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研究生:劉政宏
研究生(外文):Cheng-Hung Liu
論文名稱:在可移動式無線感測網路中的非均勻移動布置演算法
論文名稱(外文):A Moving Algorithm for Non-Uniform Deployment in Mobile Sensor Networks
指導教授:斯國峰
指導教授(外文):Kuo-Feng Ssu
學位類別:碩士
校院名稱:國立成功大學
系所名稱:電腦與通信工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2008
畢業學年度:96
語文別:英文
論文頁數:30
中文關鍵詞:系統使用時間無線感測網路
外文關鍵詞:network lifetimesensor networks
相關次數:
  • 被引用被引用:0
  • 點閱點閱:86
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  • 下載下載:9
  • 收藏至我的研究室書目清單書目收藏:0
均勻布置對於無線感測網路來說,並非最佳的節能方法,較好的模式是離資料蒐集中心最近的位置,布置最高密度的無線感測器。在這篇論文裡,我們提出了一個針對非均勻布置的演算法(MAND) ,藉由使用MAND,每個無線感測器可移動至一個可延長系統使用時間的位置。根據MAND,EMAND 更進一步提升網路的偵測範圍與系統使用時間。在最後的模擬結果也顯示,EMAND 在較多感測物件的環境中,有相當好的效能。
Previous research mentioned that uniform deployment was not the most efficient way for saving energy in wireless sensor networks. The location nearer to a sink should
have the higher density of sensors. In this paper, a moving algorithm for the nonuniform deployment (MAND) is introduced. By using MAND, mobile sensors can reach
appropriate locations to prolong the system lifetime. With two refinements on MAND (EMAND), not only the coverage of the network is maintained, but the lifetime can be improved enormously. Simulation results show that EMAND performs well even in the environment with more sensing events.
Chapter
1 Introduction 1
2 Related Work 4
2.1 Voronoi Cell 4
2.2 Movement-Assisted Sensor Deployment 5
2.3 Non-Uniform Sensor Deployment 6
3 Moving Algorithm for Non-uniform Deployment 7
3.1 Assumption and Definition 7
3.2 MAND 8
3.2.1 Getting neighboring information 8
3.2.2 Finding Ri 8
3.2.3 Target location 10
3.2.4 Termination 11
3.3 Extended MAND 12
3.3.1 Accelerating the concentration 12
3.3.2 Normalization 14
4 Simulation 16
4.1 Environment and Metrics 16
4.1.1 Environment setup 16
4.1.2 Metrics 17
4.2 Simulation Results 18
5 Conclusion and Future Work 26
5.1 Conclusion 26
5.2 Future Work 26
References 28
Vita 30
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2006.
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[6] G. Wang, G. Cao, and T. F. La Porta, “Movement-assisted sensor deployment,” in International Conference on Computer Communication, March 2004, pp. 2469–2479.
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[8] R. Subramanian and F. Fekri, “Sleep scheduling and lifetime maximization in sensor networks: fundamental limits and optimal solutions,” in Information Processing in Sensor Networks, April 2006, pp. 218–225.
[9] F. Aurenhammer, “Voronoi diagrams—a survey of a fundamental geometric data structure,” vol. 23, no. 3, pp. 345–405, September 1991.
[10] Fortune, “Voronoi diagrams and delaunay triangulations,” in Computing in Euclidean
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[11] N. Heo and P. K. Varshney, “Energy-efficient deployment of intelligent mobile sensor networks,” IEEE Transactions on Systems, Man, and Cybernetics, Part A, vol. 35, no. 1, pp. 78–92, January 2005.
[12] H.-C. Jang and H.-C. Lee, “A voronoi detection range adjustment (vera) approach for energy saving of wireless sensor networks,” in International Conference on Parallel
and Distributed Systems, vol. 1, December 2007, pp. 1–7.
[13] A. Boukerche and X. Fei, “A voronoi approach for coverage protocols in wireless sensor networks,” in IEEE Global Telecommunications Conference, December 2007,
pp. 5190–5194.
[14] W.-P. Chen, J. C. Hou, and L. Sha, “Dynamic clustering for acoustic target tracking in wireless sensor networks,” IEEE Transactions on Mobile Computing, vol. 3, no. 3, pp. 258–271, Aug 2004.
[15] M. Kushwaha, K. Molnar, J. Sallai, P. Volgyesi, M. Maroti, and A. Ledeczi, “Sensor node localization using mobile acoustic beacons,” in IEEE International Conference
on Mobile Adhoc and Sensor Systems Conference, November 2005.
[16] S. Mao and Y. T. Hou, “Beamstar: An edge-based approach to routing in wireless sensor networks,” IEEE Transactions on Mobile Computing, vol. 6, no. 11, pp. 1284–1296, November 2007.
[17] C.-H. Ou and K.-F. Ssu, “Sensor position determination with flying anchors in threedimensional
wireless sensor networks,” IEEE Transactions on Mobile Computing, vol. 7, no. 8, August 2008.
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