CLC number: TP393
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2011-01-31
Cited: 0
Clicked: 7580
Young-Mo Kwon, Harim Jung, Yon Dohn Chung. Monitoring continuous k-nearest neighbor queries in the hybrid wireless network[J]. Journal of Zhejiang University Science C, 2011, 12(3): 213-220.
@article{title="Monitoring continuous k-nearest neighbor queries in the hybrid wireless network",
author="Young-Mo Kwon, Harim Jung, Yon Dohn Chung",
journal="Journal of Zhejiang University Science C",
volume="12",
number="3",
pages="213-220",
year="2011",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C1000080"
}
%0 Journal Article
%T Monitoring continuous k-nearest neighbor queries in the hybrid wireless network
%A Young-Mo Kwon
%A Harim Jung
%A Yon Dohn Chung
%J Journal of Zhejiang University SCIENCE C
%V 12
%N 3
%P 213-220
%@ 1869-1951
%D 2011
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1000080
TY - JOUR
T1 - Monitoring continuous k-nearest neighbor queries in the hybrid wireless network
A1 - Young-Mo Kwon
A1 - Harim Jung
A1 - Yon Dohn Chung
J0 - Journal of Zhejiang University Science C
VL - 12
IS - 3
SP - 213
EP - 220
%@ 1869-1951
Y1 - 2011
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C1000080
Abstract: In a mobile/pervasive computing environment, one of the most important goals of monitoring continuous spatial queries is to reduce communication cost for location-updates. Existing work uses many cellular wireless connections, which would easily become the performance bottleneck of the overall system. This paper introduces a novel continuous kNN query monitoring method to reduce communication cost in the hybrid wireless network, where the moving objects in the wireless broadcasting system construct the ad-hoc network. Simulation results prove the efficiency of the proposed method, which leverages the wireless broadcasting channel as well as the WiFi link to alleviate the burden on the cellular uplink communication cost.
[1]Akyildiz, I.F., Su, W., Sankarasubramaniam, Y., Cayirci, E., 2002. A survey on sensor network. IEEE Commun. Mag., 40(8):102-114.
[2]Babcock, B., Olston, C., 2003. Distributed Top-k Monitoring. Proc. ACM SIGMOD Int. Conf. on Management of Data, p.28-39.
[3]Bohm, C., 2000. A cost model for query processing in high dimensional data spaces. ACM Trans. Database Syst., 25(2):129-178.
[4]Cheung, K.L., Fu, A.W.C., 1998. Enhanced nearest neighbor search on the R-tree. ACM SIGMOD Rec., 27(3):16-21.
[5]Giordano, S., Stojmenovic, I., Blazevic, L., 2003. Position-Based Routing Algorithms for Ad Hoc Networks: a Taxonomy. In: Cheng, X., Huang, X., Du, D.Z. (Eds.), Ad Hoc Wireless Networking. Kluwer Academic Publishers, Dordrecht, p.103-136.
[6]Guttman, A., 1984. R-trees: a dynamic index structure for spatial searching. ACM SIGMOD Rec., 14(2):47-57.
[7]Hjaltason, G.R., Samet, H., 1999. Distance browsing in spatial databases. ACM Trans. Database Syst., 24(2):265-318.
[8]Hu, H., Xu, J., Lee, D., 2005. A Generic Framework for Monitoring Continuous Spatial Queries over Moving Objects. Proc. ACM SIGMOD Int. Conf. on Management of Data, p.479-490.
[9]Imielinski, T., Viswanathan, S., Bardrinath, B.R., 1997. Data on air: organization and access. IEEE Trans. Knowl. Data Eng., 9(3):353-372.
[10]Junglas, I.A., Watson, R.T., 2008. Location-based services. Commun. ACM, 51(3):65-69.
[11]Ko, Y.B., Vaidya, N.H., 1998. Location-Aided Routing (LAR) in Mobile Ad Hoc Networks. Proc. 4th Annual ACM/IEEE Int. Conf. on Mobile Computing and Networking, p.66-75.
[12]Lee, D.L., Lee, W.C., Xu, J., Zheng, B., 2002. Data management in location-dependent information services: challenges and issues. IEEE Perv. Comput., 1(3):65-72.
[13]Mouratidis, K., Hadjieleftheriou, M., Papadias, D., 2005a. Conceptual Partitioning: an Efficient Method for Continuous Nearest Neighbor Monitoring. Proc. ACM SIGMOD Int. Conf. on Management of Data, p.634-645.
[14]Mouratidis, K., Papadias, D., Bakiras, S., Tao, Y., 2005b. A threshold-based algorithm for continuous monitoring of k nearest neighbors. IEEE Trans. Knowl. Data Eng., 17(11):1451-1464.
[15]Papadopoulos, A., Manolopoulos, Y., 1997. Performance of Nearest Neighbor Queries in R-Trees. Proc. 6th Int. Conf. on Database Theory, p.394-408.
[16]Roussopoulos, N., Kelley, S., Vincent, F., 1995. Nearest neighbor search. ACM SIGMOD Rec., 24(2):71-79.
[17]Tao, Y., Papadias, D., 2002. Time-Parameterized Queries in Spatio-Temporal Databases. Proc. ACM SIGMOD Int. Conf. on Management of Data, p.334-345.
[18]Xiong, X., Mokbel, M.F., Aref, W.G., 2005. SEA-CNN: Scalable Processing of Continuous K-Nearest Neighbor Queries in Spatio-Temporal Databases. 21st Int. Conf. on Data Engineering, p.643-654.
[19]Yu, X., Pu, K.Q., Koudas, N., 2005. Monitoring k-Nearest Neighbor Queries over Moving Objects. 21st Int. Conf. on Data Engineering, p.631-642.
[20]Zhang, J., Zhu, M., Papadias, D., Tao, Y., Lee, D., 2003. Location-Based Spatial Queries. Proc. ACM SIGMOD Int. Conf. on Management of Data, p.443-454.
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