本論文旨在解決無人機協作無線供電通訊網路中，最小化能源消耗的問題。針對無線供電通訊網路中使用混合存取點（Hybrid Access Point, HAP）所產生的雙重近遠問題（Doubly Near-Far problem），我們提出了一種創新的解決方案，結合無人機和節點合作傳輸技術，使用無人機代替HAP，並將無線節點分群，由無人機逐群進行服務。無人機在每個群上方懸停，為節點進行充電及接收回傳的資料。為減輕遠距離節點的負擔，我們採用節點合作傳輸方案，讓距離無人機較近的節點協助較遠節點代傳部分資料，減少雙重近遠問題帶來的能量消耗。本論文提出的最小化飛行路徑演算法（Flight Path Minimization Algorithm, FPMA）根據分群結果，優化無人機的懸停點位置及飛行路徑，同時考量懸停點的位置與訪問順序。再使用順序最小二乘規劃（Sequential Least-Squares Quadratic Pro-gramming, SLSQP）求解最小化懸停時間，進一步降低無人機的總電量消耗。模擬結果顯示，我們的方法在節點合作傳輸及路徑優化方面能有效減少能源消耗，具有顯著的性能提升。

This paper aims to address the issue of minimizing energy consumption in collaborative wireless power communication networks involving drones. To tackle the doubly near-far problem in wireless power communication networks using Hybrid Access Points (HAPs), we propose an innovative solution that combines drone and node cooperative transmission technology. In our approach, UAV replace HAPs, and wireless sensor nodes are grouped, with UAV serving each group sequentially. The drone hovers above each group, providing charging and receiving the transmitted data from the nodes. To alleviate the burden on distant nodes, we adopt a node cooperative transmission scheme, where nodes closer to the drone assist distant nodes by relaying part of their data, thus reducing the energy consumption caused by the doubly near-far problem.
Our proposed Flight Path Minimization Algorithm (FPMA) optimizes the drone's hovering points and flight path based on the grouping results, considering the position and sequence of the hovering points. We then use Sequential Least-Squares Quadratic Programming (SLSQP) to minimize the hovering time, further reducing the total energy consumption of the drone. Simulation results show that our method effectively reduces energy consumption through node cooperative transmission and path optimization, demonstrating significant performance improvements.

目錄

第1章 介紹	1
第2章 預備知識	9
2.1 網路模型	9
2.2 時間分配	11
2.3 System Overview	12
第3章 Radius-Constrained K-means	14
第4章 Flight Path Minimization Algorithm	17
4.1 Initialize Hover Point	20
4.2 Initialize Flight Path	22
4.3 FPMA Iteration	25
4.4 Reduce Flight Curvature	27
4.5 Merge Hover Points	28
第5章 Relay Selection Algorithm	31
第6章 HECm	33
6.1 問題表述	33
6.2 SLSQP Algorithm for HECm	40
第7章 績效評估	45
7.1 有無節點合作傳輸的比較	47
7.2 耗電量比較	48
7.3 FPMA不同場景大小比較圖	52
第8章 結論 Conclusion	55
參考文獻	   56

圖目錄
圖一、無線感測網路 (Wireless Sensor Networks , WSNs)	1
圖二、HTT(Harvest Then Transmit)協議	2
圖三、WPCN第一種架構	3
圖四、WPCN第二種架構	4
圖五、節點合作傳輸	6
圖六、飛行場景圖	10
圖七、時間排程	11
圖八、論文簡易架構圖	13
圖九、UAV傳輸範圍示意圖	14
圖十、懸停點範圍限制	16
圖十一、飛行功率模型變化圖	18
圖十二、飛行500公尺推進耗能變化圖	18
圖十三、初始懸停點	20
圖十四、Initialize Hover Point示意圖一	21
圖十五、Initialize Hover Point示意圖二	22
圖十六、Initialize Flight Path示意圖一	23
圖十七、Initialize Flight Path示意圖二	24
圖十八、FPMA Iteration示意圖	26
圖十九、Reduce Flight Curvature示意圖	28
圖二十、Merge Hover Points示意圖	29
圖二十一、Flight Path Minimization Algorithm 架構圖	30
圖二十二、FPMA三視圖	30
圖二十三、relay選擇場景	32
圖二十四、HECm架構圖	33
圖二十五、論文架構圖	45
圖二十六、有無節點合作傳輸差異圖	47
圖二十七、UAV整體耗電量比較圖	49
圖二十八、飛行耗電量比較圖	50
圖二十九、懸停耗電量比較圖	51
圖三十、WET階段耗電量比較圖	52
圖三十一、不同場景大小耗電量比較圖	53
圖三十二、不同場景大小懸停耗電量比較圖	54

表目錄
表1、網路模擬參數表格	46

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