臺灣博碩士論文加值系統

近年來隨著無線感測網路的發展，感測裝置的應用範圍不斷擴大。其在環境監測、智慧交通、智慧家庭、軍事場景等領域中都有廣泛的應用。為了收集更全面及精確的資料，部屬大量感測裝置已經成為一種趨勢。然而在特定或基礎設施不足的區域，感測裝置可能無法將其資料傳送到基地台，因此可以利用無人機的高機動性移動到感測裝置附近進行資料收集，透過無人機的協助可以實現對特定區域的全面監測及資料收集，方便後續進行進一步的資料分析。有別於傳統的多無人機資料收集方案，本論文根據無人機的通訊覆蓋範圍去尋找能夠覆蓋所有感測裝置的懸停位置，來減少無人機所需移動的距離。在確認懸停位置後利用無人機資料收集路徑規劃演算法來尋找無人機的飛行路徑，確保其能夠完成資料收集任務。透過這兩種方法的搭配能夠大量減少無人機的飛行耗電及時間。不過由於環境中存在頻寬限制的問題，感測裝置需要共享頻寬來減少彼此之間的訊號干擾，因此本論文提出根據懸停位置的感測裝置數量平均分配、基於距離分配及基於訊道條件分配頻寬的方法來解決這個問題，適當的頻寬分配能有效降低無人機在每個懸停位置所要花費的時間。同時利用K-means分群方法，將懸停位置進行分群，分群數及為環境中無人機的數量，此方法能有效的平均分配懸停位置給無人機。與現有的文獻比較時，在考慮無人機電量有限的情況下，本論文提出的方法能夠使用較少的無人機來完成資料收集任務。在模擬結果中對任務完成時間、飛行時間、懸停時間、無人機數量及無人機耗電進行比較，不論是在無人機電池電量限制或無人機數量限制的情況下，本論文提出的方法皆優於其他現有文獻。

In recent years, with the development of wireless sensor networks, the application range of sensing devices continues to expand. They are widely used in areas such as environmental monitoring, smart transportation, smart homes, and military scenarios. In order to collect more comprehensive and accurate data, deploying a large number of sensing devices has become a trend. However, in specific or infrastructure-deficient areas, sensing devices may not be able to transmit their data to base stations. Therefore, UAVs can be utilized, taking advantage of their high mobility to move close to the sensing devices for data collection. With the help of UAVs, comprehensive monitoring and data collection in specific areas can be realized, which is convenient for further data analysis. Unlike traditional multi-UAV data collection solutions, this paper proposes to find hovering positions that can cover all sensing devices based on the communication coverage of UAVs to reduce the distance UAVs need to travel. Once the hovering positions are confirmed, a UAV data collection path planning algorithm is used to find the flight path of the UAV, ensuring its ability to complete the data collection task. The combination of these two methods can greatly reduce the UAV's flight power consumption and time. However, due to bandwidth limitations in the environment, sensing devices need to share bandwidth to reduce interference with each other. Therefore, this paper proposes to solve this problem by methods of evenly distributing the bandwidth based on the number of sensing devices at each hovering position, based on distance distribution, and based on channel condition distribution. Appropriate bandwidth allocation can effectively reduce the time spent by UAVs at each hovering position. At the same time, the K-means clustering method is used to cluster the hovering positions. The number of clusters is equivalent to the number of UAVs in the environment. This method can effectively distribute the hovering positions among the UAVs. Compared with existing literature, under the consideration of limited UAV power, the method proposed in this paper can use fewer UAVs to complete data collection tasks. In the simulation results, the task completion time, flight time, hovering time, the number of UAVs, and UAV power consumption are compared. Regardless of the battery power limitation or the number of UAV limitations, the method proposed in this paper outperforms other existing literature.

摘要...i
Abstract...ii
誌謝...iv
目錄...v
表目錄...vii
圖目錄...ix
第一章 前言...1
1.1 研究背景與目的...1
1.2 相關研究...3
1.2.1 單無人機資料收集相關研究...3
1.2.2 多無人機資料收集相關研究...5
1.3 論文架構...7
第二章 系統模型...8
2.1 無線感測網路中使用無人機進行資料收集模型與操作...8
2.2 空對地資料傳輸模型...9
2.3 無人機電量消耗模型...10
第三章 無人機懸停位置選擇...11
3.1 改進的最小化資料收集時間演算法...11
3.2 貪婪演算法...14
3.2.1 基於地圖的貪婪演算法...14
3.2.2 基於感測裝置的貪婪演算法...17
3.3 模擬結果與分析...21
第四章 無人機資料收集路徑規劃...32
4.1 隨機排序演算法...32
4.2 最短路徑搜尋演算法...34
4.3 模擬退火解決旅行商人問題演算法...36
4.4 模擬結果與分析...39
第五章 感測裝置傳輸頻寬分配...44
5.1 平均分配演算法...44
5.2 基於距離分配演算法...47
5.3 基於訊道條件分配演算法...50
5.4 模擬結果與分析...53
第六章 多無人機資料收集...56
6.1 多無人機資料收集策略...56
6.2 模擬結果與分析...61
6.2.1 無人機數量的限制...61
6.2.2 無人機電池電量的限制...75
第七章 結論...89
參考文獻...90
Extended Abstract...94

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