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arXiv 2103.06513cs.DCcs.LG
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不确定条件下的无人机即服务组合

Drone-as-a-Service Composition Under Uncertainty

RMIT University（皇家墨尔本理工大学）；Deakin University（迪肯大学）；University of Sydney（悉尼大学）

Ali Hamdi, Flora D. Salim, Du Yong Kim, Azadeh Ghari Neiat, Athman Bouguettaya

备注：
20 pages, 20 figures, Accepted for publication at IEEE Transactions on Services Computing
发表信息：
IEEE Transactions on Services Computing (TSC), 2021
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摘要：

我们提出了一种不确定性感知服务方法，以有效地提供基于无人机的交付服务，称为无人机即服务（DaaS）。具体来说，我们提出了一个服务模型的DaaS的基础上的动态时空特征的无人机和他们在飞行中的上下文。所提出的DaaS服务方法由三个组件组成：调度、路由规划和组合。首先，我们开发了一个DaaS调度模型，通过Skyway网络生成DaaS行程。其次，我们提出了一种不确定性感知的DaaS路线规划算法，该算法在天气不确定性下选择最佳Skyways。第三，我们开发了两种DaaS组合技术，以在规划路线的每个站点选择最佳DaaS组合。时空DaaS作曲家首先根据其时空可用性和无人机功能选择最佳DaaS。然后，预测DaaS合成器利用第一合成器的结果，使用几种机器学习分类方法来实现快速准确的DaaS合成。我们训练分类器使用一组新的时空功能，除了其他DaaS QoS属性。实验结果表明了该方法的有效性和高效性。

英文摘要：

We propose an uncertainty-aware service approach to provide drone-based delivery services called Drone-as-a-Service (DaaS) effectively. Specifically, we propose a service model of DaaS based on the dynamic spatiotemporal features of drones and their in-flight contexts. The proposed DaaS service approach consists of three components: scheduling, route-planning, and composition. First, we develop a DaaS scheduling model to generate DaaS itineraries through a Skyway network. Second, we propose an uncertainty-aware DaaS route-planning algorithm that selects the optimal Skyways under weather uncertainties. Third, we develop two DaaS composition techniques to select an optimal DaaS composition at each station of the planned route. A spatiotemporal DaaS composer first selects the optimal DaaSs based on their spatiotemporal availability and drone capabilities. A predictive DaaS composer then utilises the outcome of the first composer to enable fast and accurate DaaS composition using several Machine Learning classification methods. We train the classifiers using a new set of spatiotemporal features which are in addition to other DaaS QoS properties. Our experiments results show the effectiveness and efficiency of the proposed approach.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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