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Issue No.01 - January (2011 vol.23)

pp: 95-109

Hsiao-Ping Tsai , National Taiwan University, Taipei

De-Nian Yang , National Taiwan University, Taipei

Ming-Syan Chen , National Taiwan University, Taipei

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2010.30

ABSTRACT

Natural phenomena show that many creatures form large social groups and move in regular patterns. However, previous works focus on finding the movement patterns of each single object or all objects. In this paper, we first propose an efficient distributed mining algorithm to jointly identify a group of moving objects and discover their movement patterns in wireless sensor networks. Afterward, we propose a compression algorithm, called 2P2D, which exploits the obtained group movement patterns to reduce the amount of delivered data. The compression algorithm includes a sequence merge and an entropy reduction phases. In the sequence merge phase, we propose a Merge algorithm to merge and compress the location data of a group of moving objects. In the entropy reduction phase, we formulate a Hit Item Replacement (HIR) problem and propose a Replace algorithm that obtains the optimal solution. Moreover, we devise three replacement rules and derive the maximum compression ratio. The experimental results show that the proposed compression algorithm leverages the group movement patterns to reduce the amount of delivered data effectively and efficiently.

INDEX TERMS

Data compression, distributed clustering, object tracking.

CITATION

Hsiao-Ping Tsai, De-Nian Yang, Ming-Syan Chen, "Exploring Application-Level Semantics for Data Compression",

*IEEE Transactions on Knowledge & Data Engineering*, vol.23, no. 1, pp. 95-109, January 2011, doi:10.1109/TKDE.2010.30REFERENCES

- [1] S.S. Pradhan, J. Kusuma, and K. Ramchandran, "Distributed Compression in a Dense Microsensor Network,"
IEEE Signal Processing Magazine, vol. 19, no. 2, pp. 51-60, Mar. 2002.- [2] A. Scaglione and S.D. Servetto, "On the Interdependence of Routing and Data Compression in Multi-Hop Sensor Networks,"
Proc. Eighth Ann. Int'l Conf. Mobile Computing and Networking, pp. 140-147, 2002.- [3] N. Meratnia and R.A. de By, "A New Perspective on Trajectory Compression Techniques,"
Proc. ISPRS Commission II and IV, WG II/5, II/6, IV/1 and IV/2 Joint Workshop Spatial, Temporal and Multi-Dimensional Data Modelling and Analysis, Oct. 2003.- [4] S. Baek, G. de Veciana, and X. Su, "Minimizing Energy Consumption in Large-Scale Sensor Networks through Distributed Data Compression and Hierarchical Aggregation,"
IEEE J. Selected Areas in Comm., vol. 22, no. 6, pp. 1130-1140, Aug. 2004.- [5] C.M. Sadler and M. Martonosi, "Data Compression Algorithms for Energy-Constrained Devices in Delay Tolerant Networks,"
Proc. ACM Conf. Embedded Networked Sensor Systems, Nov. 2006.- [6] Y. Xu and W.-C. Lee, "Compressing Moving Object Trajectory in Wireless Sensor Networks,"
Int'l J. Distributed Sensor Networks, vol. 3, no. 2, pp. 151-174, Apr. 2007.- [7] 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.- [8] 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, vol. 47, no. 2, pp. 146-153, June 2009.- [9] 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.- [10] 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.- [11] F. Giannotti, M. Nanni, F. Pinelli, and D. Pedreschi, "Trajectory Pattern Mining,"
Proc. ACM SIGKDD, pp. 330-339, 2007.- [12] 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.- [13] L. Chen, M. TamerÖzsu, and V. Oria, "Robust and Fast Similarity Search for Moving Object Trajectories,"
Proc. ACM SIGMOD, pp. 491-502, 2005.- [14] 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.- [15] 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.- [16] H.-P. Tsai, D.-N. Yang, W.-C. Peng, and M.-S. Chen, "Exploring Group Moving Pattern for an Energy-Constrained Object Tracking Sensor Network,"
Proc. 11th Pacific-Asia Conf. Knowledge Discovery and Data Mining, May 2007.- [17] C.E. Shannon, "A Mathematical Theory of Communication,"
J. Bell System Technical, vol. 27, pp. 379-423, 623-656, 1948.- [18] R. Agrawal and R. Srikant, "Mining Sequential Patterns,"
Proc. 11th Int'l Conf. Data Eng., pp. 3-14, 1995.- [19] J. Han, J. Pei, B. Mortazavi-Asl, Q. Chen, U. Dayal, and M. Hsu, "Freespan: Frequent Pattern-Projected Sequential Pattern Mining,"
Proc. ACM SIGKDD, pp. 355-359, 2000.- [20] 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.- [21] 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.- [22] 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.- [23] V. Guralnik and G. Karypis, "A Scalable Algorithm for Clustering Sequential Data,"
Proc. First IEEE Int'l Conf. Data Mining, pp. 179-186, 2001.- [24] J. Yang and W. Wang, "CLUSEQ: Efficient and Effective Sequence Clustering,"
Proc. 19th Int'l Conf. Data Eng., pp. 101-112, Mar. 2003.- [25] 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.- [26] 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.- [27] 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.- [28] "Stargate: A Platform x Project," http:/platformx.sourceforge. net, 2010.
- [29] "Mica2 Sensor Board," http:/www.xbow.com, 2010.
- [30] 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.- [31] J. Hightower and G. Borriello, "Location Systems for Ubiquitous Computing,"
Computer, vol. 34, no. 8, pp. 57-66, Aug. 2001.- [32] 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.- [33] D. Ron, Y. Singer, and N. Tishby, "Learning Probabilistic Automata with Variable Memory Length,"
Proc. Seventh Ann. Conf. Computational Learning Theory, July 1994.- [34] D. Katsaros and Y. Manolopoulos, "A Suffix Tree Based Prediction Scheme for Pervasive Computing Environments,"
Proc. Panhellenic Conf. Informatics, pp. 267-277, Nov. 2005.- [35] 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.- [36] 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-V562, 2004.- [37] E. Hartuv and R. Shamir, "A Clustering Algorithm Based on Graph Connectivity,"
Information Processing Letters, vol. 76, nos. 4-6, pp. 175-181, 2000.- [38] A. Strehl and J. Ghosh, "Cluster Ensembles—A Knowledge Reuse Framework for Combining Partitionings,"
Proc. Conf. Artificial Intelligence, pp. 93-98, July 2002.- [39] 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.- [40] G. Saporta and G. Youness, "Comparing Two Partitions: Some Proposals and Experiments,"
Proc. Computational Statistics, Aug. 2002.- [41] 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.- [42] D. Culler, D. Estrin, and M. Srivastava, "Overview of Sensor Networks,"
Computer, special issue on sensor networks, vol. 37, no. 8, pp. 41-49, Aug. 2004.- [43] 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.- [44] 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.- [45] 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.- [46] 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.- [47] 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.- [48] 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.- [49] G. Mathur, P. Desnoyers, D. Ganesan, and P. Shenoy, "Ultra-Low Power Data Storage for Sensor Networks,"
Proc. Fifth Int'l Conf. Information Processing in Sensor Networks, pp. 374-381, Apr. 2006.- [50] P. Dutta, D. Culler, and S. Shenker, "Procrastination Might Lead to a Longer and More Useful Life,"
Proc. Sixth Workshop Hot Topics in Networks, Nov. 2007.- [51] Y. Diao, D. Ganesan, G. Mathur, and P.J. Shenoy, "Rethinking Data Management for Storage-Centric Sensor Networks,"
Proc. Third Biennial Conf. Innovative Data Systems Research, pp. 22-31, Nov. 2007.- [52] F. Osterlind and A. Dunkels, "Approaching the Maximum 802.15.4 Multi-Hop Throughput,"
Proc. Fifth Workshop Embedded Networked Sensors, 2008.- [53] S. Watanabe, "Pattern Recognition as a Quest for Minimum Entropy,"
Pattern Recognition, vol. 13, no. 5, pp. 381-387, 1981.- [54] L. Yuan and H.K. Kesavan, "Minimum Entropy and Information Measurement,"
IEEE Trans. System, Man, and Cybernetics, vol. 28, no. 3, pp. 488-491, Aug. 1998.- [55] 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.- [56] D. Bolier, "SIM : A C++ Library for Discrete Event Simulation," http://www.cs.vu.nl/elienssim, Oct. 1995.
- [57] 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.- [58] 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.- [59] 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.- [60] C. Largeron-Leténo, "Prediction Suffix Trees for Supervised Classification of Sequences,"
Pattern Recognition Letters, vol. 24, no. 16, pp. 3153-3164, 2003. |