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系統識別號 U0026-2706201714502300
論文名稱(中文) 可充電式無線感測網路之穩健資料收集方法
論文名稱(英文) Robust Data Collection 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 105
學期 2
出版年 106
研究生(中文) 陳妍蓁
研究生(英文) Yen-Chen Chen
學號 R76054022
學位類別 碩士
語文別 中文
論文頁數 46頁
口試委員 指導教授-劉任修
口試委員-蔡青志
口試委員-胡政宏
中文關鍵字 無線感測網路  能源收集  穩健最佳化  資料收集樹  動態調整取樣頻率 
英文關鍵字 Energy Harvesting  Wireless Sensor Networks  Data Collection Tree  Robust Optimization  Adaptive rate assignment 
學科別分類
中文摘要 隨著再生能源的興起,可充電式無線感測網路能透過再生能源充電,解決
傳統無線感測器電量有限、壽命短的問題。然而再生能源的收穫量會隨時間波動且具不確定性,使得求解資料收集問題更加困難。為了讓感測網路能達到不間斷的運作,過去有許多研究提出改善方法,主要可分為動態調整取樣頻率及動態調整傳輸路徑,然而動態調整取樣頻率的表現會受限於使用的傳輸路徑結構,在某些再生能源收穫量極少的情境下取樣頻率的表現可能會極差;動態調整傳輸路徑則會在重新建構路徑結構時停止感測工作,且需要消耗電量在全域信號傳輸上。
本研究的目的在於建構一棵資料收集樹,能在再生能源收穫量極差的情境
下也有穩健的取樣頻率,避免在感測網路運作時需要重新調整傳輸路徑並將干擾與噪音納入考量,在模型中加入信號與跟干擾比(SINR)。而為了處理再生能源收穫量的不確定性,採用兩階段穩健最佳化,並使用Column-And-Constraint Generation (C&CG)演算法為基本架構進行求解。最後,本研究亦提出一個分散式演算法,能在資料收集樹以及再生能源收穫量已知的條件下,計算出取樣頻率。不只能更快速求解出穩健資料收集樹,更能在感測網路實際運行作時,根據再生能源收穫量動態調整取樣頻率,避免電池電量耗盡。
英文摘要 As renewable energy grows rapidly, energy-harvesting wireless sensor networks(EH-WSN) have drawn much attention in recent years. Energy-harvesting wireless sensor nodes can recharge power from renewable energy and enable the perpetual operations of sensor networks. However, the instability and uncertainty of renewable energy sources have imposed new challenges to data collection in EHWSN. In order to achieve perpetual operation, many studies have proposed methods to prolong the lifetime of EH-WSN. These methods can be roughly classified into two categories: adaptive sampling rates and dynamic routing structures. However,the former is sensitive to the routing structure and might perform poorly in some scenarios, while the latter requires global signaling and interrupts the operations of
data collection.
In this thesis, a method is proposed to construct a routing structure that works well even when the amount of renewable energy collected by sensor nodes is very
low. The proposed methods take interference and background noise into consideration via the use of signal to interference plus noise ratio (SINR) constrain into our model. To handle the instability and uncertainty of renewable energy, a two-stage robust optimization is formulated and the Column-And-Constrain-Generation(C&CG)
method is utilized to solve it. Besides, we proposed another distributed algorithm that adjusts the sensors sampling rate to avoid battery depletion during the operation. Additionally, the distributed algorithm can also be used to speed up the computation time of the two-stage robust optimization.
論文目次 目錄
摘要 i
EXTENDED ABSTRACT ii
誌謝 viii
目錄 ix
表目錄 xi
圖目錄 xii
1 緒論 1
2 相關文獻探討 5
2.1 動態調整取樣頻率 5
2.2 動態調整傳輸路徑 6
2.3 混合式方法及其他 7
2.4 穩健最佳化(Robust Optimization) 8
2.5 小結 9
3 研究方法 10
3.1 問題描述及模型介紹 11
3.1.1 問題描述及目標式 11
3.1.2 傳輸通道模型(Channel Model) 12
3.1.3 充電模型(Energy Model) 13
3.1.4 資料流與資料收集樹模型(Data Collection Tree Model) 14
3.2 兩階段穩健最佳化模型 15
3.2.1 主問題(Master Problem) 17
3.2.2 次問題(Sub-problem) 18
3.3 兩階段穩健資料收集樹 18
3.3.1 最佳化詞典編纂次序的取樣頻率分配演算法(Algorithm 1) 19
3.3.2 動態調整取樣頻率(Algorithm 2 & 3) 22

3.3.2.1 最大化取樣頻率演算法(Algorithm 2) 22
3.3.2.2 動態調整取樣頻率演算法(Algorithm 3) 23
3.3.3 穩健資料收集樹演算法(Algorithm 4) 28
4 實驗及結果探討 32
4.1 實驗環境建置與參數設定 32
4.1.1 實驗環境建置 32
4.1.2 實驗參數設定 32
4.2 實驗結果與分析 34
4.2.1 取樣頻率表現 34
4.2.2 執行時間 40
5 結論及未來發展 41
參考文獻 43
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