Routing Techniques for Data Aggregation in Sensor Networks
1
조회
0
다운로드
1
인용
0
참고문헌
키워드
초록
GR-tree and query aggregation techniques have been proposed for spatial query processing in conventionalspatial query processing for wireless sensor networks. Although these spatial query processing techniquesconsider spatial query optimization, time query optimization is not taken into consideration. The indexreorganization cost and communication cost for the parent sensor nodes increase the energy consumptionthat is required to ensure the most efficient operation in the wireless sensor node. This paper proposesitinerary-based R-tree (IR-tree) for more efficient spatial-temporal query processing in wireless sensornetworks. This paper analyzes the performance of previous studies and IR-tree, which are the conventionalspatial query processing techniques, with regard to the accuracy, energy consumption, and query processingtime of the query results using the wireless sensor data with Uniform, Gauss, and Skew distributions. Thispaper proves the superiority of the proposed IR-tree-based space-time indexing.
Abstract
GR-tree and query aggregation techniques have been proposed for spatial query processing in conventionalspatial query processing for wireless sensor networks. Although these spatial query processing techniquesconsider spatial query optimization, time query optimization is not taken into consideration. The indexreorganization cost and communication cost for the parent sensor nodes increase the energy consumptionthat is required to ensure the most efficient operation in the wireless sensor node. This paper proposesitinerary-based R-tree (IR-tree) for more efficient spatial-temporal query processing in wireless sensornetworks. This paper analyzes the performance of previous studies and IR-tree, which are the conventionalspatial query processing techniques, with regard to the accuracy, energy consumption, and query processingtime of the query results using the wireless sensor data with Uniform, Gauss, and Skew distributions. Thispaper proves the superiority of the proposed IR-tree-based space-time indexing.
참고문헌 (8)
- 김경태, "에너지 효율적 무선 센서 네트워크를 위한 트리 기반 클러스터링 프로토콜", 정보처리학회논문지C, 17(1), pp.69~80 (2010), 한국정보처리학회
- M. Lee, "An energy-aware spanning tree algorithm for data aggregation in wireless sensor networks", Proceedings of the IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, pp.300~303 (2005)
- K. Akkaya, "A survey on routing protocols for wireless sensor networks", Ad Hoc Network, 3(3), pp.325~349 (2005)[DOI]
- V. S. Felix Enigo, "Effective management of high rate spatio-temporal queries in Wireless Sensor Networks", Wireless Personal Communications, 79(2), pp.1111~1128 (2014)[DOI]
- T. K. Sellis, "The R+-Tree: a dynamic index for multi-dimensional objects", Proceedings of the 13th International Conference on Very Large Data Bases, pp.507~518 (1987)
- A. Guttman, "R-Trees: a dynamic index structure for spatial searching", Proceedings of the ACM SIGMOD International Conference on Management of Data, pp.47~57 (1984)
- 김민수, "GR-tree: 무선 센서 네트워크에서 에너지 효율적인 분산 공간색인기법", Spatial Information Research, 19(5), pp.63~74 (2011), 대한공간정보학회
- 김정준, "무선 센서 네트워크에서 효율적인 집계 질의 처리", Spatial Information Research, 19(3), pp.95~106 (2011), 대한공간정보학회