臺灣博碩士論文加值系統

在無線感應網路中如何使能量消耗最小並且達到延長網路生命週期是一個重要的議題。在本論文中，我們所討論的二階層無線網路是由許多小型的感應節點、以及數個應用節點和基地台所組成的。在過去的研究中Pan等人提出兩個演算法來求得在二階層無線網路中最佳的基地台位置，然而在他們所提出的演算法運算過程中必需假設所有的應用節點的初始能量和能量損耗參數必需要是一樣的，如果上述的初始能量或參數不相同時他們的演算法就無法運行。近幾年來，粒子群優演算法的技術被廣泛的應用在收尋近似最佳解的問題上並且有不錯的成績。在本論文中，我們提出幾個利用粒子群優演算法技術的演算法來收尋各種能量損耗公式下的近似最佳解的基地台位置。當各個應用節點擁有不同的初始能量、不同的傳送數率和不同的參數值時，我們所提出的方法可以在這種異質的無線感應網路中找到近似最佳解的基地台位置。實驗結果顯示出我們提出的方法在執行效果上有不錯的結果，我們也顯示出在不同參數的影響下的實驗結果。我們所提出的演算法可以在二階層無線網路中找到比較好的基地台配置位置，使能量的損耗變小並且達到網路生命週期最長的時間。

In wireless sensor networks, minimizing power consumption to prolong network lifetime is very crucial. In this paper, a two-tiered wireless sensor networks consisting of small sensor nodes, application nodes and base-stations is considered. In the past, Pan et al. proposed two algorithms to find the optimal locations of base stations in two-tiered wireless sensor networks. Their approaches assumed the initial energy and the energy-consumption parameters were the same for all application nodes. If any of the above parameters were not the same, their approaches could not work. Recently, the PSO technique has been widely used in finding nearly optimal solutions for optimization problems. In this paper, several algorithms based on particle swarm optimization (PSO) are proposed for general power-consumption constraints. The proposed approaches can search for nearly optimal BS locations in heterogeneous sensor networks, where application nodes may own different data transmission rates, initial energies and parameter values. Experimental results also show the good performance of the proposed PSO approaches and the effects of the parameters on the results. The proposed algorithm can thus help find good BS locations to reduce power consumption and maximize network lifetime in two-tiered wireless sensor networks.

CHAPER 1 INTRODUCTUON
CHAPER 2 REVIEW OF RELATED WORKS
CHAPER 3 REVIEW OF PARTICLE SWARM OPTIMIZATION
CHAPER 4 AGENERAL PSO ALGORITHM FOR THE N-OF-N LIFETIME PROBLEM
4.1 ALGORITHM
4.2 AN EXAMPLE
CHAPER 5 AGENERAL PSO ALGORITHM FOR THE K-OF-N LIFETIME PROBLEM
5.1 ALGORITHM
5.2 AN EXAMPLE
CHAPER 6 AGENERAL PSO ALGORITHM FOR THE m-IN-K-OF-N LIFETIME PROBLEM
6.1 ALGORITHM
6.2 AN EXAMPLE
CHAPER 7 AGENERAL PSO ALGORITHM FOR ALLOCATION OF MULTIPLE BASE STATIONS
7.1 ALGORITHM
7.2 AN EXAMPLE
CHAPER 8 EXPERIMENTAL RESULTS
8.1 EXPERIMENTAL RESULTS FOR N-of-N, K-of-N AND m-in-K-of-N LIFETIME
8.2 EXPERIMENTAL RESULTS FOR MULTIPLE BASE STATIONS
CHAPER 9 CONCLUSIONS AND FUTURE WORKSREFERENCES

[1] J. Chou, D. Petrovis and K. Ramchandran, “A distributed and adaptive
signal processing approach to reducing energy consumption in sensor
networks,” The 22nd IEEE Conference on Computer Communications
(INFOCOM), pp. 1054-1062, 2003.
[2] R. C. Eberhart and J. Kennedy, “A new optimizer using particles swarm
theory,” The Sixth International Symposium on Micro Machine and Human
Science, 1995, pp. 39-43.
[3] R. C. Eberhart and J. Kennedy, “Particle swarm optimization,” The IEEE
International Conference on Neural Networks, Vol. 4, 1995, pp. 1942-1948.
[4] Z. L. Gaing, “Discrete particle swarm optimization algorithm for unit
commitment,” The IEEE Power Engineering Society General Meeting, 2003.
[5] W. Heinzelman, J. Kulik, and H. Balakrishnan, “Adaptive protocols for
information dissemination in wireless sensor networks,” The Fifth ACM
International Conference on Mobile Computing and Networking, pp. 174-185,
1999.
[6] W. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “Energy-efficient
communication protocols for wireless microsensor networks,” The Hawaiian
International Conference on Systems Science, 2000.
[7] C. Intanagonwiwat, R. Govindan and D. Estrin, “Directed diffusion: a
scalable and robust communication paradigm for sensor networks,” The ACM
International Conference on Mobile Computing and Networking, 2000.
[8] Vikas Kawadia and P. R. Kumar, “Power control and clustering in ad hoc
networks,” The 22nd IEEE Conference on Computer Communications
(INFOCOM), 2003.
[9] N. Li, J. C. Hou and L. Sha, “Design and analysis of an mst-based
topology control algorithm,” The 22nd IEEE Conference on Computer
Communications (INFOCOM), 2003.
[10] S. Lee, W. Su and M. Gerla, “Wireless ad hoc multicast routing with
mobility prediction,” Mobile Networks and Applications, Vol. 6, No. 4,
pp. 351-360, 2001.
[11] D. Niculescu and B. Nath, “Ad hoc positioning system (APS) using AoA,”
The 22nd IEEE Conference on Computer Communications (INFOCOM), pp. 1734-
1743, 2003.
[12] J. Pan, Y. Hou, L. Cai, Y. Shi and X. Shen, “Topology control for
wireless sensor networks,” The Ninth ACM International Conference on
Mobile Computing and Networking, pp. 286-299, 2003.
[13] J. Pan, L. Cai, Y. T. Hou, Y. Shi and S. X. Shen, "Optimal base-station
locations in two-tiered wireless sensor networks," IEEE Transactions on
Mobile Computing, Vol. 4, No. 5, pp. 458-473, 2005.
[14] Y. Q. Qin, D. B. Sun, N. Li and Y. G. Cen, “Path planning for mobile
robot using the particle swarm optimization with mutation operator,” The
IEEE Third International Conference on Machine Learning and Cybernetics,
pp. 2473-2478 2004.
[15] V. Rodoplu and T. H. Meng, “Minimum energy mobile wireless networks,”
IEEE Journal on Selected Areas in Communications, Vol. 17, No. 8, 1999.
[16] R. Ramanathan and R. Hain, “Topology control of multihop wireless
networks using transmit power adjustment,” The 19th IEEE Conference on
Computer Communications (INFOCOM), 2000.
[17] Y. Shi and R. C. Eberhart, “A modified particle swarm optimizer,” The
IEEE International Conference on Evolutionary Programming, 1998, pp. 69-
73.
[18] A. Stacey, M. Jancic and I. Grundy, “Particle swarm optimization with
mutation,” The IEEE Congress on Evolutionary Computation, 2003, pp. 1425-
1430.
[19] D. Tian and N. Georganas, “Energy efficient Routing with guaranteed
delivery in wireless sensor networks,” The IEEE Wireless Communication &
Networking Conference, 2003.
[20] F. Ye, H. Luo, J. Cheng, S. Lu and L. Zhang, “A two-tier data
dissemination model for large scale wireless sensor networks,” The ACM
International Conference on Mobile Computing and Networking, 2002.