在近幾年由於微型化技術日益成熟,無線感測網路 (Wireless Sensor Network, WSN)
的應用層面也越來越廣泛。由於組成無線感測網路需要成千上萬個感測器,而感測器
是具有固定能量限制的微小裝置,不論是要替換電池或是替換感測器所消耗的成本都
是相當可觀的。因此如何能夠以比較有效率的方式使用感測器的能量,進而延長整個
無線感測網路的生命週期就變得十分重要。能夠改變未來生活的科技中,除了無線感
測網路之外,量子電腦也是被認為是能夠影響現今資訊產業的一門技術,因此雖然商
業型的量子電腦尚未被開發出來,但已經有許多的研究架構其上,其中包含了量子演
化式演算法 (Quantum-inspired Evolutionary Algorithm, QEA) 。本論文結合了兩種具有未來性的技術,提出一個基於低能量適應性分群階層法 (Low Energy Adaptive Clustering Hierarchy, LEACH) 和量子演化式演算法的新型演算法。實驗結果顯示此演算法能夠有效的延長無線感測網路的生命週期,並且具有較大的彈性能夠適合各種不同的環境。

Wireless sensor network (WSN) is one of the most promising and well-known technologies, which has a very broad range of applications. However, to make it work efficiently, the lifetime of sensors has to be prolonged, for most of the sensors in such a network environment are battery-charged, thus having a very short lifespan. Also, the number of sensors in a WSN can easily grow up to tens of thousands; in this case, the lifetime optimization will become a difficult problem. To solve this problem, many state-of-the-art methods have been proposed. Although no quantum computers are commercially available yet, many studies have been built on the concept of quantum computers, such as the quantum-inspired evolutionary algorithm (QEA). In this thesis, we propose a new, quantum computer based algorithm for prolonging the lifetime of a WSN, by leveraging the strength of the low energy adaptive clustering hierarchy (LEACH) and QEA. Experimental results show that the proposed algorithm can not only prolong the lifespan of a WSN, it can also be applied to all kinds of environments.

論文審定書 i
誌謝 iii
摘要 iv
Abstract v
List of Figures viii
List of Tables x
Chapter 1 簡介
1.1 動機 2
1.2 論文貢獻 3
1.3 論文架構 3
Chapter 2 相關文獻探討
2.1 低能量適應性分群階層法及其相關演算法 4
2.1.1 低能量適應性分群階 層法 (LEACH) 6
2.1.2 低能量適應性分群階層法的相關演算法 7
2.2 量子啟發式演算法及其相關演算法 10
2.2.1 量子啟發式演算法 (QEA) 10
2.2.2 量子啟發式演算法的相關演算法 14
2.3 結論 15
Chapter 3 以量子演化式演算法為基礎之分群演算法解無線感測網路問題
3.1 演算法設計概念 16
3.2 演算法流程 17
3.2.1 初始化 18
3.2.2 分群 18
3.2.2.1 觀察 18
3.2.2.2 判斷負載平衡機制 19
3.2.2.3 計算目標值 19
3.2.2.4 更新 20
3.2.3 廣播 21
3.2.4 傳輸 21
3.3 範例 22
Chapter 4 實驗結果
4.1 執行環境、參數設定 25
4.2 模擬環境 27
4.3 模擬結果 28
4.4 總結 36
Chapter 5 結論與未來展望
5.1 結論 37
5.2 未來展望 37

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