Representative Tree Selection

In this experiment, we evaluate the performance of two metrics of representative tree selection and vary variable ρ between [0,1] to show the flexibility for applications. In Figure 7.10, the comparison between representative tree selection metrics and the effec-tiveness of variable ρ are presented. As seen, the reduction rate increases as variableρ increases. If we only favor the storage cost of emission tree and set variable ρ to be 1, we can obtain the reduction rate which is higher than others. Inversely, we can obtain the hit rate of prediction which is higher than others if the variable ρ is set to be 0. For metric 1, its performance is very close to the performance of metric 2 with ρ = 0.5. It supports that metric 2 really enjoy the flexibility between reduction rate and hit rate of prediction.

74

Figure 7.10: The impact of variable ρ.

Chapter 8 Conclusion

In this paper, we consider how to reduce the huge storage cost resulted from storing emis-sion trees. For the sake of saving storage, we propose the framework GBOT to perform group-based object tracking for HTM. There are mainly three steps to be executed. To clustering objects with similar moving behaviors, we first define the dissimilarity among emission trees to distinguish the moving behaviors of objects. Based on such dissimi-larity measures, we formulate two clustering schemes, reactive grouping and proactive grouping, to group objects reactively or proactively. In order to select the representative emission tree for a group, two metrics are provided to further reduce the storage cost and increase the prediction accuracy. Then, we develop a group VMM model to adequately train the representative emission trees. In addition, a maintenance algorithm is proposed to maintain the quality of groups. We also conduct several experiments to evaluate the performance of GBOT. The experimental results show GBOT not only effectively reduce the storage cost but preserve the prediction accuracy.

Bibliography

[1] J. A. Aslam, Z. J. Butler, F. Constantin, V. Crespi, G. Cybenko, and D. Rus. Track-ing a movTrack-ing object with a binary sensor network. In ProceedTrack-ing of ACM In-ternational Conference on Embedded Networked Sensor Systems (SenSys), pages 150–161, 2003.

[2] J.-C. Cano and P. Manzoni. Group mobility impact over tcp and cbr traffic in mobile ad hoc networks. In Proceeding of Euromicro Workshop on Parallel, Distributed and Network-Based Processing (PDP), pages 382–389, 2004.

[3] W.-P. Chen, J. C. Hou, and L. Sha. Dynamic clustering for acoustic target tracking in wireless sensor networks. In Proceeding of IEEE International Conference on Network Protocols (ICNP), pages 284–294, 2003.

[4] M. Chu, H. Haussecker, and F. Zhao. Scalable information-driven sensor querying and routing for ad hoc heterogeneous sensor networks. International Journal on High Performance Computing Applications, 16(3), 2002.

[5] C. Gui and P. Mohapatra. Power conservation and quality of surveillance in tar-get tracking sensor networks. In Proceeding of ACM International Conference on Mobile Computing and Networking (MobiCom), pages 129–143, 2004.

[6] X. Hong, M. Gerla, G. Pei, and C. Chiang. A group mobility model for ad hoc wireless networks. In Proceeding of ACM International Workshop on Modeling Analysis and Simulation of Wireless and Mobile Systems (MSWiM), pages 53–60, 1999.

[7] J.-L. Huang, M.-S. Chen, and W.-C. Peng. Exploring group mobility for replica data allocation in a mobile environment. In Proceeding of ACM International Conference on Information and Knowledge Management (CIKM), pages 161–168, 2003.

[8] H. T. Kung and D. Vlah. Efficient location tracking using sensor networks. In Pro-ceeding of IEEE Wireless Communications and Networking Conference (WCNC), volume 3, pages 16–20, March 2003.

[9] K. Oflazer. Error-tolerant tree matching. In Proceeding of International Conference on Computational Linguistics (COLING), pages 860–864, 1996.

[10] K. Oflazer. Error-tolerant retrieval of trees. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(12):1376–1380, 1997.

[11] S. Pattem, S. Poduri, and B. Krishnamachari. Energy-quality tradeoffs for target tracking in wireless sensor networks. In Proceeding of IEEE International Confer-ence on Information Processing in Sensor Networks (IPSN), pages 32–46, 2003.

[12] W.-C. Peng, Y.-Z. Ko, and W.-C. Lee. On mining moving patterns for object track-ing sensor networks. In Proceedtrack-ing of International Conference on Mobile Data Management (MDM), page 41, 2006.

[13] 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. In Proceeding of Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2007.

[14] K. Wang and B. Li. Group mobility and partition prediction in wireless ad-hoc ntworks. In Proceeding of IEEE International Conference on Communications (ICC), 2002.

[15] Q. Wang, W.-P. Chen, R. Zheng, K. Lee, and L. Sha. Acoustic target tracking using tiny wireless sensor devices. In Proceeding of IEEE International Conference on Information Processing in Sensor Networks (IPSN), pages 642–657, 2003.

[16] Y. Xu, J. Winter, and W.-C. Lee. Dual prediction-based reporting for object track-ing sensor networks. In Proceedtrack-ing of International Conference on Mobile and Ubiquitous Systems (MobiQuitous), pages 154–163, 2004.

[17] Y. Xu, J. Winter, and W.-C. Lee. Prediction-based strategies for energy saving in object tracking sensor networks. In Proceeding of International Conference on Mobile Data Management (MDM), pages 346–357, 2004.

[18] H. Yang and B. Sikdar. A protocol for tracking mobile targets using sensor net-works. In Proceeding of IEEE International Workshop on Sensor Networks Proto-cols and Applications, pages 71–81, 2003.

[19] J. Yang and W. Wang. Agile: A general approach to detect transitions in evovling data streams. In Proceeding of IEEE International Conference on Data Mining (ICDM), pages 559–562, 2004.

[20] W. Zhang and G. Cao. Dctc: Dynamic convoy tree-based collaboration for tar-get tracking in sensor networks. IEEE Transactions on Wireless Communications, pages 1689–1701, 2004.

[21] W. Zhang and G. Cao. Optimizing tree reconfiguration for mobile target tracking in sensor networks. In Proceeding of IEEE International Conference on Computer Communications (INFOCOM), pages 2434–2445, 2004.

[22] F. Zhao, J. Shin, and J. Reich. Information-driven dynamic sensor collaboration