CLC number: TN929
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2014-07-16
Cited: 1
Clicked: 8326
Yuan-Ko Huang, Lien-Fa Lin. Designing a location update strategy for free-moving and network-constrained objects with varying velocity[J]. Journal of Zhejiang University Science C, 2014, 15(8): 675-686.
@article{title="Designing a location update strategy for free-moving and network-constrained objects with varying velocity",
author="Yuan-Ko Huang, Lien-Fa Lin",
journal="Journal of Zhejiang University Science C",
volume="15",
number="8",
pages="675-686",
year="2014",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C1300337"
}
%0 Journal Article
%T Designing a location update strategy for free-moving and network-constrained objects with varying velocity
%A Yuan-Ko Huang
%A Lien-Fa Lin
%J Journal of Zhejiang University SCIENCE C
%V 15
%N 8
%P 675-686
%@ 1869-1951
%D 2014
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1300337
TY - JOUR
T1 - Designing a location update strategy for free-moving and network-constrained objects with varying velocity
A1 - Yuan-Ko Huang
A1 - Lien-Fa Lin
J0 - Journal of Zhejiang University Science C
VL - 15
IS - 8
SP - 675
EP - 686
%@ 1869-1951
Y1 - 2014
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C1300337
Abstract: spatio-temporal databases aim at appropriately managing moving objects so as to support various types of queries. While much research has been conducted on developing query processing techniques, less effort has been made to address the issue of when and how to update location information of moving objects. Previous work shifts the workload of processing updates to each object which usually has limited CPU and battery capacities. This results in a tremendous processing overhead for each moving object. In this paper, we focus on designing efficient update strategies for two important types of moving objects, free-moving objects (FMOs) and network-constrained objects (NCOs), which are classified based on object movement models. For FMOs, we develop a novel update strategy, namely the FMO update strategy (FMOUS), to explicitly indicate a time point at which the object needs to update location information. As each object knows in advance when to update (meaning that it does not have to continuously check), the processing overhead can be greatly reduced. In addition, the FMO update procedure (FMOUP) is designed to efficiently process the updates issued from moving objects. Similarly, for NCOs, we propose the NCO update strategy (NCOUS) and the NCO update procedure (NCOUP) to inform each object when and how to update location information. Extensive experiments are conducted to demonstrate the effectiveness and efficiency of the proposed update strategies.
[1]Brinkhoff, T., 2002. A framework for generating network-based moving objects. GeoInformatica, 6(2):153-180.
[2]Chen, S., Ooi, B.C., Zhang, Z., 2010. An adaptive updating protocol for reducing moving object database workload. Int. Conf. on Very Large Data Bases, p.735-746.
[3]Cheng, R., Kalashnikov, D.V., Prabhakar, S., 2004. Querying imprecise data in moving object environments. IEEE Trans. Knowl. Data Eng., 16(9):1112-1127.
[4]Chung, B.S.E., Lee, W.C., Chen, A.L.P., 2009. Processing probabilistic spatio-temporal range queries over moving objects with uncertainty. Int. Conf. on Extending Database Technology, p.60-71.
[5]Forlizzi, L., Güting, R.H., Nardelli, E., et al., 2000. A data model and data structures for moving objects databases. Int. Conf. on ACM Management of Data, p.319-330.
[6]Güting, R.H., Bohlen, M.H., Erwig, M., et al., 2000. A foundation for representing and querying moving objects. ACM Trans. Database Syst., 25(1):1-42.
[7]Huang, Y.K., Lee, C., 2010. Efficient evaluation of continuous spatio-temporal queries on moving objects with uncertain velocity. GeoInformatica, 14(2):163-200.
[8]Huang, Y.K., Liao, S.J., Lee, C., 2009. Evaluating continuous K-nearest neighbor query on moving objects with uncertainty. Inform. Syst., 34(4-5):415-437.
[9]Huang, Y.K., Su, I.F., Lin, L.F., et al., 2013. Efficient processing of updates for moving objects with varying speed and direction. Int. Conf. on Advanced Information Networking and Applications, p.854-861.
[10]Sistla, A.P., Wolfson, O., Chamberlain, S., et al., 1997. Modeling and querying moving objects. Int. Conf. on Data Engineering, p.422-432.
[11]Song, Z., Roussopoulos, N., 2001. K-nearest neighbor search for moving query point. Int. Conf. on Spatial and Temporal Databases, p.79-96.
[12]Tao, Y., Papadias, D., 2002. Time parameterized queries in spatio-temporal databases. Int. Conf. on ACM Management of Data, p.334-345.
[13]Tao, Y., Faloutsos, C., Papadias, D., et al., 2004. Prediction and indexing of moving objects with unknown motion patterns. Int. Conf. on ACM Management of Data, p.611-622.
[14]Wolfson, O., Yin, H., 2003. Accuracy and resource consumption in tracking and location prediction. LNCS, 2750:325-343.
[15]Wolfson, O., Sistla, A.P., Chamberlain, S., et al., 1999. Updating and querying databases that track mobile units. Distr. Parall. Databases, 7(3):257-387.
[16]Xiong, X., Mokbel, M.F., Aref, W.G., 2005. SEA-CNN: scalable processing of continuous K-nearest neighbor queries in spatio-temporal databases. Int. Conf. on Data Engineering, p.643-654.
[17]Xiong, X., Mokbel, M.F., Aref, W.G., 2006. LUGrid: update-tolerant grid-based indexing for moving object. Int. Conf. on Mobile Data Management, p.13-20.
[18]Yu, X., Pu, K.Q., Koudas, N., 2005. Monitoring K-nearest neighbor queries over moving objects. Int. Conf. on Data Engineering, p.631-642.
Open peer comments: Debate/Discuss/Question/Opinion
<1>