SNEE: a query processor for wireless sensor networks | Distributed and Parallel Databases Skip to main content

Advertisement

Springer Nature Link
Log in

SNEE: a query processor for wireless sensor networks

  • Published:
Distributed and Parallel Databases Aims and scope Submit manuscript

Abstract

A wireless sensor network (WSN) can be construed as an intelligent, large-scale device for observing and measuring properties of the physical world. In recent years, the database research community has championed the view that if we construe a WSN as a database (i.e., if a significant aspect of its intelligent behavior is that it can execute declaratively-expressed queries), then one can achieve a significant reduction in the cost of engineering the software that implements a data collection program for the WSN while still achieving, through query optimization, very favorable cost:benefit ratios. This paper describes a query processing framework for WSNs that meets many desiderata associated with the view of WSN as databases. The framework is presented in the form of compiler/optimizer, called SNEE, for a continuous declarative query language over sensed data streams, called SNEEql. SNEEql can be shown to meet the expressiveness requirements of a large class of applications. SNEE can be shown to generate effective and efficient query evaluation plans. More specifically, the paper describes the following contributions: (1) a user-level syntax and physical algebra for SNEEql, an expressive continuous query language over WSNs; (2) example concrete algorithms for physical algebraic operators defined in such a way that the task of deriving memory, time and energy analytical cost-estimation models (CEMs) for them becomes straightforward by reduction to a structural traversal of the pseudocode; (3) CEMs for the concrete algorithms alluded to; (4) an architecture for the optimization of SNEEql queries, called SNEE, building on well-established distributed query processing components where possible, but making enhancements or refinements where necessary to accommodate the WSN context; (5) algorithms that instantiate the components in the SNEE architecture, thereby supporting integrated query planning that includes routing, placement and timing; and (6) an empirical performance evaluation of the resulting framework.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Abadi, D.J., Carney, D., Çetintemel, U., Cherniack, M., Convey, C., Lee, S., Stonebraker, M., Tatbul, N., Zdonik, S.B.: Aurora: a new model and architecture for data stream management. VLDB J. 12(2), 120–139 (2003)

    Article  Google Scholar 

  2. Arasu, A., Babcock, B., Babu, S., Datar, M., Ito, K., Motwani, R., Nishizawa, I., Srivastava, U., Thomas, D., Varma, R., Widom, J.: STREAM: the Stanford stream data manager. IEEE Data Eng. Bull. 26(1), 19–26 (2003)

    Google Scholar 

  3. Arasu, A., Babcock, B., Babu, S., Datar, M., Ito, K., Nishizawa, I., Rosenstein, J., Widom, J.: STREAM: the Stanford stream data manager. In: SIGMOD Conference, p. 665 (2003)

    Google Scholar 

  4. Arasu, A., Babu, S., Widom, J.: The CQL continuous query language: semantic foundations and query execution. VLDB J. 15(2), 121–142 (2006)

    Article  Google Scholar 

  5. Beckwith, R., Teibel, D., Bowen, P.: Report from the field: results from an agricultural wireless sensor network. In: LCN, pp. 471–478 (2004)

    Google Scholar 

  6. Bonfils, B.J., Bonnet, P.: Adaptive and decentralized operator placement for in-network query processing. In: IPSN, pp. 47–62 (2003)

    Google Scholar 

  7. Bonnet, P., Gehrke, J., Seshadri, P.: Towards sensor database systems. In: Mobile Data Management, pp. 3–14 (2001)

    Chapter  Google Scholar 

  8. Brayner, A., Lopes, A., Meira, D., Vasconcelos, R., Menezes, R.: An adaptive in-network aggregation operator for query processing in wireless sensor networks. J. Syst. Softw. 81(3), 328–342 (2008)

    Article  Google Scholar 

  9. Brenninkmeijer, C.Y.: Querying sensor networks: requirements, semantics, algorithms and cost models. PhD thesis, School of Computer Science, University of Manchester (2010)

  10. Brenninkmeijer, C.Y.A., Galpin, I., Fernandes, A.A.A., Paton, N.W.: A semantics for a query language over sensors, streams and relations. In: BNCOD, pp. 87–99. Springer, Berlin (2008)

    Google Scholar 

  11. Brenninkmeijer, C.Y.A., Galpin, I., Fernandes, A.A.A., Paton, N.W.: Validated cost models for sensor network queries. In: DMSN. ACM International Conference Proceeding Series (2009)

    Google Scholar 

  12. Burrell, J., Brooke, T., Beckwith, R.: Vineyard computing: sensor networks in agricultural production. IEEE Pervasive Comput., January–March, pp. 38–45 (2004)

  13. Chandrasekaran, S., Cooper, O., Deshpande, A., Franklin, M.J., Hellerstein, J.M., Hong, W., Krishnamurthy, S., Madden, S., Raman, V., Reiss, F., Shah, M.A.: TelegraphCQ: continuous dataflow processing for an uncertain world. In: CIDR (2003)

    Google Scholar 

  14. Chandrasekaran, S., Cooper, O., Deshpande, A., Franklin, M.J., Hellerstein, J.M., Hong, W., Krishnamurthy, S., Madden, S., Reiss, F., Shah, M.A.: TelegraphCQ: continuous dataflow processing. In: SIGMOD Conference, p. 668 (2003)

    Google Scholar 

  15. Chaudhuri, S.: An overview of query optimization in relational systems. In: PODS, pp. 34–43 (1998)

    Google Scholar 

  16. Chen, J., DeWitt, D.J., Naughton, J.F.: Design and evaluation of alternative selection placement strategies in optimizing continuous queries. In: ICDE, pp. 345–356 (2002)

    Google Scholar 

  17. Chu, D., Deshpande, A., Hellerstein, J.M., Hong, W.: Approximate data collection in sensor networks using probabilistic models. In: ICDE, p. 48 (2006)

    Google Scholar 

  18. Chu, D., Tavakoli, A., Popa, L., Hellerstein, J.M.: Entirely declarative sensor network systems. In: VLDB, pp. 1203–1206 (2006)

    Google Scholar 

  19. Deshpande, A., Guestrin, C., Madden, S., Hellerstein, J.M., Hong, W.: Model-based approximate querying in sensor networks. VLDB J. 14(4), 417–443 (2005)

    Article  Google Scholar 

  20. Fernandes, A.A.A., Galpin, I., Gray, A.J.G., Paton, N.W.: An approach to network resilience for SNEE query evaluation in SemSorGrid4Env. Technical report, School of Computer Science, University of Manchester (2009)

  21. Galpin, I., Brenninkmeijer, C.Y.A., Jabeen, F., Fernandes, A.A.A., Paton, N.W.: An architecture for query optimization in sensor networks. In: ICDE, pp. 1439–1441 (2008)

    Google Scholar 

  22. Galpin, I., Brenninkmeijer, C.Y.A., Jabeen, F., Fernandes, A.A.A., Paton, N.W.: Comprehensive optimization of declarative sensor network queries. In: SSDBM, pp. 339–360 (2009)

    Google Scholar 

  23. Ganeriwal, S., Kumar, R., Srivastava, M.B.: Timing-sync protocol for sensor networks. In: SenSys, pp. 138–149 (2003)

    Google Scholar 

  24. Ganesan, D., Mathur, G., Shenoy, P.J.: Rethinking data management for storage-centric sensor networks. In: CIDR, pp. 22–31 (2007)

    Google Scholar 

  25. Garcia-Molina, H., Ullman, J.D., Widom, J.: Database Systems Implementation. Prentice Hall, New York (2000)

    Google Scholar 

  26. Garofalakis, M.N., Ioannidis, Y.E.: Parallel query scheduling and optimization with time- and space-shared resources. In: VLDB, pp. 296–305 (1997)

    Google Scholar 

  27. Gay, D., Levis, P., von Behren, J.R., Welsh, M., Brewer, E.A., Culler, D.E.: The nesC language: a holistic approach to networked embedded systems. In: PLDI, pp. 1–11 (2003)

    Google Scholar 

  28. Gehrke, J., Madden, S.: Query processing in sensor networks. In: IEEE Pervasive Computing, vol. 3. IEEE Comput. Soc., Los Alamitos (2004)

    Google Scholar 

  29. Golab, L., Tamer Özsu, M.: Issues in data stream management. SIGMOD Rec. 32(2), 5–14 (2003)

    Article  Google Scholar 

  30. Gounaris, A., Sakellariou, R., Paton, N.W., Fernandes, A.A.A.: A novel approach to resource scheduling for parallel query processing on computational grids. Distrib. Parallel Databases 19(2–3), 87–106 (2006)

    Article  Google Scholar 

  31. Govindan, R., Hellerstein, J.M., Hong, W., Madden, S., Franklin, M., Shenker, S.: The sensor network as a database, 2002. Available at CiteSeerX

  32. Graefe, G.: Encapsulation of parallelism in the volcano query processing system. In: SIGMOD Conference, pp. 102–111 (1990)

    Google Scholar 

  33. Hammad, M.A., Mokbel, M.F., Ali, M.H., Aref, W.G., Catlin, A.C., Elmagarmid, A.K., Eltabakh, M.Y., Elfeky, M.G., Ghanem, T.M., Gwadera, R., Ilyas, I.F., Marzouk, M.S., Xiong, X.: Nile: a query processing engine for data streams. In: ICDE, p. 851 (2004)

    Google Scholar 

  34. Hart, J.K., Martinez, K.: Environmental sensor networks: a revolution in the earth system science? Earth-Sci. Rev. 78, 177–191 (2006)

    Article  Google Scholar 

  35. Hill, J., Szewczyk, R.., Woo, A., Hollar, S., Culler, D.E., Pister, K.S.J.: System architecture directions for networked sensors. In: ASPLOS, pp. 93–104 (2000)

    Google Scholar 

  36. Ilarri, S., Mena, E., Illarramendi, A.: Using cooperative mobile agents to monitor distributed and dynamic environments. Inf. Sci. 178(9), 2105–2127 (2008)

    Article  Google Scholar 

  37. Jain, N., Mishra, S., Srinivasan, A., Gehrke, J., Widom, J., Balakrishnan, H., Çetintemel, U., Cherniack, M., Tibbetts, R., Zdonik, S.B.: Towards a streaming SQL standard. Proc. Very Large Data Bases 1(2), 1379–1390 (2008)

    Google Scholar 

  38. Karl, H., Willig, A.: Protocols and Architectures for Wireless Sensor Networks. Wiley, New York (2005)

    Book  Google Scholar 

  39. Karl, H., Willig, A., Wolisz, A. (eds.): Wireless Sensor Networks, First European Workshop, EWSN 2004, Berlin, Germany, January 19–21, 2004, Proceedings. Lecture Notes in Computer Science, vol. 2920. Springer, Berlin (2004)

    Chapter  Google Scholar 

  40. Kossmann, D.: The state of the art in distributed query processing. ACM Comput. Surv. 32(4), 422–469 (2000)

    Article  Google Scholar 

  41. Levis, P., Madden, S., Polastre, J., Woo, A., Szewczykand, R., Whitehouse, K., Gay, D., Hill, J., Welsh, M., Brewer, E., Culler, D.: TinyOS: an operating system for sensor networks. In: Ambient Intelligence, pp. 115–148. Springer, Berlin (2005)

    Chapter  Google Scholar 

  42. Madden, S., Franklin, M.J., Hellerstein, J.M., Hong, W.: TAG: a tiny aggregation service for ad-hoc sensor networks. In: OSDI (2002)

    Google Scholar 

  43. Madden, S., Franklin, M.J., Hellerstein, J.M., Hong, W.: The design of an acquisitional query processor for sensor networks. In: SIGMOD Conference, pp. 491–502 (2003)

    Google Scholar 

  44. Madden, S., 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)

    Article  Google Scholar 

  45. Mainwaring, A.M., Culler, D.E., Polastre, J., Szewczyk, R., Anderson, J.: Wireless sensor networks for habitat monitoring. In: WSNA, pp. 88–97 (2002)

    Chapter  Google Scholar 

  46. Manjhi, A., Nath, S., Gibbons, P.B.: Tributaries and deltas: efficient and robust aggregation in sensor network streams. In: SIGMOD Conference, pp. 287–298 (2005)

    Google Scholar 

  47. Marshall, I.W., Price, M.C., Li, H., Boyd, N., Boult, S.: Multi-sensor cross correlation for alarm generation in a deployed sensor network. In: EuroSSC, pp. 286–299 (2007)

    Google Scholar 

  48. Müller, R., Alonso, G., Kossmann, D.: SwissQM: next generation data processing in sensor networks. In: CIDR, pp. 1–9 (2007)

    Google Scholar 

  49. Pottie, G.J., Kaiser, W.J.: Wireless integrated network sensors. Commun. ACM 43(5), 51–58 (2000)

    Article  Google Scholar 

  50. Tork Roth, M., Ozcan, F., Haas, L.M.: Cost models DO matter: providing cost information for diverse data sources in a federated system. In: VLDB, pp. 599–610 (1999)

    Google Scholar 

  51. Rundensteiner, E.A., Ding, L., Sutherland, T.M., Zhu, Y., Pielech, B., Mehta, N.: CAPE: continuous query engine with heterogeneous-grained adaptivity. In: VLDB, pp. 1353–1356 (2004)

    Chapter  Google Scholar 

  52. Sharaf, M.A., Beaver, J., Labrinidis, A., Chrysanthis, P.K.: TiNA: a scheme for temporal coherency-aware in-network aggregation. In: MobiDE, pp. 69–76 (2003)

    Google Scholar 

  53. Smith, J., Gounaris, A., Watson, P., Paton, N.W., Fernandes, A.A.A., Sakellariou, R.: Distributed query processing on the grid. In: GRID, pp. 279–290 (2002)

    Google Scholar 

  54. Stonebraker, M., Aoki, P.M., Litwin, W., Pfeffer, A., Sah, A., Sidell, J., Staelin, C., Mariposa, A.Yu.: A wide-area distributed database system. VLDB J. 5(1), 48–63 (1996)

    Article  Google Scholar 

  55. Szewczyk, R., Mainwaring, A.M., Polastre, J., Anderson, J., Culler, D.E.: An analysis of a large scale habitat monitoring application. In: SynSys, pp. 214–226 (2004)

    Chapter  Google Scholar 

  56. Szewczyk, R., Osterweil, E., Polastre, J., Hamilton, M., Mainwaring, A.M., Estrin, D.: Habitat monitoring with sensor networks. Commun. ACM 47(6), 34–40 (2004)

    Article  Google Scholar 

  57. Titzer, B., Lee, D.K., Palsberg, J.: Avrora: scalable sensor network simulation with precise timing. In: IPSN, pp. 477–482 (2005)

    Google Scholar 

  58. Trigoni, N., Yao, Y., Demers, A.J., Gehrke, J., Rajaraman, R.: Multi-query optimization for sensor networks. In: DCOSS, pp. 307–321 (2005)

    Google Scholar 

  59. Trigoni, N., Yao, Y., Demers, A.J., Gehrke, J., Rajaraman, R.: Wave scheduling and routing in sensor networks. Trans. Sens. Netw. 3(1), 2 (2007)

    Article  Google Scholar 

  60. Tulone, D., Madden, S.: PAQ: time series forecasting for approximate query answering in sensor networks. In: EWSN, pp. 21–37 (2006)

    Google Scholar 

  61. Viglas, S., Naughton, J.F.: Rate-based query optimization for streaming information sources. In: SIGMOD Conference, pp. 37–48 (2002)

    Google Scholar 

  62. Yao, Y., Gehrke, J.: Query processing in sensor networks. In: CIDR (2003)

    Google Scholar 

  63. Yu, H., Lim, E.-P., Zhang, J.: On in-network synopsis join processing for sensor networks. In: MDM, p. 32 (2006)

    Google Scholar 

  64. Zadorozhny, V.I., Chrysanthis, P.K., Krishnamurthy, P.: A framework for extending the synergy between query optimization and mac layer in sensor networks. In: Proceedings of the 1st International Workshop on Data Management for Sensor Networks, DMSN ’04, pp. 68–77. ACM, New York (2004)

    Google Scholar 

  65. Zhang, P., Sadler, C.M., Lyon, S.A., Martonosi, M.: Hardware design experiences in ZebraNet. In: SynSys, pp. 227–238 (2004)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK

    Ixent Galpin, Christian Y. A. Brenninkmeijer, Alasdair J. G. Gray, Farhana Jabeen, Alvaro A. A. Fernandes & Norman W. Paton

Authors
  1. Ixent Galpin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Christian Y. A. Brenninkmeijer
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alasdair J. G. Gray
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Farhana Jabeen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Alvaro A. A. Fernandes
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Norman W. Paton
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ixent Galpin.

Additional information

Communicated by Erik Buchmann.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Galpin, I., Brenninkmeijer, C.Y.A., Gray, A.J.G. et al. SNEE: a query processor for wireless sensor networks. Distrib Parallel Databases 29, 31–85 (2011). https://doi.org/10.1007/s10619-010-7074-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10619-010-7074-3

Keywords

Search

Navigation