由於無線能量傳輸技術的進步，我們能透過無線充電車（Mobile chargingvehicle）替感測器的電池進行充電，而如何設計並優化無線充電車的充電排程，從而延長無線可充感測網路（Wireless rechargeable sensor network）的整體壽命及提升網路吞吐量（Throughput）成為現今多數研究的重點，然而，在過往多半假設感測器的感測率（Sensing rate）為固定不變，這會造成感測器無法因為電量不足而適時調降感測率以便可以爭取更多時間來等待無線充電車前來充電，從而導致感測器的存活數目大幅下降、網路壽命縮短及網路吞吐量降低等問題發生，此外，部分研究也指出感測器的感測率是具自適應（Adaptive sensing rate），也就是感測器可以依據不同需求（例如：偵測到事件或進入省電模式）來動態調控其感測率。
　　基於上述考量，本論文針對無線充電車提出一套稱為 Enhance-SimulatedAnnealing based on Adaptive Sensing Rate（ESA-ASR）的路徑規劃與充電排程之演算法，首先，為了有效減少無線充電車於感測器間移動的距離，我們以模擬退火演算法（Simulated annealing）計算無線充電車的初始行駛路徑，接著，為了使無線充電車有效率選擇充電目標，我們考量拜訪感測器的移動距離及生命週期，透過 Preemptive Energy-Drained Sensor（PEDS）方法替能量即將耗盡的感測器優先進行電量補充，此外，我們也考量網路中感測器平均能量消耗的速率，並設計稱為 Energy-Balanced Dynamic Charging Strategy（EBDCS）方法以提升節點存活數目以及平均分配電源補充量，實驗結果顯示，我們所提出的 ESA-ASR 演算法可以有效提升網路整體吞吐量，並且提高存活節點數目。

Thanks to the advancement of wireless energy transmission technology, we can chargethe batteries of sensors by using a mobile charging vehicle (MCV). To extend thelifetime of a wireless rechargeable sensor network and also increase its throughput, howto efficiently calculate the moving path and charging schedule of a MCV is critical.However, most of existing studies assume that each sensor has the same (constant)sensing rate. In practice, sensors can dynamically adjust their sensing rates. Forexample, they can lower sensing rates in case of insufficient energy, so as to extendtheir lifetime and gain more time to wait the MCV for recharging them. Withoutconsidering this property, many sensors may die quickly, leading to poor throughputand shorter network lifetime. Moreover, some research efforts also point out that theabove adaptive sensing rate is natural, as sensors can vary their sensing rates based ondifferent requirements (e.g., detecting events or going to a sleeping mode).
Based on the above motivations, this paper proposes an enhance-simulated annealingbased on adaptive sensing rate (ESA-ASR) algorithm to schedule both moving path andcharging behavior of a MCV. First, to reduce the total moving distance that the MCVmoves to visit sensors, we use simulated annealing to calculate the initial path of theMCV. Then, to help the MCV efficiently select the charging target, we propose apreemptive energy-drained sensor (PEDS) method which refers to the moving cost ofthe MCV and also the lifetimes of the visited sensors. It gives a high priority to chargethose sensors which are about to run out of energy. Besides, we also develop an energybalanced dynamic charging strategy (EBDCS) method, which can adaptively decidethe amount of energy that the MCV recharges to each sensor, so as to balance sensors’venergy. Through simulation, we show that our proposed ESA-ASR algorithm caneffectively improve the overall throughput, and also increase the number of alivesensors.

目錄
論文審定書 i
誌謝 ii
摘要 iii
Abstract iv
目錄 vi
圖次 viii
表次 x
第一章 導論 1
1. 1無線可充電感測網路 1
1. 2研究動機 2
1. 3論文貢獻與章節架構 3
第二章 背景知識 5
2. 1無線行動充電車的數量 5
2. 2無線行動充電車之充電模式 7
2. 3服務站的佈署方式 9
2. 4無線行動充電車之充電週期 9
第三章 相關文獻探討 11
第四章 網路模型與問題定義 15
4. 1網路環境 15
4. 2能量消耗模型 15
4. 3問題定義 16
第五章 研究方法 18
5. 1網路初始化 20
5. 2模擬退火演算法 22
5. 3 PEDS方法 24
5. 4 EBDCS方法 26
第六章 模擬結果與分析 28
6. 1模擬環境與參數設定 28
6. 2 ESA-ASR與現有充電排程方法之比較 29
6.2.1網路吞吐量之比較 29
6.2.2死亡節點數之比較 33
6. 3 ESA-ASR與SA存活節點比例與節點能量分布之比較 38
6. 4 ESA-ASR與SA存活節點位置散佈圖之比較 45
6. 5 ESA-ASR參數對於實驗結果之影響 48
6.5.1 PEDS電量門檻百分比∆E之影響 48
6.5.2 EBDCS門檻值δ之影響 50
6.5.3 EBDCS電池容量權重ε之影響 51
6.5.4 EBDCS浮動電量值權重∆E之影響 52
第七章 結論與未來研究方向 54
參考文獻 56

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