以隨意散佈形成的無線感測器網路基於考慮在遮蔽效應的環境下之感測涵蓋範圍的研究

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研究生：	林典育 Tien-Yu Lin
論文名稱：	以隨意散佈形成的無線感測器網路基於考慮在遮蔽效應的環境下之感測涵蓋範圍的研究 A Study of Sensing Coverage for Randomly Distributed Wireless Sensor Networks in Shadowed Environments
指導教授：	蔡育仁 Yuh-Ren Tsai
口試委員:
學位類別：	碩士 Master
系所名稱：	電機資訊學院 - 通訊工程研究所 Communications Engineering
論文出版年：	2005
畢業學年度：	93
語文別：	英文
論文頁數：	44
中文關鍵詞：	感測器、涵蓋、遮蔽效應、隨意、散佈、無線
外文關鍵詞：	Sensor, Coverage, Shadowing, Random, Distributed, Wireless
相關次數：	點閱：3 下載：0
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本篇論文探討關於在無線感測器網路中，如何去計算涵蓋率。考慮我們把N個感測器以隨意一致性的方式佈置在一個大區域上，區域的面積假設是A，假設事件如果距離感測器一定範圍內、它可會被感測器所偵測到，而那個距離我們稱為感測半徑Rmax。對於在無線信號傳輸環境中，我們以最常用的路徑功率損失模型來決定感測半徑的大小；此外，因為信號在空氣中傳播會受到遮蔽物的影響，會使得接收端所收到的能量減少、而且根據遮蔽物的影響大小、收到的能量也會有很大的變動性，而遮蔽效應是無線信號傳輸一個無可避免的現象，所以在決定感測半徑時，我們也將遮蔽效應列入考量。因為感測器收到各個方向的信號、受到遮蔽物的影響都不太一樣，所以換一個角度，遮蔽效應會使得一個感測器它在四面八方的感測範圍都不一樣。
在本研究中，分別考慮沒有遮蔽效應的環境、和有遮蔽效的環境，我們會決定出感測涵蓋率和一些系統參數的關係、像是感測器個數、區域面積、涵蓋半徑…等。此外，為了在同樣的資源下增加涵蓋率，我們將整個區域分成所多小區域，然後在各個小區域上分別以隨意一致性的方式佈置同樣數目的感測器，這樣的想法是為了使感測器的分佈更加均勻，達到改善涵蓋率的效果。除了隨意一致性的分佈的討論，我們也在Poisson分佈和二維Gaussian分佈下去探討涵蓋率的估計。對於Poisson分佈和隨意一致性分布，我們驗證出兩者間一些相似的地方；另外在二維Gaussian分佈下，我們以Poisson的分佈來近似一小區域上感測器的分佈，以此方式估算涵蓋率。

Abstract                                   i
Contents                                  ii
1  Introduction                                       1
2  System & Channel Models                       4
    2.1    System Model   …………………………4
    2.2    Sensing Signal Propagation Model …4
3  Determination of Sensing Coverage                 8
    3.1 Uniformly Random Distribution  …………8
           3.1.1 Non-shadowed    Environment    ……8
           3.1.2   Shadowed    Environment   ……10
           3.1.3 Deployment on Partitioned Area
                          ………………………………12
        3.2 Numerical Approximation by Poisson Point
            Process       ………………………………14
           3.2.1 Non-shadowed    Environment   ……14
           3.2.2   Shadowed    Environment   ……15
        3.3 Uniformly Random Distribution v.s. Poisson
            Point Process      ……………………16
           3.3.1  Non-shadowed    Environment   ……17
           3.3.2   Shadowed    Environment   ……18
4  Coverage Determination under Bivariate Normal
   Distribution                                   19
    4.1 System Model  ………………………………19
        4.2 Determination of Sensing Coverage ……20
           4.2.1   Shadowed    Environment ………21
           4.2.2 Non-shadowed    Environment ………22
        4.3  Optimal Value of …………………………23
           4.3.1 Non-shadowed    Environment   ……23
           4.3.2   Shadowed    Environment   ……24
        4.4 One-Sided Bivariate Normal Distribution
                          ………………………………24
5  Discussions, Simulations & Numerical Results
                                            26
        5.1 Environmental Parameters and Simulation Setup
                          ………………………………26
    5.2 Accuracy Comparison for Uniformly Random
            Distribution     ………………………27
           5.2.1  Deployment on Un-partitioned Areas
                              …………………………27
           5.2.2  Deployment on  -partitioned Areas
                              …………………………29
           5.2.3  Simulation Accuracy     ……………30
    5.3 Numerical Coverage Comparison among
            Uniformly Random Distribution and Poisson
            Point Process     …………………………31
        5.4 Analysis of Some Numerical Results for
            Deployment on n-partitioned Areas   …33
           5.4.1  Numerical Results   ………………33
           5.4.2  Proportion of Conservation of Sensor
                  Nodes          …………………………34
           5.4.3  Comparisons of Sensing Coverage
               with Poisson Point Process   ………36
        5.5 Results Bivariate Normal Distribution 37
           5.5.1  Accuracy Comparison ………………37
           5.4.2  Optimal Value of Deployment
                  Standard Deviation        …………39
6  Conclusion and Future Works                  42
Bibliography                          43

                                

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