近年來，無人機互聯網(Internet of Drones，IoD)引起了人們的興趣，通過使用無人機(也稱為無人駕駛飛行器)作為物聯網設備來提高傳統物聯網的靈活性。然而，無人機互聯網仍然面臨著一些挑戰。由於用戶的快速增長，大量用戶同時存取網路中的資料會導致網路擁塞，電池的限制也使無人機無法頻繁地提供服務。在最近的研究中，在用戶附近的邊緣伺服器中暫存無人機數據被譽為是減少無人機電池消耗的一種很有前景的方法。當用戶要請求無人機數據時，暫存了該數據的邊緣伺服器可以直接將數據提供給用戶，而不是向無人機取得，以減少用戶向無人機直接請求數據的次數。此方法不僅能夠減少無人機頻繁地傳輸數據的電池消耗，也可以減少網路的擁塞。本論文同時考慮了快取內容放置和用戶請求分配問題，其中每個邊緣伺服器皆配備一個容量有限的快取(cache)，並且每個邊緣伺服器可以處理的用戶請求是有限制的。為了減少無人機傳輸數據的次數，選擇合適的數據放入快取中，並將用戶對數據的請求分配給適當的邊緣伺服器，同時不超過邊緣伺服器所能負荷的的服務率限制。此問題被定義為整數規劃問題。本文將用戶請求分配問題轉換為子集合優化問題，提出一種基於模擬退火和動態規劃的啟發式演算法來尋找快取數據放置及用戶請求分配的最優解。本文使用了不同的用戶請求分佈和實驗參數設置進行了廣泛的實驗，以展示我們提出的演算法在不同的情況下皆優於傳統方法。

Internet of drones (IoD) has recently attracted interest in improving the flexibility of the internet of things (IoT) by using drones, also known as unmanned aerial vehicles (UAVs), as IoT devices. However, IoD networks still face several challenges. Because of the rapid growth of users, simultaneous users access to the network will lead to network congestion. Battery constraint also makes drones incapable of transmitting data frequently. Caching the IoT data in the edge servers near the users is a promising way to reduce drones’ battery consumption. When the user requests IoT data, the edge servers that have cached that data can directly provide them to the user instead of obtaining them from the drones, thus reducing the number of direct requests from the drones, thereby reducing the battery consumption of the drones. This paper considers the joint cache content placement and user request allocation problem, where each edge server equips with a cache with limited capacity, and the user requests that each edge server can process are fixed. An effective and efficient scheme is proposed to place the data from the drones in the caches and allocate user requests to the proper edge servers. The problem is formulated as an integer programming problem. The user request allocation problem is transformed into a subset sum optimization problem and proposed a heuristic algorithm based on simulated annealing and dynamic programming to find the optimal solution for cache content placement and user request allocation problem. Extensive simulations have been carried out to show our proposed algorithm’s performance compared to other methods. Results show that the proposed algorithm outperforms the comparison methods in different data distribution and experimental parameter settings.

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