Abstract
Sensor networks have played an important role in our daily life. The most common applications are light and humidity monitoring, environment and habitat monitoring. Window queries over the sensor networks become popular. However, due to the limited power supply, ordinary query methods can not be applied on sensor networks. Queries over sensor networks should be power-aware to guarantee the maximum power savings. In this paper, we concentrate on minimal power consumption by avoiding the expensive communication. A lot of work have been done to reduce the participated nodes, but none of them have considered the overlapping minimum bounded rectangle (MBR) of sensors which make them impossible to reach the optimization solution. The OMSI-tree and OMR algorithm proposed by us can efficiently solve this problem by executing a given query only on the sensors involved. Experiments show that there is an obvious improvement compared with TinyDB and other spatial index, adopting the proposed schema and algorithm.
This research was supported by the MIC (Ministry of Information and Communication), Korea, under the ITRC (Information Technology Research Center) support program supervised by the IITA (Institute of Information Technology Assessment).
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References
21 ideas for the 21st century, Business Week, pp. 78–167 (August 30, 1999)
Rappaport, T.: Wireless Communications: Principles and Practice. PH Inc. (1996)
Madden, S.R., Franklin, M.J., Hellerstein, J.M., Tiny, D.B.: An Acquisitional Query Processing System for Sensor Networks. ACM Transasctions on Database Systems 30(1), 122–173 (March 2005)
Gutman, A.: R-Tree – A dynamic index structure for spatial searching, SIGMOD 1984, Boston, MA (1984)
Madden, S., Franklin, M.J.: Fjording the Stream: An Architecture for Queries over Streaming Sensor Data. In: Proc.18th Int. Conference on Data Engineering, pp. 555–566 (2002)
Yao, Y., Gehrke, J.: The Cougar approach to in-network query processing in sensor networks. SIGMOD Record 31(3), 9–18 (2002)
Soheili, A., Kalogeraki, V., Gunopulos, D.: Spatial queries in sensor networks. In: 13th annual ACM international workshop on Geographic information systems, Bremen, Germany, pp. 61–70 (2005)
Eo, S.H., Pandey, S., Park, S.-Y., Bae, H.-Y.: Energy Efficient Design for Window Query Processing in Sensor Networks. APWeb Workshops, pp. 310–314 (2006)
Beckmann, N., kriegel, H.-P., Schneider, R., Seeger, B.: The R*-Tree: An Efficient and Robust Access Method for Points and Rectangles. ACM (1990)
Sharaf, M.A., Beaver, J., Labrinidis, A., Chrysanthis, P.: TiNA: A Scheme for Temporal Coherency-Aware in-Network Aggregation. In: Proc. of, International Workshop in MobileData Engineering (2003)
Eo, S.H., Pandey, S., Kim, M.-K., Oh, Y.-H., Bae, H.-Y.: FDSI-Tree: A Fully Distributed Spatial Index Tree for Efficient & Power-Aware Range Queries in Sensor Network. In: Wiedermann, J., Tel, G., Pokorný, J., Bieliková, M., Štuller, J. (eds.) SOFSEM 2006. LNCS, vol. 3831, pp. 254–261. Springer, Heidelberg (2006)
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Zha, W., Eo, SH., You, BS., Lee, DW., Bae, HY. (2007). OMSI-Tree: Power-Awareness Query Processing over Sensor Networks by Removing Overlapping Regions. In: Chang, K.CC., et al. Advances in Web and Network Technologies, and Information Management. APWeb WAIM 2007 2007. Lecture Notes in Computer Science, vol 4537. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72909-9_21
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DOI: https://doi.org/10.1007/978-3-540-72909-9_21
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