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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 郭斯彥 | |
dc.contributor.author | Yi-Ming Tseng | en |
dc.contributor.author | 曾繹銘 | zh_TW |
dc.date.accessioned | 2021-06-16T06:54:19Z | - |
dc.date.available | 2024-07-21 | |
dc.date.copyright | 2014-07-29 | |
dc.date.issued | 2014 | |
dc.date.submitted | 2014-07-21 | |
dc.identifier.citation | [1] D. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory, vol. 52, no. 4, pp. 1289–1306, Apr. 2006.
[2] C. Luo, F. Wu, J. Sun, and C. W. Chen, “Compressive data gathering for large-scale wireless sensor networks,” in Proc. 2009 ACM Mobicom, pp. 145–156. [3] E. W. Dijkstra, “A note on two problems in connexion with graphs,” Numerische mathematik, vol. 1, no. 1, pp. 269–271, 1959. [4] D. B. Johnson, “Efficient algorithms for shortest paths in sparse networks,” J. Assoc. Comput. Mach., vol. 24, pp. 1-13, 1977. [5] R. W. Floyd, “Algorithm 97, shortest path,” Commun. Assoc. Comput. Mach., vol. 5, 1962. [6] P. Hart, N. Nilsson, and B. Raphael, “A formal basis for the heuristic determination of minimum cost paths,” IEEE Trans. Sys. Sci. Cybern., vol. SSC-4, no. 2, pp. 100-107, July 1968. [7] H. Zheng, S. Xiao, Xinbing Wang, X. Tian, M. Guizani, “Capacity and Delay Analysis for Data Gathering with Compressive Sensing in Wireless Sensor Networks,” IEEE Trans. Wireless Commun., vol. 12, no. 2 , pp. 917-927, 2012. [8] P. Gupta and P. R. Kumar, “The capacity of wireless networks,” IEEE Trans. Inf. Theory, vol. 46, no. 2, pp. 388–404, Mar. 2000. [9] L. Atzori, A.Iera, and G. Morabito, “The Internet of Things: A Survey,” Elsevier Computer Networks, vol. 54, no. 15, pp. 2787-2805, Oct. 2010. [10] E. Cand`es, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory, vol. 52, no. 2, pp. 489–509, Feb. 2006. [11] F. Xue and P. R. Kumar, “The number of neighbors needed for connectivity of wireless networks,” Wireless Networks, vol. 10, no. 2, pp. 169–181, Mar. 2004. [12] P. Santi, “On the data gathering capacity and latency in wireless sensor networks,” IEEE J. Sel. Areas Commun., vol. 28, no. 7, pp. 1211–1221, Sep. 2010. [13] S. Chen, Y. Wang, X. Li, and X. Shi, “Order-optimal data collection in wireless sensor networks: delay and capacity,” in 2009 IEEE SECON. [14] S. Shakkottai, X. Liu, and R. Srikant, “The multicast capacity of large multihop wireless networks,” IEEE/ACM Trans. Networking, vol. 18, no. 6, pp. 1691–1700, Dec. 2010. [15] X. Li, S. Tang, and O. Frieder, “Multicast capacity of large scale wireless ad hoc networks,” in Proc. 2007 ACM MobiCom, pp. 266–277. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/57617 | - |
dc.description.abstract | 現今社會中智慧裝置越來越多,這些裝置大多有無線網路連結,並執行著資料收集與特定內容播放。針對這些節點的相互聯結,控制以及操作,我們稱為物聯網相關技術。其中,內容的收集以及共用,在物聯網中是一個相當重要的議題。另一方面,壓縮感知的技術讓收集分散的資料變得更加有效率,並可以大幅減少資料量傳輸。在這篇論文中,我們使用提出一個路徑規劃的方法,結合壓縮感知演算法,以有效率的收集各地必要的資料。此方法在空間中存在有一些受訊號干擾而不能傳輸的區域時,還是能傳輸資料,不受太大的影響。且當訊號干擾不存在時,傳輸的速率規模維持不變,皆為θ(NW/M) 。我們也架構出網路資料傳輸速率的模型,並進行相關模擬,證實了我們的演算法的確具有較好的傳輸效率。 | zh_TW |
dc.description.abstract | Nowadays, more and more smart device appears in our society. Most of them connect to Internet. They collect data via Internet and play specific content. The technology of connecting, controlling and operating device, we call it Internet of Thing (IoT) technology. In this domain, how to collect and share content is a very important issue. On the other hand, Compressive Sensing (CS) provides a new method to collecting data more efficiently, and it largely decreases the data traffic. We propose a routing algorithm for single-sink data collection, which combine with CS, to collect needed file data efficiently, which the capacity is θ(NW/M). Our method can still work when there is some area can’t transmit data because of signal interference without losing too many data.
We also construct the model to calculate data transmission speed, and make the simulation. It proves that our algorithm has the better transmission efficiency indeed. | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T06:54:19Z (GMT). No. of bitstreams: 1 ntu-103-R01921052-1.pdf: 1993260 bytes, checksum: 182e9d501016a922d5d21807c9889bf2 (MD5) Previous issue date: 2014 | en |
dc.description.tableofcontents | 口試委員會審定書 #
誌謝 i 中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vi LIST OF ALGORITHM viii Chapter 1 Introduction 1 Chapter 2 Related Works 2 2.1 Internet of Things 2 2.2 Shortest Path Algorithm 3 2.3 Compressive Sensing 4 2.4 Data and Network Model 5 2.5 K2 - TDMA 7 2.6 Cell partition size 8 Chapter 3 Proposed Algorithm 9 3.1 Data gathering 9 3.2 Routing algorithm for head nodes 11 3.3 Pipeline 12 3.4 Dynamic adjust routing path 14 3.5 Capacity analysis 18 Chapter 4 Simulation and results 19 Chapter 5 Conclusion and Future Works 26 REFERENCES 27 | |
dc.language.iso | en | |
dc.title | 在不穩定無線感測網路下之資料收集路徑演算法 | zh_TW |
dc.title | Routing Algorithms for Data Gathering in Unreliable Wireless Sensor Networks | en |
dc.type | Thesis | |
dc.date.schoolyear | 102-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 顏嗣鈞,雷欽隆,陳英一,陳俊良 | |
dc.subject.keyword | 物聯網,壓縮感知,路徑選擇演算法,資料收集網路, | zh_TW |
dc.subject.keyword | IOT,Compressive sensing,routing algorithm,data collection network, | en |
dc.relation.page | 28 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2014-07-21 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
顯示於系所單位: | 電機工程學系 |
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ntu-103-1.pdf 目前未授權公開取用 | 1.95 MB | Adobe PDF |
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