  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.
