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QoS-aware optimization of sensor network queries

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Abstract

The resource-constrained nature of mote-level wireless sensor networks (WSNs) poses challenges for the design of a general-purpose sensor network query processors (SNQPs). Existing SNQPs tend to generate query execution plans (QEPs) that are selected on the basis of a fixed, implicit expectation, for example, that energy consumption should be kept as small as possible. However, in WSN applications, the same query may be subject to several, possibly conflicting, quality-of-service (QoS) expectations concomitantly (for example maximizing data acquisition rates subject to keeping energy consumption low). It is also not uncommon for the QoS expectations to change over the lifetime of a deployment (for example from low to high data acquisition rates). This paper describes optimization algorithms that respond to stated QoS expectations (about acquisition rate, delivery time, energy consumption and lifetime) when making routing, placement, and timing decisions for in-WSN query processing. The paper shows experimentally that QoS-awareness offers significant benefits in responding to, and reconciling, diverse QoS expectations, thereby enabling QoS-aware SNQPs to generate efficient QEPs for a broader range WSN applications than has hitherto been possible.

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Notes

  1. Due to space limitations, we must refer the reader to our previous papers [11, 12, 17] for more details on SNEEql.

  2. In our work, we have built the code generator to emit source code in the nesC [19] language for use in WSNs that support TinyOS [2]. However, our contributions do not depend on this choice: replacing the code generator would allow the same benefits to be reaped for another language/OS pair.

  3. We refer the reader to [17] for details on the algebra into which SNEEql is compiled.

  4. Note that, once operator instances have been assigned to sites by NOMADm, those that are co-located are merged into a single operator instance.

  5. NOMADm implements the class of Mesh-Adaptive Direct Search (MADS) algorithms for solving derivative-free nonlinear optimization problems with expensive functions and allowing for categorical constraints. MADS searches in a set of directions that becomes dense in the limit. It thus achieves superior convergence properties. Our use of MADS, however, is not motivated so much by its effectiveness and efficiency. Instead, its attractiveness stems from the fact that the characteristics of the optimization problems that the compiler generates, that is, derivative-free and involving categorical constraints, are handled by MADS with good theoretical guarantees (for which, we refer the reader to [7]).

  6. It is a requirement of the code generation step for the inequality \(\alpha (\beta -1)+\pi <\alpha \beta \) to be satisfied.

  7. For each operator (and fragment) in the DAF, there is a fixed cost \(F\), associated with the invocation of the operator itself, and a variable cost \(V\), which is proportional to \(\beta \). QoSA-SNEE where-scheduling uses a version of the CEMs that emit expressions in which the fixed and variable elements are decomposed. These are denoted as Memory \(_\beta \)(t), Time \(_\beta \)(t) and Energy \(_\beta \)(t).

  8. The agenda in Fig. 16c has one fragment less than the other agendas because no instances of the iterative AGGR_MERGE operator have been created during where-scheduling.

  9. Note, however, that as one would expect, although QoS 2,3 both favour QEPs that are similar in nature (that is agendas with a low buffering factor, and RTs with as few hops as possible), given that \(\delta =\alpha (\beta -1)+\pi \), when \(\beta =1\), there is no correlation between \(\alpha \) and \(\delta \) variables, as demonstrated by the use of two different values of \(\alpha \) in QoS 2,3 having exactly the same value for \(\delta \).

  10. For example, the number of lines of nesC code generated by QoSA-SNEE for the query 3 agendas in Fig. 16, is, respectively, 10189, 12232, 17285, excluding libraries provided by TinyOS. Even assuming that an expert human programmer might reduce the count by a substantial percentage, these figures suggest a significant amount of specialized development for this.

References

  1. Supplementary material. http://vldb.org/vldb_journal/index.php/published-articles

  2. TinyOS website. http://www.tinyos.net/ (2012)

  3. Abramson, M.A., Audet, C., Couture, G., Dennis, J.E. Jr., Le Digabel, S., Tribes, C.: The NOMAD project. Software available at http://www.gerad.ca/nomad

  4. Andreou, P., Zeinalipour-Yazti, D., Pamboris, A., Chrysanthis, P.K., Samaras, G.: Optimized query routing trees for wireless sensor networks. Inf. Syst. 36(2), 267–291 (2011)

    Article  Google Scholar 

  5. 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 

  6. Astrahan, M.M., Blasgen, M.W., Chamberlin, D.D., Gray, J.N., King, W.F., Lindsay, B.G., Lorie, R.A., Mehl, J.W., Price, T.G., Putzolu, G.R., Schkolnick, M., Selinger, P.P., Slutz, D.R., Strong, H.R., Tiberio, P., Traiger, I.L., Wade, B.W., Yost R.A.: System R: a relational data base management system. Computer 12, 42–48. ISSN 0018–9162 (1979)

    Google Scholar 

  7. Audet, C., Dennis J.E. Jr.: Mesh adaptive direct search algorithms for constrained optimization. SIAM J. Optim. 17(1), 188–217 (2006)

    Google Scholar 

  8. Balke, W-T., Güntzer, U.: Multi-objective query processing for database systems. In: VLDB, pp. 936–947 (2004)

  9. Boyd, S., Kim, S., Vandenberghe, L., Hassibi, A.: A tutorial on geometric programming. Optim. Eng. 8(1), 67–127 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  10. Brayner, A., Lopes, A., Meira, D., Vasconcelos, R., Menezes, R.: Toward adaptive query processing in wireless sensor networks. Signal Process.87(12), 2911–2933 (2007)

    Google Scholar 

  11. 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 (2008)

  12. Brenninkmeijer, C.Y.A., Galpin, I., Fernandes, A.A.A., Paton, N.W.: Validated cost models for sensor network queries. In: DMSN (2009)

  13. Carney, D., Çetintemel, U., Cherniack, M., Convey, C., Lee, S., Seidman, G., Stonebraker, M., Tatbul, N., Zdonik, S.B.: Monitoring streams—a new class of data management applications. In: VLDB, pp. 215–226 (2002)

  14. Dalvi, N.N., Sanghai, S.K., Roy, P., Sudarshan, S.: Pipelining in multi-query optimization. In: PODS (2001)

  15. Deligiannakis, A., Kotidis, Y., Stoumpos, V., Delis, A.: Collection trees for event-monitoring queries. Inf. Syst. 36(2), 386–405 (2011)

    Article  Google Scholar 

  16. Galpin, I.: Quality of service aware optimization of sensor network queries. PhD thesis, University of Manchester (2010)

  17. Galpin, I., Brenninkmeijer, C.Y.A., Gray, A.J.G., Jabeen, F., Fernandes, A.A.A., Paton, N.W.: SNEE: a query processor for wireless sensor networks. Distrib. Parallel Databases 29(1–2), 31–85 (2011)

  18. Garcia-Molina, H., Ullman, J.D., Widom, J.: Database Systems Implementation. Prentice Hall, Upper Saddle River (2000)

    Google Scholar 

  19. 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)

  20. Gehrke, J., Madden, S.: Query processing in sensor networks. In: IEEE Pervasive Computing, vol. 3. IEEE Computer Society (2004)

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

  22. Grant, M., Boyd, S.: CVX: Matlab software for disciplined convex programming, version 1.22. http://www.stanford.edu/boyd/cvx/ (2012)

  23. 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 

  24. Ioannidis, Y.E., Kang, Y.C.: Randomized algorithms for optimizing large join queries. In: SIGMOD Conference, pp. 312–321 (1990)

  25. Karl, H., Willig, A.: Protocols and Architectures for Wireless Sensor Networks. Wiley, New York. ISBN 0-470-09510-5 (2005)

  26. Klan, D., Karnstedt, M., Hose, K., Ribe-Baumann, L., Sattler, K.: Stream engines meet wireless sensor networks: cost-based planning and processing of complex queries in AnduIN. Distrib. Parallel Databases 29(1–2), 151–183 (2011)

    Article  Google Scholar 

  27. Le Digabel, S.: Algorithm 909: NOMAD: nonlinear optimization with the MADS algorithm. ACM Trans. Math. Softw. 37(4), 1–15 (2011)

    Article  Google Scholar 

  28. Lédeczi, Á., Nádas, A., Völgyesi, P., Balogh, G., Kusy, B., Sallai, J., Pap, G., Dóra, S., Molnár, K., Maróti, M., Simon, G.: Countersniper system for urban warfare. TOSN 1(2), 153–177 (2005)

    Article  Google Scholar 

  29. Li, W., Batra, V.S., Raman, V., Han, W., Narang, I.: QoS-based data access and placement for federated information systems. In: VLDB, pp. 1358–1362 (2005)

  30. Li, W., Gao, D., Bhatti, R., Narang, I., Matsuzawa, H., Numao, M., Ohkawa, M., Fukuda, T.: Deadline and QoS aware data warehouse. In: VLDB, pp. 1418–1421 (2007)

  31. Lin, S., Arai, B., Gunopulos, D., Das, G.: Region sampling: continuous adaptive sampling on sensor networks. In: ICDE, pp. 794–803 (2008)

  32. 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 

  33. 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)

  34. Martinez, K., Ong, R., Hart, J.: Glacsweb: a sensor network for hostile environments. IEEE SECON (2004)

  35. Papadimitriou, C.H., Yannakakis, M.: Multiobjective query optimization. In: PODS (2001)

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

    Article  Google Scholar 

  37. Rappaport, T.S.: Wireless Communications Principles and Practice, 2nd edn. Prentice Hall, Upper Saddle River (2002)

    Google Scholar 

  38. Schroeder, B., Harchol-Balter, M., Iyengar, A., Nahum, E.M.: Achieving class-based QoS for transactional workloads. In: ICDE, p. 153 (2006)

  39. Sharplesa, J.J., McRaeb, R.H.D., Webera, R.O., Gill, A.M.: A simple index for assessing fire danger rating. Environ. Model. Softw. 24(6), 764–774 (2009)

    Article  Google Scholar 

  40. Swami, A.N.: Optimization of large join queries: combining heuristic and combinatorial techniques. In: SIGMOD Conference, pp. 367–376 (1989)

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

  42. Tatbul, N., Çetintemel, U., Zdonik, S.B., Cherniack, M., Stonebraker, M.: Load shedding in a data stream manager. In: VLDB, pp. 309–320 (2003)

  43. Tatbul, N., Çetintemel, U., Zdonik S.B.: Staying FIT: efficient load shedding techniques for distributed stream processing. In: VLDB, pp. 159–170 (2007)

  44. Thiele, M., Bader, A., Lehner, W.: Multi-objective scheduling for real-time data warehouses. Comput. Sci. Res. Dev. 24, 137–151 (2009)

    Article  Google Scholar 

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

  46. Werner-Allen, G., Lorincz, K., Johnson, J., Lees, J., Welsh, M.: Fidelity and yield in a volcano monitoring sensor network. In: OSDI, pp. 381–396 (2006)

  47. Xing, Y., Hwang, J., Çetintemel, U., Zdonik, S.B.: Providing resiliency to load variations in distributed stream processing. In: VLDB, pp. 775–786 (2006)

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

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Acknowledgments

We thank A. J. Gray and C. Y. A. Brenninkmeijer for their comments and their work on FG-SNEE.

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

    Ixent Galpin, Alvaro A. A. Fernandes & Norman W. Paton

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  1. Ixent Galpin
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Correspondence to Ixent Galpin.

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Funded by the EC 7th Framework Programme and the UK EPSRC WINES Programme.

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Galpin, I., Fernandes, A.A.A. & Paton, N.W. QoS-aware optimization of sensor network queries. The VLDB Journal 22, 495–517 (2013). https://doi.org/10.1007/s00778-012-0300-z

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