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dc.contributor.authorLiao, Shih-Kuanen_US
dc.identifier.citation1. Wen, C.-Y. and W.A. Sethares, “Automatic Decentralized Clustering for Wireless Sensor Networks”, EURASIP Journal on Wireless Communications and Networking, 2005. 5: p. 686-697. 2. Chen, Y.-C. and C.-Y. Wen, “Decentralized Cooperative TOA/AOA Target Tracking for Hierarchical Wireless Sensor Networks”, Sensors, 2012. 12(11): p. 15308-15337. 3. Li, J. and Q. Ren, “Compressing Information of Target Tracking in Wireless Sensor Networks”, Wireless Sensor Network, 2011. 3(2): p. 73-81. 4. Heinzelman, W.R., A. Chandrakasan, and H. Balakrishnan. “Energy-efficient communication protocol for wireless microsensor networks”, in System Sciences, 2000. Proceedings of the 33rd Annual Hawaii International Conference on. 2000. IEEE. 5. Chen, W.-P. and J.C. Adviser-Hou,“A Data-quality Driven Framework for Data Dissemination in Wireless Sensor Networks”. 2004: PhD Dissertation, University of Illinois at Urbana-Champaign. 6. Al Aghbari, Z., I. Kamel, and W. Elbaroni, “Energy-efficient distributed wireless sensor network scheme for cluster detection”, International Journal of Parallel, Emergent and Distributed Systems, 2013. 28(1): p. 1-28. 7. Kim, W., et al. “On target tracking with binary proximity sensors”, in Information Processing in Sensor Networks, 2005. IPSN 2005. Fourth International Symposium on. 2005. IEEE. 8. Shrivastava, N., R.M.U. Madhow, and S. Suri. “Target tracking with binary proximity sensors: fundamental limits, minimal descriptions, and algorithms”, in Proceedings of the 4th international conference on Embedded networked sensor systems. 2006. ACM. 9. Singh, J., et al. “Tracking multiple targets using binary proximity sensors”, in Proceedings of the 6th international conference on Information processing in sensor networks. 2007. ACM. 10. Wang, Z., E. Bulut, and B.K. Szymanski, “Distributed energy-efficient target tracking with binary sensor networks”, ACM Transactions on Sensor Networks (TOSN), 2010. 6(4): p. 32. 11. Arulampalam, M.S., et al., “A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking”, Signal Processing, IEEE Transactions on, 2002. 50(2): p. 174-188. 12. Gordon, N.J., D.J. Salmond, and A.F. Smith. “Novel approach to nonlinear/non-Gaussian Bayesian state estimation”, in IEE Proceedings F (Radar and Signal Processing). 1993. IET. 13. Savvides, A., C.-C. Han, and M.B. Strivastava. “Dynamic fine-grained localization in ad-hoc networks of sensors”, in Proceedings of the 7th annual international conference on Mobile computing and networking. 2001. ACM. 14. Bravos, G. and A.G. Kanatas. “Energy consumption and trade-offs on wireless sensor networks”, in Personal, Indoor and Mobile Radio Communications, 2005. PIMRC 2005. IEEE 16th International Symposium on. 2005. IEEE. 15. Heinzelman, W.B., A.P. Chandrakasan, and H. Balakrishnan, “An application-specific protocol architecture for wireless microsensor networks”, Wireless Communications, IEEE Transactions on, 2002. 1(4): p. 660-670. 16. Wang, A., et al., “Energy-scalable protocols for battery-operated microsensor networks”, Journal of VLSI signal processing systems for signal, image and video technology, 2001. 29(3): p. 223-237.en_US
dc.description.tableofcontents誌謝辭 i Abstract iii Contents iv List of Tables vi List of Figures vii 1. Introduction 1 1.1. Motivation 1 1.2. Relative Works 1 1.3. Contribution 3 1.3.1. Position Estimation Refinement 3 1.3.2. Proposed Data Processing Scheme 3 1.4. Organization of Thesis 4 2. Distributed Target Tracking System Configuration 5 2.1. Tasking Leader Selection 6 2.2. Choosing the Sub-Cluster Members 9 2.3. Target Positioning 11 2.3.1. Geometrical Positioning with Particle Filtering 11 3. Data Processing Schemes 13 3.1. TCAT 14 3.2. DCTTP 20 3.3. CTCI: Cooperative tracking via compressing information 24 3.4. Data Size Estimation 32 4. Simulation 33 4.1. The relationship between the simulation parameters in TCAT and CTCI 34 4.2. The Initial Simulation Setting 35 4.2.1. Average Error Distance, Average Energy Consumption and Average Data Size 36 4.3. Influence of Simulation Parameters 37 4.3.1. Number of Sensor Nodes 37 4.3.2. Transmission Range 40 4.3.3. Compressing Bound 42 4.3.4. Number of Sampling Points 47 4.3.5. Noise 49 4.3.6. Number of Sub-cluster Size 51 5. Conclusions 53 References 54zh_TW
dc.subjectinformation compressionen_US
dc.subjecttarget trackingen_US
dc.subjectparticle filteren_US
dc.subjectwireless sensor networken_US
dc.titleCompressing Information of Cooperative Target Tracking for Hierarchical Wireless Sensor Networksen_US
dc.typeThesis and Dissertationzh_TW
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item.openairetypeThesis and Dissertation-
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