臺灣博碩士論文加值系統

精準農業（Precision agriculture, PA）是一種農業管理理念，利用遙測技術提供最佳生長模式參數，以最少的資源達到最佳的成果。無線感測網路（Wireless Sensor Networks, WSN）是觀察這些參數的常用技術。在高經濟價值作物的重點區域，如何找到感測器的最佳覆蓋範圍，並保持感測網路的時間更長，是一個重要課題。
為了確保作物在重點區域的安全性不受感測網路的損害影響，本文重點介紹了感測器分佈與重點區域的優化，通過使用自適應粒子群體最佳演算法（Adaptive Particle Swarm Optimization, APSO）來提升關鍵區域的覆蓋面積。 APSO在全局和本地搜索之間取得平衡，並降低落入局部最佳解的可能性。
模擬結果表明，在感測器數量相同的隨機事故情況下，有設置重點區域的覆蓋率比無設置高10％，有效降低了經濟作物對事故的影響。

Precision agriculture (PA) is a farming management concept that uses telemetry technology to provide the parameters of best growth pattern to reach the goal of optimizing returns on inputs while preserving resources. Wireless sensor networks (WSNs) is the common technology for observing these parameters. In the important area that with high economic crops, how to find the best coverage of the sensors and to maintain sensors network for a longer time, is an important issue.
To ensure crops safety in important area from the damage of the sensors network, this thesis focus on the optimization of sensors distribution with important areas to enhance the key areas’ coverage by using the Adaptive Particle Swarm Optimization algorithms (APSO). APSO gives balance between global and local searching and reduces the possibility of falling into the locally optimal solution.
The simulation results show that in the randomized accident situation with the same amount sensors, the coverage rate region with important areas setup is 10% higher than that without setup, which effectively reduces the impact of accidents on high economic crops.

摘 要 i
Abstract ii
誌 謝 iii
目錄 iv
表 目 錄 vi
圖 目 錄 vii
一、緒論 1
1.1前言 1
1.2研究動機 2
1.3研究目的 2
1.4論文架構 3
二、無線感測網路介紹與覆蓋率 4
2.1 無線感測網路介紹 4
2.1.1無線感測網路種類介紹 7
2.1.2國外在農業上應用之研究 9
2.1.3國內在農業上應用現況 11
2.2 無線感測網路覆蓋率 12
2.2.1數值法 13
2.2.2列舉法 13
2.2.3隨機搜尋法 13
三、演算法的研究與介紹 15
3.1研究簡介 15
3.2粒子群最佳化演算法 16
3.2.1粒子群最佳化演算法的介紹 16
3.2.2 粒子群最佳演算法特性 17
3.2.3 粒子群最佳演算法運算 21
3.3自適應粒子群最佳化演算法的介紹 24
四、使用APSO 解決感測器覆蓋問題 26
4.1基本感測器的覆蓋相關運算 26
4.1.1感測器重覆覆蓋率分析 27
4.1.2感測器浪費面積分析 30
4.1.3感測器整體覆蓋面積 33
4.2自適應粒子群體最佳演算法之設計與應用 35
4.2.1解決感測器覆蓋問題之架構 35
4.2.2集中重心法則 37
4.2.3 APSO應用於感測器最佳解 38
4.3使用APSO解決覆蓋問題之設計架構 40
4.4自適應粒子群體最佳演算法(APSO)示意圖 43
五、模擬結果與分析 46
5.1論文研究模擬簡介 46
5.2自適應粒子群最佳演算法參數系統設計 47
5.3感測器使用數量分析 48
5.4研究模擬與分析 49
5.4.1研究模擬(一) 49
5.4.2研究模擬(二) 52
5.5研究結果比較與分析 55
六、結論與未來展望 56
6.1結論 56
6.2未來展望 57
參考文獻 58

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