecker, H., Zhao, F.: Scalable information-driven sensor querying and routing for ad hoc heterogeneous networks. Int. J. High Perform. Comput. Appl. 16(2), 293–313 (2002)
Hellerstein, J.M., Haas, P.J., Wang, H.: Online aggregation. In: SIGMOD, Tucson, AZ, pp. 171–182 (1997)
Hellerstein, J.M., Avnur, R., Chou, A., Hidber, C., Olston, C., Raman, V., Roth, T., Haas, P.J.: Interactive data analysis with CONTROL. IEEE Comput. 32(8) (1999)
Ioannidis, Y.E., Ng, R.T., Shim, K., Sellis, T.K.: Parametric query optimization. In: Proceedings of the 18th International Conference on Very Large Data Bases (1992)
Graefe, G., Ward, K.: Dynamic query evaluation plans. In: SIGMOD (1989)
Cole, R., Graefe, G.: Optimization of dynamic query evaluation plans. In: SIGMOD (1994)
Hellerstein, J.M., Franklin, M.J., Chandrasekaran, S., Deshpande, A., Hildrum, K., Madden, S., Raman, V., Shah, M.: Adaptive query processing: Technology in evolution. IEEE Data Eng. Bull. 23(2), 7–18 (2000)
Babu, S., Motwani, R., Munagala, K., Nishizawa, I., Widom, J.: Adaptive ordering of pipelined stream filters. In: SIGMOD (2004)
Shivakumar, N., Garcia-Molina, H., Chekuri, C.: Filtering with approximate predicates. In: Proceedings of Conference on Very Large Data Bases (1998)
Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice Hall, Englewood Cliffs, NJ (1994)
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This article includes and extends results that were previously published in VLDB 2004 [Desphande, A., Guestrin, C., Madden, S., Hellerstein, J.M., Hong, W.: Model-driven data acquisition in sensor networks. In {VLDB} (2004)], and combines these techniques with the conditional planning approach published in ICDE 2005 [Deshpande, A., Guestrin, C., Madden, S., Hong, W.: Exploiting correlated attributes in acquisitional query processing. In {ICDE} (2005)].
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Deshpande, A., Guestrin, C., Madden, S.R. et al. Model-based approximate querying in sensor networks. The VLDB Journal 14, 417–443 (2005). https://doi.org/10.1007/s00778-005-0159-3
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DOI: https://doi.org/10.1007/s00778-005-0159-3