_:naedc22425727445e9330c0ccdd05e7d9xb28
<http://schema.org/description>
"We propose an uncertainty-aware service approach to provide drone-based\ndelivery services called Drone-as-a-Service (DaaS) effectively. Specifically,\nwe propose a service model of DaaS based on the dynamic spatiotemporal features\nof drones and their in-flight contexts. The proposed DaaS service approach\nconsists of three components: scheduling, route-planning, and composition.\nFirst, we develop a DaaS scheduling model to generate DaaS itineraries through\na Skyway network. Second, we propose an uncertainty-aware DaaS route-planning\nalgorithm that selects the optimal Skyways under weather uncertainties. Third,\nwe develop two DaaS composition techniques to select an optimal DaaS\ncomposition at each station of the planned route. A spatiotemporal DaaS\ncomposer first selects the optimal DaaSs based on their spatiotemporal\navailability and drone capabilities. A predictive DaaS composer then utilises\nthe outcome of the first composer to enable fast and accurate DaaS composition\nusing several Machine Learning classification methods. We train the classifiers\nusing a new set of spatiotemporal features which are in addition to other DaaS\nQoS properties. Our experiments results show the effectiveness and efficiency\nof the proposed approach."
<https://deepai.org/profile/athman-bouguettaya>