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Power efficiency through tuple ranking in wireless sensor network monitoring

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Abstract

In this paper, we present an innovative framework for efficiently monitoring Wireless Sensor Networks (WSNs). Our framework, coined KSpot, utilizes a novel top-k query processing algorithm we developed, in conjunction with the concept of in-network views, in order to minimize the cost of query execution. For ease of exposition, consider a set of sensors acquiring data from their environment at a given time instance. The generated information can conceptually be thought as a horizontally fragmented base relation R. Furthermore, the results to a user-defined query Q, registered at some sink point, can conceptually be thought as a view V. Maintaining consistency between V and R is very expensive in terms of communication and energy. Thus, KSpot focuses on a subset V′(⊆V) that unveils only the k highest-ranked answers at the sink, for some user defined parameter k.

To illustrate the efficiency of our framework, we have implemented a real system in nesC, which combines the traditional advantages of declarative acquisition frameworks, like TinyDB, with the ideas presented in this work. Extensive real-world testing and experimentation with traces from UC-Berkeley, the University of Washington and Intel Research Berkeley, show that KSpot provides an up to 66% of energy savings compared to TinyDB, minimizes both the size and number of packets transmitted over the network (up to 77%), and prolongs the longevity of a WSN deployment to new scales.

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References

  1. Agrawal, D., Ganesan, D., Sitaraman, R.K., Diao, Y., Singh, S.: Lazy-adaptive tree: an optimized index structure for flash devices. Proc. VLDB Endow. 2(1), 361–372 (2009)

    Google Scholar 

  2. Andreou, P., Zeinalipour-Yazti, D., Chrysanthis, P.K., Samaras, G.: Workload-aware query routing trees in wireless sensor networks. In: Proceedings of the 9th International Conference on Mobile Data Management (MDM’08), Beijing, China, April 27–30, pp. 189–196 (2008)

    Chapter  Google Scholar 

  3. Andreou, P., Zeinalipour-Yazti, D., Vassiliadou, M., Chrysanthis, P.K., Samaras, G.: KSpot: effectively monitoring the k most important events in a wireless sensor network. In: Proceedings of the 25th International Conference on Data Engineering (ICDE’09), Shanghai, China, May 29–April 4, pp. 1503–1506 (2009)

    Chapter  Google Scholar 

  4. Balke, W.-T., Nejdl, W., Siberski, W., Thaden, U.: Progressive distributed top-k retrieval in peer-to-peer networks. In: Proceedings of the 21st International Conference on Data Engineering (ICDE’05), Tokyo, Japan, April 5–8, pp. 174–185 (2005)

    Chapter  Google Scholar 

  5. Babcock, B., Olston, C.: Distributed top-k monitoring. In: Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data (SIGMOD’03), San Diego, California, USA, June 9–12, pp. 28–39 (2003)

    Chapter  Google Scholar 

  6. Benenson, Z., Bestehorn, M., Buchmann, E., Freiling, F.C., Jawurek, M.: Query dissemination with predictable reachability and energy usage in sensor networks. In: Proceedings of the 7th International Conference on Ad-hoc, Mobile and Wireless Networks (ADHOC-NOW’08), Sophia-Antipolis, France, September 10–12, pp. 279–292 (2008)

    Google Scholar 

  7. Blakeley, J., Larson, P.A., Tompa, F.W.: Efficiently updating materialized views. In: Proceedings of the 1986 ACM SIGMOD International Conference on Management of Data (SIGMOD’86), Washington, D.C., USA, May 28–30, pp. 61–71 (1986)

    Chapter  Google Scholar 

  8. Bruno, N., Gravano, L., Marian, A.: Evaluating top-k queries over web accessible databases. In: Proceedings of the 18th International Conference on Data Engineering (ICDE’02), San Jose, California, USA, February 26–March 1, pp. 369–382 (2002)

    Chapter  Google Scholar 

  9. Cao, P., Wang, Z.: Efficient top-k query calculation in distributed networks. In: Proceedings of the 23rd Annual ACM Symposium on Principles of Distributed Computing (PODC’04), St. John’s, Newfoundland, Canada, July 25–28, pp. 206–215 (2004)

    Google Scholar 

  10. Cao, Q., Abdelzaher, T., Stankovic, J., He, T.: The LiteOS operating system: towards unix-like abstractions for wireless sensor networks. In: Proceedings of the 7th International Conference on Information Processing in Sensor Networks (IPSN’08), St. Louis, Missouri, USA, April 22–24, pp. 233–244 (2008)

    Chapter  Google Scholar 

  11. Chaudhuri, S., Krishnamurthy, R., Potamianos, S., Shim, K.: Optimizing queries with materialized views. In: Proceedings of the 11th International Conference on Data Engineering (ICDE’95), Taipei, Taiwan, March 6–10, pp. 190–200 (1995)

    Chapter  Google Scholar 

  12. Chaves, L.W.F., Buchmann, E., Hueske, F., Bohm, K.: Towards materialized view selection for distributed databases. In: Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology (EDBT’09), Saint Petersburg, Russia, March 23–26, pp. 1088–1099 (2009)

    Chapter  Google Scholar 

  13. Colby, L.S., Griffin, T., Libkin, L., Mumick, I.S., Trickey, H.: Algorithms for deferred view maintenance. In: Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data (SIGMOD’96), Montreal, Quebec, Canada, June 4–6, pp. 469–480 (1996)

    Chapter  Google Scholar 

  14. Coman, A., Nascimento, M.A.: A distributed algorithm for joins in sensor networks. In: Proceedings of the 19th International Conference on Scientific and Statistical Database (SSDBM ’07), Banff, Canada, July 9–11, p. 27 (2007)

    Chapter  Google Scholar 

  15. Coman, A., Sander, J., Nascimento, M.A.: Adaptive processing of historical spatial range queries in peer-to-peer sensor networks. Distrib. Parallel Databases 222(3), 133–163 (2007)

    Article  MATH  Google Scholar 

  16. Considine, J., Li, F., Kollios, G., Byers, J.: Approximate aggregation techniques for sensor databases. In: Proceedings of the 20th International Conference on Data Engineering (ICDE’04), Boston, MA, USA, March 30–April 2, pp. 449–460 (2004)

    Chapter  Google Scholar 

  17. Crossbow Technology Inc.: http://www.xbow.com/ (2010)

  18. Das, G., Gunopulos, D., Koudas, N., Tsirogiannis, D.: Answering top-k queries using views. In: Proceedings of the 32nd International Conference on Very Large Data Bases (VLDB’06), Seoul, Korea, September 12–15, pp. 451–462 (2006)

    Google Scholar 

  19. Deligiannakis, A., Kotidis, Y., Roussopoulos, N.: Compressing historical information in sensor networks. In: Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data (SIGMOD’04), Paris, France, June 13–18, pp. 527–538 (2004)

    Chapter  Google Scholar 

  20. Deshpande, A., Madden, S.R.: MauveDB: supporting model-based user views in database systems. In: Proceedings of the 2006 ACM SIGMOD International Conference on Management of Data (SIGMOD’06), Chicago, Illinois, USA, June 26–29, pp. 73–84 (2006)

    Chapter  Google Scholar 

  21. Diao, Y., Ganesan, D., Mathur, G., Shenoy, P.: Rethinking data management for storagecentric sensor networks. In: Proceedings of the 3rd Biennial Conference on Innovative Data Systems Research (CIDR’07), Asilomar, California, USA, January 7–10, pp. 22–31 (2007)

    Google Scholar 

  22. Dunkels, A., Gronvall, B., Voigt, T.: Contiki—a lightweight and flexible operating system for tiny networked sensors. In: Proceedings of the 29th Annual IEEE International Conference on Local Computer Networks (LCN’04), Tampa, Florida, USA, November 16–18, pp. 455–462 (2004)

    Chapter  Google Scholar 

  23. Earth Climate and Weather: University of Washington. http://www-k12.atmos.washington.edu/k12/grayskies/ (2010)

  24. Fagin, R.: Combining fuzzy information from multiple systems. J. Comput. Syst. Sci. 58(1), 83–99 (1999)

    Article  MATH  MathSciNet  Google Scholar 

  25. Fagin, R., Lotem, A., Naor, M.: Optimal aggregation algorithms for middleware. In: Proceedings of the Twentieth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (PODS’01), Santa Barbara, California, USA, May 21–23, pp. 102–113 (2001)

    Chapter  Google Scholar 

  26. Galpin, I., Brenninkmeijer, C.Y.A., Jabeen, F., Fernandes, A.A.A., Paton, N.W.: Comprehensive optimization of declarative sensor network queries. In: Proceedings of the 21st International Conference on Scientific and Statistical Database Management (SSDBM’09), New Orleans, Louisiana, USA, June 2–4, pp. 339–360 (2009)

    Google Scholar 

  27. Gay, D., Levis, P., Von Behren, R., Welsh, M., Brewer, E., Culler, D.: The nesC language: a holistic approach to networked embedded systems. In: Proceedings of the ACM SIGPLAN 2003 Conference on Programming Language Design and Implementation (PLDI’03), San Diego, California, USA, June 9–11, pp. 1–11 (2003)

    Chapter  Google Scholar 

  28. Gu, Y., Lo, A., Niemegeers, I.: A survey of indoor positioning systems for wireless personal networks. IEEE Commun. Surv. Tutor. 11(1), 13–32 (2009)

    Article  Google Scholar 

  29. Hill, J., Szewczyk, R., Woo, A., Hollar, S., Culler, D., Pister, K.: System architecture directions for networked sensors. ACM SIGPLAN Not. 35(11), 93–104 (2000)

    Article  Google Scholar 

  30. Intanagonwiwat, C., Govindan, R., Estrin, D.: Directed diffusion: a scalable and robust communication paradigm for sensor networks. In: Proceedings of the 6th Annual International Conference on Mobile Computing and Networking (MOBICOM’00), Boston, Massachusetts, USA, August 6–11, pp. 56–67 (2000)

    Chapter  Google Scholar 

  31. Intel Lab Data: http://db.csail.mit.edu/labdata/labdata.html (2010)

  32. Kalnis, P., Ng, W.-S., Ooi, B.-C., Tan, K.-L.: Answering similarity queries in peer-to-peer networks. In: Proceedings of the 13th International World Wide Web Conference (WWW’04), New York City, NY, USA, May 19–21, pp. 482–483 (2004)

    Google Scholar 

  33. Klan, D., Hose, K., Sattler, K.-U.: Developing and deploying sensor network applications with AnduIN. In: Proceedings of the 6th Workshop on Data Management for Sensor Networks (DMSN’09), Lyon, France, August 24, No. 11 (2009)

    Google Scholar 

  34. Larson, P.-A., Yang, H.Z.: Computing queries from derived relations. In: Proceedings of the 11th International Conference on Very Large Data Bases (VLDB’85), Stockholm, Sweden, August 21–23, pp. 259–269 (1985)

    Google Scholar 

  35. Lee, C.K., Zheng, B., Lee, W.-C., Winter, J.: Materialized in-network view for spatial aggregation queries in wireless sensor network. ISPRS J. Photogramm. Remote Sens. 62(5), 382402 (2007)

    Article  Google Scholar 

  36. Lee, K.C.K., Lee, W.-C., Zheng, B., Winter, J.: Processing multiple aggregation queries in geo-sensor networks. In: Proceedings of the 11th International Conference on Database Systems for Advanced Applications (DASFAA’06), Singapore, April 12–15, pp. 20–34 (2006)

    Google Scholar 

  37. Levis, P., Lee, N., Welsh, M., Culler, D.: TOSSIM: accurate and scalable simulation of entire TinyOS applications. In: Proceedings of the 1st International Conference on Embedded Networked Sensor Systems (SenSys’03), Los Angeles, California, USA, November 5–7, pp. 126–137 (2003)

    Chapter  Google Scholar 

  38. Li, Q., Beaver, J., Amer, A., Chrysanthis, P.K., Labrinidis, A.: Multi-criteria routing in wireless sensor-based pervasive environments. J. Pervasive Comput. Commun. (JPCC’05) 1(4), 313–326 (2005)

    Article  Google Scholar 

  39. Madden, S.R., Franklin, M.J., Hellerstein, J.M., Hong, W.: TAG: a tiny aggregation service for ad-hoc sensor networks. ACM SIGoPS Oper. Syst. Rev. 36(SI), 131–146 (2002). Proceedings of the 5th Symposium on Operating Systems Design and Implementation (OSDI’02)

    Article  Google Scholar 

  40. Madden, S.R., Franklin, M.J., Hellerstein, J.M., Hong, W.: The design of an acquisitional query processor for sensor networks. In: Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data (SIGMOD’03), San Diego, California, USA, June 9–12, pp. 491–502 (2003)

    Chapter  Google Scholar 

  41. Maiocchi, R., Pernici, B.: Temporal data management systems: a comparative view. IEEE Trans. Knowl. Data Eng. (TKDE’91) 3(4), 504–524 (1991)

    Article  Google Scholar 

  42. Malhotra, B., Nascimento, M.A., Nikolaidis, I.: Better tree—better fruits: using dominating set trees for MAX queries. In: Proceedings of the 5th Workshop on Data Management for Sensor Networks (DMSN’08), Auckland, New Zealand, August 24, pp. 1–7 (2008)

    Chapter  Google Scholar 

  43. Marian, A., Gravano, L., Bruno, N.: Evaluating top-k queries over web-accessible databases. ACM Trans. Database Syst. (TODS’04) 29(2), 319–362 (2004)

    Article  Google Scholar 

  44. Michel, S., Triantafillou, P., Weikum, G.: KLEE: a framework for distributed top-k query algorithms. In: Proceedings of the 31st International Conference on Very Large Data Bases (VLDB’05), Trondheim, Norway, August 30–September 2, pp. 637–648 (2005)

    Google Scholar 

  45. Polastre, J., Szewczyk, R., Culler, D.E.: TELOS: enabling ultra-low power wireless research. In: Proceedings of the 4th International Symposium on Information Processing in Sensor Networks (IPSN’05), Los Angeles, California, USA, April 25–27, pp. 364–369 (2005)

    Google Scholar 

  46. Sadler, C., Zhang, P., Martonosi, M., Lyon, S.: Hardware design experiences in zebraNet. In: Proceedings of the 2nd International Conference on Embedded Networked Sensor Systems (SenSys’04), Baltimore, Maryland, USA, November 3–5, pp. 227–238 (2004)

    Google Scholar 

  47. Sharaf, M.A., Beaver, J., Labrinidis, A., Chrysanthis, P.K.: TiNA: a scheme for temporal coherency-aware in-network aggregation. In: Proceedings of the 3rd ACM International Workshop on Data Engineering for Wireless and Mobile Access (MobiDe’03), San Diego, California, USA, September 19, pp. 69–76 (2003)

    Chapter  Google Scholar 

  48. Sharaf, M.A., Beaver, J., Labrinidis, A., Chrysanthis, P.K.: Balancing energy efficiency and quality of aggregate data in sensor networks. Int. J. Very Large Data Bases (VLDBJ’04) 13(4), 384–403 (2004)

    Article  Google Scholar 

  49. Shnayder, V., Hempstead, M., Chen, B., Werner-Allen, G., Welsh, M.: Simulating the power consumption of large-scale sensor network applications. In: Proceedings of the 2nd International Conference on Embedded Networked Sensor Systems (SenSys’04), Baltimore, MD, USA, November 3–5, pp. 188–200 (2004)

    Chapter  Google Scholar 

  50. Silberstein, A., Braynard, R., Ellis, C., Munagala, K., Yang, J.: A sampling-based approach to optimizing top-k queries in sensor networks. In: Proceedings of the 22nd International Conference on Data Engineering (ICDE’06), Atlanta, Georgia, USA, April 3–8, p. 68 (2006)

    Chapter  Google Scholar 

  51. Stern, M., Buchmann, E., Bohm, K.: Towards efficient processing of general-purpose joins in sensor networks. In: Proceedings of the 2009 IEEE International Conference on Data Engineering (ICDE’09), Shanghai, China, March 29–April 2, pp. 126–137 (2009)

    Chapter  Google Scholar 

  52. Szewczyk, R., Mainwaring, A., Polastre, J., Anderson, J., Culler, D.: An analysis of a large scale habitat monitoring application. In: Proceedings of the 2nd International Conference on Embedded Networked Sensor Systems (SenSys’04), Baltimore, Maryland, USA, November 3–5, pp. 214–226. (2004)

    Chapter  Google Scholar 

  53. Texas Instruments: CC2420, single-chip 2.4 GHz IEEE 802.15.4 compliant and ZigBee(TM) ready RF transceiver. Texas Instrument Document. http://www.ti.com/lit/gpn/cc2420 (2007)

  54. Thomas, H., Yi, S., Sherali, H.D.: Rate allocation in wireless sensor networks with network lifetime requirement. In: Proceedings of the 5th ACM International Symposium on Mobile ad hoc Networking and Computing (MobiHoc’04), Tokyo, Japan, May 24–26, pp. 67–77 (2004)

    Google Scholar 

  55. Voltree Power Inc.: http://www.voltreepower.com/ (2010)

  56. Weissman-Lauzac, S., Chrysanthis, P.K.: Personalizing information gathering for mobile database clients. In: Proceedings of the 2002 ACM Symposium on Applied Computing (SAC’02), Madrid, Spain, March 11–14, pp. 49–56 (2002)

    Chapter  Google Scholar 

  57. Weissman-Lauzac, S., Chrysanthis, P.K.: Utilizing versions of views within a mobile environment. In: Proceedings of the International Conference on Computing and Information (ICCI’98), Winnipeg, Manitoba, Canada, June 17–20, pp. 201–208 (1998)

    Google Scholar 

  58. Wu, M., Xu, J., Tang, X., Lee, W.-C.: Top-k monitoring in wireless sensor networks. IEEE Trans. Knowl. Data Eng. 19(7), 962–976 (2007)

    Article  Google Scholar 

  59. Xia, P., Chrysanthis, P.K., Labrinidis, A.: Similarity-aware query processing in sensor networks. In: Proceedings of the 14th International Workshop on Parallel and Distributed Real-Time Systems (WPDRTS’06), Island of Rhodes, Greece, April 25–26, p. 8 (2006)

    Google Scholar 

  60. Yao, Y., Gehrke, J.E.: The cougar approach to in-network query processing in sensor networks. ACM SIGMOD Rec. (SIGMOD’02) 31(3), 9–18 (2002)

    Article  Google Scholar 

  61. Yang, J., Widom, J.: Maintaining temporal views over non-temporal information sources for data warehousing. In: Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology (EDBT’98), Valencia, Spain, March 23–27, pp. 389–403 (1998)

    Google Scholar 

  62. Yu, H., Li, H., Wu, P., Agrawal, D., Abbadi, A.E.: Efficient processing of distributed top-k queries. In: Proceedings of the 16th International Conference on Database and Expert Systems (DEXA’05), Copenhagen, Denmark, August 22–26, pp. 65–74 (2005)

    Google Scholar 

  63. Zeinalipour-Yazti, D., Andreou, P., Chrysanthis, P.K., Samaras, G.: MINT views: materialized in network top-k views in sensor networks. In: Proceedings of the 8th International Conference on Mobile Data Management (MDM’07), Mannheim, Germany, May 7–11, pp. 182–189 (2007)

    Chapter  Google Scholar 

  64. Zeinalipour-Yazti, D., Andreou, P., Chrysanthis, P.K., Samaras, G., Pitsillides, A.: The MicroPulse framework for adaptive waking windows in sensor networks. In: Proceedings of the 1st International Workshop on Data Intensive Sensor Networks (DISN’07), Mannheim, Germany, May 11, pp. 351–355 (2007)

    Google Scholar 

  65. Zeinalipour-Yazti, D., Lin, S., Kalogeraki, V., Gunopulos, D., Najjar, W.: MicroHash: an efficient index structure for flash-based sensor devices. In: Proceedings of the 4th USENIX Conference on File and Storage Technologies (FAST’05), San Francisco, California, USA, December 13–16, pp. 31–44 (2005)

    Google Scholar 

  66. Zeinalipour-Yazti, D., Lin, S., Gunopulos, D.: Distributed spatio-temporal similarity search. In: Proceedings of the 15th ACM International Conference on Information and Knowledge Management (CIKM’06), Arlington, VA, USA, November 6–11, pp. 14–23 (2006)

    Chapter  Google Scholar 

  67. Zeinalipour-Yazti, D., Vagena, Z., Gunopulos, D., Kalogeraki, V., Tsotras, V., Vlachos, M., Koudas, N., Srivastava, D.: The threshold join algorithm for top-k queries in distributed sensor networks. In: Proceedings of the 2nd International Workshop on Data Management for Sensor Networks (DMSN’05), Trondheim, Norway, August 29, pp. 61–66 (2005)

    Chapter  Google Scholar 

  68. ZigBee Alliance: ZigBee specification. ZigBee Document 053474r06, Version 1.0 (2004)

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Authors and Affiliations

  1. Department of Computer Science, University of Cyprus, Nicosia, 1678, Cyprus

    Panayiotis Andreou, Demetrios Zeinalipour-Yazti & George Samaras

  2. Department of Computer Science, University of Pittsburgh, Pittsburgh, PA, 5213-4034, USA

    Panos K. Chrysanthis

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  1. Panayiotis Andreou
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  2. Demetrios Zeinalipour-Yazti
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  3. Panos K. Chrysanthis
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  4. George Samaras
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Correspondence to Demetrios Zeinalipour-Yazti.

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Communicated by Erik Buchmann.

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Andreou, P., Zeinalipour-Yazti, D., Chrysanthis, P.K. et al. Power efficiency through tuple ranking in wireless sensor network monitoring. Distrib Parallel Databases 29, 113–150 (2011). https://doi.org/10.1007/s10619-010-7072-5

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