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研究生:賴勇勳
研究生(外文):Yong-Hsun Lai
論文名稱:在無線感測網路下以等級式資料聚集之研究
論文名稱(外文):Level-based Data Aggregation Method in Wireless Sensor Networks
指導教授:朱鴻棋朱鴻棋引用關係
指導教授(外文):Hung-Chi Chu
學位類別:碩士
校院名稱:朝陽科技大學
系所名稱:網路與通訊研究所
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:中文
論文頁數:76
中文關鍵詞:等級式的資料聚集自動化路由機制無線感測網路
外文關鍵詞:level-based data aggregationWireless sensor networkautomatic routing mechanism
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近年來隨著微機電系統和無線網路通訊技術的進步,使得無線感測網路的誕生,無線感測網路是由數百或成千上萬個感測節點所組成,在無線感測網路中,由於感測節點的感測範圍有互相重疊的情形,同一事件發生可能觸發多個感測節點進行資料回報,如果不先經過資料聚集的處理,則會造成資料重覆的傳送和不必要的能源消耗。而且感測節點一般都受到能源的限制,因此為了要減少感測節點的能源消耗,所以資料聚集的採用是必要的,因為透過資料聚集不但可以減少不必要的資料傳輸又可以減少資料量,以達到節能的效果。在本論文中,我們提出一個自動化路由機制的概念和等級式的資料聚集方法來偵測特定區域的溫度變化,將感測節點所偵測到的溫度以等級的方式來表示。當感測節點收集到一定數量的資料時,就會將資料進行融合與壓縮後再傳送到匯集點或資料收集中心,達到縮短資料量、減少傳送次數和節能的效果。若溫度異常的變化,感測節點會立刻將所偵測到的溫度值回傳到資料收集中心,讓使用者可以迅速的得到異常資訊並做適當的處置。實驗模擬使用網路模擬器,模擬結果顯示我們提出的方法能有效的減少傳輸所消耗的能源和頻寬的使用。
In recent years, the rapid advances in Micro-electromechanical System (MEMS) and wireless communication have made wireless sensor networks possible. Wireless sensor networks (WSNs) are composed of hundreds or thousands of sensor nodes. In wireless sensor networks, as result of sensing range of sensor nodes may overlap each other. The same event will trigger the response from many nearby sensor nodes and transmit data to sink. If sensor nodes receive data without data aggregation process, it will cause the repetition of data transmission and the unnecessary energy consumption. Therefore, in order to reduce the energy consumption, it is essential to adopt the data aggregation technique. This is because that data aggregation technique can effectively reduce the unnecessary transmission and the size of transmission data. These effects cause the energy saving effect. In this thesis, we proposed an automatic routing mechanism and level-based data aggregation method to detect the variety of temperature within a specific area. According to the temperature that sensed by sensor nodes, it can be expressed by the level degree. When sensor nodes collect a certain amount of data, they perform the data of aggregation, compression process and then transmit it to the sink node. This process can reduce the transmission data size and the number of communication with energy-saving. If the sensed data is exceptional, this emergent data will be transmitted to sink node immediately to notify the manager. By using network simulator version 2 (NS-2) in our simulation, it shows that the proposed method can effective reduce the energy consumption of transmission and bandwidth utilization.
中文摘要 ................................................I
Abstract .................................................II
誌謝 ...................................................IV
目錄 ....................................................V
表目錄 .................................................VII
圖目錄 ................................................VIII
第一章、緒論 ..............................................1
1.1 前言 .............................................1
1.2 研究動機與目的 .................................2
1.3 論文架構 .....................................2
第二章、文獻探討 ...............................3
2.1 無線感測網路概念 .................................3
2.2 無線感測網路之應用與硬體架構 .....................5
2.3 資料聚集之研究 ...................................8
2.4 無線路由之研究 ..................................11
2.4.1 無基礎式需求距離向量路由協定 (AODV) .....16
2.4.2 以最短路徑優先 (Shortest Path First) ..........20
2.4.3 以叢集為基礎 (Cluster-based) ................22
2.4.4 以鏈為基礎 (Chain-based) ...................27
2.4.5 以網格的方式 (Grid Method) .................29
2.4.6 以擴散的方式 (Diffusion Method) .............31
2.4.7 影響無線路由之因素 .......................33
第三章、自動化路由機制 ...................................35
第四章、等級式的資料聚集方法 .............................42
第五章、實驗與模擬分析 ...................................48
5.1 模擬架構 ........................................48
5.2 自動化路由機制的效能分析 ........................50
5.3 等級式資料聚集分析 ..............................53
5.3.1 模擬架構 ...................................54
5.3.2 效能分析 ...................................54
第六章、結論 .............................................70
參考文獻 ................................................71

表目錄
表 5-1、模擬參數設定值 ...................................49

圖目錄
圖 2-1、無線感測網路之基本架構 ............................5
圖 2-2、感測節點之硬體架構 ................................7
圖 2-3、(a)未使用資料聚集技術和(b)使用資料聚集技術 .........10
圖 2-4、無線電之能源消耗模組 .............................12
圖 2-5、(a)位置中心導向和(b)資料中心導向的路由方式 .........14
圖 2-6、最靠近資料收集中心的聚集方法(CNS)之運作 ..........14
圖 2-7、(a)路徑請求的運作 (b)路徑回覆的回應以及(c)資料的傳送.19
圖 2-8、以角度為基礎的路由圖 .............................21
圖 2-9、(a)不考慮節點剩餘能源和(b)考慮節點的剩餘能源 .......23
圖 2-10、低能源叢集的階層架構 ............................24
圖 2-11、階層式叢集架構 ..................................27
圖 2-12、鏈狀架構(PEGASIS) ...............................28
圖 2-13、兩階層資料散佈之運作 ............................31
圖 2-14、有向性散佈的運作示意圖 ..........................32
圖 3-1、網路拓樸之示意圖 .................................36
圖 3-2、感測節點之狀態轉換圖 .............................37
圖 3-3、虛擬鏈結之例子 ...................................39
圖 3-4、資料聚集的路由排程設計 ...........................40
圖 4-1、感測節點所偵測到的溫度值 .........................44
圖 4-2、等級值的傳送 .....................................45
圖 4-3、替換值的傳送 .....................................46
圖 4-4、使用者利用等級值和替換值轉換回真實溫度值 .........47
圖 5-1、AODV 與ARM 傳送延遲時間之比較 ..................51
圖 5-2、AODV 與ARM 通訊鏈結數之比較 ....................52
圖 5-3、AODV 與ARM 能源消耗之比較 ......................53
圖 5-4、AODV、CNS、ARM+LDA 傳送延遲時間之比較 .........55
圖 5-5、AODV、CNS、ARM+LDA 通訊鏈結數之比較 ...........57
圖 5-6、AODV、CNS、ARM+LDA 能源消耗之比較 .............58
圖 5-7、等級2、5、8、10 的表示方法 ...........................60
圖 5-8、等級2、5、8、10 所消耗的能源 .........................61
圖 5-9、等級5、8、10 所消耗的能源 ...........................61
圖 5-10、等級5 之2 次式迴歸模型 ...........................62
圖 5-11、等級8 之2 次式迴歸模型 ............................63
圖 5-12、等級10 之2 次式迴歸模型 ..........................63
圖 5-13、等級5、8、10 之2 次式迴歸模型 ......................64
圖 5-14、等級2、5、10的表示方法 ............................65
圖 5-15、等級2、5、10所消耗的能源 ..........................66
圖 5-16、等級2 之2 次式迴歸模型 ...........................67
圖 5-17、等級5 之2 次式迴歸模型 ...........................68
圖 5-18、等級10之2次式迴歸模型 ...........................68
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