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https://hdl.handle.net/2440/137304
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Type: | Journal article |
Title: | Drone-as-a-Service Composition Under Uncertainty |
Author: | Ali, A. Salim, F.D. Kim, D.Y. Ghari Neiat, A. Bouguettaya, A. |
Citation: | IEEE Transactions on Services Computing, 2021; 15(5):1-14 |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Issue Date: | 2021 |
ISSN: | 1939-1374 1939-1374 |
Statement of Responsibility: | Ali Hamdi, Flora D. Salim, Du Yong Kim, Azadeh Ghari Neiat, and Athman Bouguettaya |
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. |
Keywords: | Drone-as-a-Service; uncertainty-aware; service scheduling; route-planning; composition |
Rights: | © 2021 IEEE |
DOI: | 10.1109/TSC.2021.3066006 |
Grant ID: | http://purl.org/au-research/grants/arc/LP150100246 http://purl.org/au-research/grants/arc/DP160103595 http://purl.org/au-research/grants/arc/LE180100158 |
Published version: | http://dx.doi.org/10.1109/tsc.2021.3066006 |
Appears in Collections: | Mathematical Sciences publications |
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