In this paper, we propose an architecture for UAV virtualization with the primary aim of providing virtualized UAV services to multiple users by envisioning the concept of UAV-as-a-Service. In contrast to traditional UAVs, which are resource-constraint in nature and exhibit shorter flight times, our proposed UAV virtualization overcomes the limitations of short flight time of traditional UAVs, in turn allowing them to provide persistent and ubiquitous services. We achieve the virtualization of a UAV through multiple collaborating real-life UAVs performing various tasks in tandem. In this work, we focus on the placement and selection of UAVs to achieve virtualization. We use a social welfare-based approach to select suitable UAVs, from the available ones, and map the UAV to a virtual one. This work enables the provision of different UAV services to multiple end-users, without actual procurement of the UAVs by the end-users. We compare the results for optimal placement, normal maximum energy-based UAV selection, and Atkinson-based selection method. We also compare the virtual model and simple UAV-to-task model to physical UAV usage, task completion ratio, and residual energy of the system. Our proposed model outperforms the traditional model with a task completion efficiency of $\text{94.26}{\%}$. The residual energy of the system also increases with an increase in the number of tasks.

中文翻译：

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用于实现异构和持久无人机即服务的无人机虚拟化

在本文中，我们提出了一种无人机虚拟化架构，其主要目的是通过设想无人机即服务的概念为多个用户提供虚拟化的无人机服务。与传统无人机资源受限、飞行时间较短的特点相比，我们提出的无人机虚拟化克服了传统无人机飞行时间短的局限性，从而使它们能够提供持久和无处不在的服务。我们通过多个协作的现实生活中的无人机协同执行各种任务来实现无人机的虚拟化。在这项工作中，我们专注于无人机的放置和选择以实现虚拟化。我们使用基于社会福利的方法从可用的无人机中选择合适的无人机，并将无人机映射到虚拟无人机。这项工作可以为多个最终用户提供不同的无人机服务，而无需最终用户实际采购无人机。我们比较了最佳放置、基于正常最大能量的无人机选择和基于阿特金森的选择方法的结果。我们还将虚拟模型和简单的无人机到任务模型与系统的物理无人机使用率、任务完成率和剩余能量进行了比较。我们提出的模型优于传统模型，任务完成效率为 $\text{94.26}{\%}$。系统的剩余能量也随着任务数量的增加而增加。我们还将虚拟模型和简单的无人机到任务模型与系统的物理无人机使用率、任务完成率和剩余能量进行了比较。我们提出的模型优于传统模型，任务完成效率为 $\text{94.26}{\%}$。系统的剩余能量也随着任务数量的增加而增加。我们还将虚拟模型和简单的无人机到任务模型与物理无人机的使用、任务完成率和系统的剩余能量进行了比较。我们提出的模型优于传统模型，任务完成效率为 $\text{94.26}{\%}$。系统的剩余能量也随着任务数量的增加而增加。