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系統識別號 U0026-3107201815085300
論文名稱(中文) 可充電無線感測網路下同時考慮再生能源管理與傳輸效率之資料收集樹建立方法
論文名稱(英文) A Joint Energy Management and Data Collection Algorithm for Energy-Harvesting Wireless Sensor Networks
校院名稱 成功大學
系所名稱(中) 資訊管理研究所
系所名稱(英) Institute of Information Management
學年度 106
學期 2
出版年 107
研究生(中文) 吳昭典
研究生(英文) Zhao-Dian Wu
學號 R76051105
學位類別 碩士
語文別 中文
論文頁數 64頁
口試委員 指導教授-劉任修
口試委員-胡政宏
口試委員-張裕清
中文關鍵字 無線感測網路  再生能源  資料收集率  傳輸路徑  資料收集樹 
英文關鍵字 Wireless Sensor Networks  Energy-Harvesting  Adaptive Data Collection Rate  Routing Structure  Data Collection Tree 
學科別分類
中文摘要 近年來能源採集技術逐漸成熟,越來越多無線感測器都可以藉由轉換不同的能源對電池進行充電,讓整體無線感測網路能夠透過感測器的充電來延長網路生命。然而採集能源收穫量會因為無線感測器分布之地點、時間差異而有所不同,因此規劃良好的資料收集方法變得更加困難。無線感測網路為了能夠永續運作,會透過不同的方式來延長感測器之壽命,來避免感測器耗盡電池電量而造成部分連結中斷以及資料收集不完整。此外,為了提升網路效能,許多研究都將整體網路不中斷作為必要條件,將感測器的電量有效地利用及分配,使感測器可以輸出最大的資料收集率,藉此提升整體網路資料收集量。

本研究將再生能源當作電量來源,目的是在再生能源收穫量已知的前提下,快速地建立一棵資料收集樹以及各感測器相對應的資料收集率。該資料收集樹中每一個感測器在所有的工作時段內皆不會耗盡自身電池電量,且輸出最大資料收集率,即為整體網路的資料收集率辭典編纂排序最大化。而本論文提出的建樹方法為分散式的啟發式演算法,首先使用一任意樹作為起始樹狀結構,接著過透過不斷提升感測器節點的資料收集率來調整樹狀結構,加快建樹速度並提升資料收集率之辭典編纂排序。

最後實驗顯示本研究提出的演算法能夠加速求解時間,並且能夠求得一組高辭典編纂排序的資料收集率,而隨著無線感測網路越大,能夠加速的時間也越多。
英文摘要 With the new energy-harvesting technology, wireless sensors can recharge batteries by converting energy from different kinds of renewable sources. Then the energy-harvesting wireless networks (EH-WSN) can prolong lifetime by recharging batteries of wireless sensors. However, the renewable energy will be different based on the locations and the time periods of wireless sensors. Therefore, if there is no efficient method to manage and utilize renewable energy, some sensors will run out of battery and interrupt the operations of data collection.

In this thesis, a data collection algorithm is proposed to compute a routing structure and a high lexicographic rate assignment rapidly under the premise that the renewable energy data are given. The routing structure is constructed by adjusting the data collection rate and routing structure.

Besides network lifetime, data collection rate is also an important criterion. Most studies focus on raising the data collection rate under the premise that wireless sensor network won't be terminated due to depletion of some batteries. And these methods can roughly be classified into two categories: maximizing the total data collection rate of all wireless sensors and maximizing lexicographic data collection rate assignment.

In the routing structure, each wireless sensor will not deplete the battery at any time between one day. The algorithm we proposed to construct the routing structure.
論文目次 摘要i
EXTENDED ABSTRACT ii
誌謝vii
目錄viii
表目錄x
圖目錄xi
1 緒論1
1.1 研究背景. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 研究動機. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 研究貢獻. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.4 論文架構. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2 相關文獻探討6
2.1 調整資料收集率. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2 調整傳輸路徑. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.3 混合式及其他方法. . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.4 小結. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3 研究方法12
3.1 問題描述及模型介紹. . . . . . . . . . . . . . . . . . . . . . . . . . 12
3.1.1 問題描述及目標式. . . . . . . . . . . . . . . . . . . . . . . 13
3.1.2 最佳化辭典編纂排序之資料收集率分配演算法. . . . . . . 17
3.2 分散式資料收集率配置方法. . . . . . . . . . . . . . . . . . . . . . 20
3.2.1 最大化資料收集率演算法. . . . . . . . . . . . . . . . . . . 21
3.2.2 資料收集率分配演算法. . . . . . . . . . . . . . . . . . . . . 23
3.3 資料收集率辭典編纂排序最大化建樹演算法. . . . . . . . . . . . . 29
3.3.1 資料收集樹建置方法. . . . . . . . . . . . . . . . . . . . . . 29
3.3.2 感測器節點轉傳必要條件. . . . . . . . . . . . . . . . . . . 32
4 實驗與結果探討38
4.1 實驗環境建置與參數設定. . . . . . . . . . . . . . . . . . . . . . . . 38
4.1.1 實驗環境建置. . . . . . . . . . . . . . . . . . . . . . . . . . 38
4.1.2 實驗參數設定. . . . . . . . . . . . . . . . . . . . . . . . . . 39
4.2 實驗結果與分析. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4.2.1 30個感測器節點之網路. . . . . . . . . . . . . . . . . . . . . 41
4.2.2 40個感測器節點之網路. . . . . . . . . . . . . . . . . . . . . 44
4.2.3 50個感測器節點之網路. . . . . . . . . . . . . . . . . . . . . 46
4.2.4 60個感測器節點之網路. . . . . . . . . . . . . . . . . . . . . 49
4.2.5 執行時間與感測器累積分布函數圖. . . . . . . . . . . . . . 51
4.2.6 實驗總結. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
5 結論及未來發展54
參考文獻55
附錄一59
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