The Community for Technology Leaders
RSS Icon
Issue No.02 - February (2011 vol.23)
pp: 266-281
Hsiao-Ping Tsai , National Chung Hsing University, Taichung
De-Nian Yang , Academia Sinica, Taipei
Ming-Syan Chen , Academia Sinica and National Taiwan University, Taipei
Existing object tracking applications focus on finding the moving patterns of a single object or all objects. In contrast, we propose a distributed mining algorithm that identifies a group of objects with similar movement patterns. This information is important in some biological research domains, such as the study of animals' social behavior and wildlife migration. The proposed algorithm comprises a local mining phase and a cluster ensembling phase. In the local mining phase, the algorithm finds movement patterns based on local trajectories. Then, based on the derived patterns, we propose a new similarity measure to compute the similarity of moving objects and identify the local group relationships. To address the energy conservation issue in resource-constrained environments, the algorithm only transmits the local grouping results to the sink node for further ensembling. In the cluster ensembling phase, our algorithm combines the local grouping results to derive the group relationships from a global view. We further leverage the mining results to track moving objects efficiently. The results of experiments show that the proposed mining algorithm achieves good grouping quality, and the mining technique helps reduce the energy consumption by reducing the amount of data to be transmitted.
Distributed clustering, similarity measure, object tracking, WSN.
Hsiao-Ping Tsai, De-Nian Yang, Ming-Syan Chen, "Mining Group Movement Patterns for Tracking Moving Objects Efficiently", IEEE Transactions on Knowledge & Data Engineering, vol.23, no. 2, pp. 266-281, February 2011, doi:10.1109/TKDE.2009.202
[1] African Elephant, habitat/ wildlifeelephant.php, 2010.
[2] Mica2 Sensor Board, http:/, 2010.
[3] Stargate: A Platform X Project, http:/, 2010.
[4] R. Agrawal and R. Srikant, "Mining Sequential Patterns," Proc. 11th Int'l Conf. Data Eng., pp. 3-14, 1995.
[5] I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, "Wireless Sensor Networks: A Survey," Computer Networks, vol. 38, no. 4, pp. 393-422, 2002.
[6] J.N. Al Karaki and A.E. Kamal, "Routing Techniques in Wireless Sensor Networks: A Survey," IEEE Wireless Comm., vol. 11, no. 6, pp. 6-28, Dec. 2004.
[7] A. Apostolico and G. Bejerano, "Optimal Amnesic Probabilistic Automata or How to Learn and Classify Proteins in Linear Time and Space," Proc. Fourth Ann. Int'l Conf. Computational Molecular Biology, pp. 25-32, 2000.
[8] H. Ayad, O.A. Basir, and M. Kamel, "A Probabilistic Model Using Information Theoretic Measures for Cluster Ensembles," Proc. Fifth Int'l Workshop Multiple Classifier Systems, pp. 144-153, June 2004.
[9] G. Bejerano and G. Yona, "Variations on Probabilistic Suffix Trees: Statistical Modeling and the Prediction of Protein Families," Bioinformatics, vol. 17, no. 1, pp. 23-43, 2001.
[10] D. Bolier, "SIM: A C++ Library for Discrete Event Simulation,", Oct. 1995.
[11] D. Bollegala, Y. Matsuo, and M. Ishizuka, "Measuring Semantic Similarity between Words Using Web Search Engines," Proc. 16th Int'l World Wide Web Conf., pp. 757-766, 2007.
[12] L. Chen, M. TamerÖzsu, and V. Oria, "Robust and Fast Similarity Search for Moving Object Trajectories," Proc. ACM SIGMOD, pp. 491-502, 2005.
[13] M.-S. Chen, J.S. Park, and P.S. Yu, "Efficient Data Mining for Path Traversal Patterns," Knowledge and Data Eng., vol. 10, no. 2, pp. 209-221, 1998.
[14] T.M. Cover and J.A. Thomas, Elements of Information Theory. Wiley Interscience, Aug. 1991.
[15] X.Z. Fern and C.E. Brodley, "Random Projection for High Dimensional Data Clustering: A Cluster Ensemble Approach," Proc. 20th Int'l Conf. Machine Learning, pp. 1186-1193, June 2003.
[16] A.L.N. Fred and A.K. Jain, "Combining Multiple Clusterings Using Evidence Accumulation," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 6, pp. 835-850, June 2005.
[17] F. Giannotti, M. Nanni, F. Pinelli, and D. Pedreschi, "Trajectory Pattern Mining," Proc. 13th ACM SIGKDD, pp. 330-339, 2007.
[18] B. Gloss, M. Scharf, and D. Neubauer, "Location-Dependent Parameterization of a Random Direction Mobility Model," Proc. IEEE 63rd Conf. Vehicular Technology, vol. 3, pp. 1068-1072, 2006.
[19] V. Guralnik and G. Karypis, "A Scalable Algorithm for Clustering Sequential Data," Proc. First IEEE Int'l Conf. Data Mining, pp. 179-186, 2001.
[20] J. Han, J. Pei, B. Mortazavi-Asl, Q. Chen, U. Dayal, and M. Hsu, "Freespan: Frequent Pattern-Projected Sequential Pattern Mining," Proc. Sixth ACM SIGKDD, pp. 355-359, 2000.
[21] E. Hartuv and R. Shamir, "A Clustering Algorithm Based on Graph Connectivity," Information Processing Letters, vol. 76, nos. 4-6, pp. 175-181, 2000.
[22] J. Hightower and G. Borriello, "Location Systems for Ubiquitous Computing," Computer, vol. 34, no. 8, pp. 57-66, Aug. 2001.
[23] X. Hong, M. Gerla, G. Pei, and C. Chiang, "A Group Mobility Model for Ad Hoc Wireless Networks," Proc. Ninth ACM/IEEE Int'l Symp. Modeling, Analysis and Simulation of Wireless and Mobile Systems, pp. 53-60, Aug. 1999.
[24] H. Kargupta, W. Huang, K. Sivakumar, and E.L. Johnson, "Distributed Clustering Using Collective Principal Component Analysis," Knowledge and Information System, vol. 3, pp. 422-448, 2001.
[25] D. Katsaros and Y. Manolopoulos, "A Suffix Tree Based Prediction Scheme for Pervasive Computing Environments," Proc. Panhellenic Conf. Informatics, pp. 267-277, Nov. 2005.
[26] H.T. Kung and D. Vlah, "Efficient Location Tracking Using Sensor Networks," Proc. Conf. IEEE Wireless Comm. and Networking, vol. 3, pp. 1954-1961, Mar. 2003.
[27] C. Largeron-Leténo, "Prediction Suffix Trees for Supervised Classification of Sequences," Pattern Recognition Letters, vol. 24, no. 16, pp. 3153-3164, 2003.
[28] J.-G. Lee, J. Han, and K.-Y. Whang, "Trajectory Clustering: A Partition-and-Group Framework," Proc. ACM SIGMOD, pp. 593-604, 2007.
[29] D. Li, K.D. Wong, Y.H. Hu, and A.M. Sayeed, "Detection, Classification, and Tracking of Targets," IEEE Signal Processing Magazine, vol. 19, no. 2, pp. 17-30, Mar. 2002.
[30] Y. Li, J. Han, and J. Yang, "Clustering Moving Objects," Proc. 10th ACM SIGKDD, pp. 617-622, 2004.
[31] C.-Y. Lin, W.-C. Peng, and Y.-C. Tseng, "Efficient In-Network Moving Object Tracking in Wireless Sensor Networks," IEEE Trans. Mobile Computing, vol. 5, no. 8, pp. 1044-1056, Aug. 2006.
[32] G. Mazeroff, J. Gregor, M. Thomason, and R. Ford, "Probabilistic Suffix Models for API Sequence Analysis of Windows XP Applications," Pattern Recognition, vol. 41, no. 1, pp. 90-101, 2008.
[33] M. Morzy, "Prediction of Moving Object Location Based on Frequent Trajectories," Proc. 21st Int'l Symp. Computer and Information Sciences, pp. 583-592, Nov. 2006.
[34] M. Morzy, "Mining Frequent Trajectories of Moving Objects for Location Prediction," Proc. Fifth Int'l Conf. Machine Learning and Data Mining in Pattern Recognition, pp. 667-680, July 2007.
[35] M. Nanni and D. Pedreschi, "Time-Focused Clustering of Trajectories of Moving Objects," J. Intelligent Information Systems, vol. 27, no. 3, pp. 267-289, 2006.
[36] S. Pandey, S. Dong, P. Agrawal, and K. Sivalingam, "A Hybrid Approach to Optimize Node Placements in Hierarchical Heterogeneous Networks," Proc. IEEE Conf. Wireless Comm. and Networking Conf., pp. 3918-3923, Mar. 2007.
[37] W.-C. Peng and M.-S. Chen, "Developing Data Allocation Schemes by Incremental Mining of User Moving Patterns in a Mobile Computing System," IEEE Trans. Knowledge and Data Eng., vol. 15, no. 1, pp. 70-85, Jan./Feb. 2003.
[38] W.-C. Peng, Y.-Z. Ko, and W.-C. Lee, "On Mining Moving Patterns for Object Tracking Sensor Networks," Proc. Seventh Int'l Conf. Mobile Data Management, p. 41, 2006.
[39] C.J. Van Rijsbergen, Information Retrieval. Springer, 1979.
[40] D. Ron, Y. Singer, and N. Tishby, "Learning Probabilistic Automata with Variable Memory Length," Proc. Seventh Ann. Conf. Computational Learning Theory, July 1994.
[41] C. Roux and R.T.F. Bernard, "Home Range Size, Spatial Distribution and Habitat Use of Elephants in Two Enclosed Game Reserves in the Eastern Cape Province, South Africa," African J. Ecology, Oct. 2007.
[42] S. Santini and K. Romer, "An Adaptive Strategy for Quality-Based Data Reduction in Wireless Sensor Networks," Proc. Third Int'l Conf. Networked Sensing Systems, pp. 29-36, June 2006.
[43] G. Saporta and G. Youness, "Comparing Two Partitions: Some Proposals and Experiments," Proc. Computational Statistics, Aug. 2002.
[44] G. Shannon, B. Page, K. Duffy, and R. Slotow, "African Elephant Home Range and Habitat Selection in Pongola Game Reserve, South Africa," African Zoology, vol. 41, no. 1, pp. 37-44, Apr. 2006.
[45] A. Silberstein, "Push and Pull in Sensor Network Query Processing," Proc. Southeast Workshop Data and Information Management, Mar. 2006.
[46] A. Strehl and J. Ghosh, "Cluster Ensembles—A Knowledge Reuse Framework for Combining Partitionings," Proc. Conf. Artificial Intelligence, pp. 93-98, July 2002.
[47] J. Tang, B. Hao, and A. Sen, "Relay Node Placement in Large Scale Wireless Sensor Networks," J. Computer Comm., special issue on sensor networks, vol. 29, no. 4, pp. 490-501, 2006.
[48] V.S. Tseng and K.W. Lin, "Energy Efficient Strategies for Object Tracking in Sensor Networks: A Data Mining Approach," J. Systems and Software, vol. 80, no. 10, pp. 1678-1698, 2007.
[49] G. Wang, H. Wang, J. Cao, and M. Guo, "Energy-Efficient Dual Prediction-Based Data Gathering for Environmental Monitoring Applications," Proc. IEEE Wireless Comm. and Networking Conf., Mar. 2007.
[50] Y. Wang, E.-P. Lim, and S.-Y. Hwang, "Efficient Mining of Group Patterns from User Movement Data," Data Knowledge Eng., vol. 57, no. 3, pp. 240-282, 2006.
[51] Y. Xu, J. Winter, and W.-C. Lee, "Dual Prediction-Based Reporting for Object Tracking Sensor Networks," Proc. First Ann. Int'l Conf. Mobile and Ubiquitous Systems: Networking and Services, pp. 154-163, Aug. 2004.
[52] J. Yang and M. Hu, "Trajpattern: Mining Sequential Patterns from Imprecise Trajectories of Mobile Objects," Proc. 10th Int'l Conf. Extending Database Technology, pp. 664-681, Mar. 2006.
[53] J. Yang and W. Wang, "CLUSEQ: Efficient and Effective Sequence Clustering," Proc. 19th Int'l Conf. Data Eng., pp. 101-112, Mar. 2003.
[54] J. Yang and W. Wang, "Agile: A General Approach to Detect Transitions in Evolving Data Streams," Proc. Fourth IEEE Int'l Conf. Data Mining, pp. 559-562, 2004.
[55] J. Yick, B. Mukherjee, and D. Ghosal, "Analysis of a Prediction-Based Mobility Adaptive Tracking Algorithm," Proc. Second Int'l Conf. Broadband Networks, pp. 753-760, Oct. 2005.
[56] M. Younis and K. Akkaya, "Strategies and Techniques for Node Placement in Wireless Sensor Networks: A Survey," Ad Hoc Networks, vol. 6, no. 4, pp. 621-655, 2008.
[57] W. Zhang and G. Cao, "DCTC: Dynamic Convoy Tree-Based Collaboration for Target Tracking in Sensor Networks," IEEE Trans. Wireless Comm., vol. 3, no. 5, pp. 1689-1701, Sept. 2004.
38 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool