Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
권호 표지
DOI QR코드

DOI QR Code

Routing Techniques for Data Aggregation in Sensor Networks

  • Kim, Jeong-Joon (Dept. of Computer Science & Engineering, Korea Polytechnic University)
  • Received : 2017.04.12
  • Accepted : 2017.09.09
  • Published : 2018.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

GR-tree and query aggregation techniques have been proposed for spatial query processing in conventional spatial query processing for wireless sensor networks. Although these spatial query processing techniques consider spatial query optimization, time query optimization is not taken into consideration. The index reorganization cost and communication cost for the parent sensor nodes increase the energy consumption that is required to ensure the most efficient operation in the wireless sensor node. This paper proposes itinerary-based R-tree (IR-tree) for more efficient spatial-temporal query processing in wireless sensor networks. This paper analyzes the performance of previous studies and IR-tree, which are the conventional spatial query processing techniques, with regard to the accuracy, energy consumption, and query processing time of the query results using the wireless sensor data with Uniform, Gauss, and Skew distributions. This paper proves the superiority of the proposed IR-tree-based space-time indexing.

Keywords

References

  1. K. T. Kim and H. Y. Youn, "Tree-based clustering protocol for energy-efficient wireless sensor networks," The KIPS Transactions: Part C, vol. 17, no. 1, pp. 69-80, 2010.
  2. M. Lee and V. W. S. Wong, "An energy-aware spanning tree algorithm for data aggregation in wireless sensor networks," in Proceedings of the IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, Victoria, Canada, 2005, pp. 300-303.
  3. K. Akkaya and M. Younis, "A survey on routing protocols for wireless sensor networks," Ad Hoc Network, vol. 3 no. 3, pp.325-349, 2005. https://doi.org/10.1016/j.adhoc.2003.09.010
  4. V. S. Felix Enigo and V. Ramachandran, "Effective management of high rate spatio-temporal queries in Wireless Sensor Networks," Wireless Personal Communications, vol. 79, no. 2, pp. 1111-1128, 2014. https://doi.org/10.1007/s11277-014-1920-y
  5. T. K. Sellis, N. Roussopoulos, and C. Faloutsos, "The R+-Tree: a dynamic index for multi-dimensional objects," in Proceedings of the 13th International Conference on Very Large Data Bases, Brighton, England, 1987, pp. 507-518.
  6. A. Guttman, "R-Trees: a dynamic index structure for spatial searching," in Proceedings of the ACM SIGMOD International Conference on Management of Data, Boston, MA, 1984, pp. 47-57.
  7. M. S. Kim and I. S. Jang, "The GR-tree: an energy-efficient distributed spatial indexing scheme in wireless sensor networks," Journal of Korean Spatial Information Society, vol. 19, no. 5, pp. 63-74, 2011.
  8. J. J. Kim, I. S. Shin, K. Y. Lee, and K. J. Han, "Efficient Processing of aggregate queries in wireless sensor networks," Journal of Korean Spatial Information Society, vol. 19, no. 3, pp. 95-106, 2011.