Drone-as-a-Service Composition Under Uncertainty
Date
2021
Authors
Ali, A.
Salim, F.D.
Kim, D.Y.
Ghari Neiat, A.
Bouguettaya, A.
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Journal article
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IEEE Transactions on Services Computing, 2021; 15(5):1-14
Statement of Responsibility
Ali Hamdi, Flora D. Salim, Du Yong Kim, Azadeh Ghari Neiat, and Athman Bouguettaya
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Abstract
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.
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© 2021 IEEE