Resilient Composition of Drone Services for Delivery
Babar Shahzaad (1), Athman Bouguettaya (1), Sajib Mistry (2), Azadeh, Ghari Neiat (3) ((1) The University of Sydney, Sydney NSW 2000, Australia,, (2) Curtin University, Perth WA 6102, Australia, (3) Deakin University,, Geelong VIC 3220, Australia)
TL;DR
This paper introduces a resilient drone service composition framework that adapts to weather-induced uncertainties using skyline selection, lookahead algorithms, and heuristic updates, ensuring reliable delivery.
Contribution
It presents a novel framework combining skyline selection, constraint-aware lookahead, and heuristic adaptation for resilient drone delivery service composition.
Findings
Efficient drone service selection using skyline approach.
Effective adaptation to dynamic weather conditions.
Experimental validation shows improved delivery reliability.
Abstract
We propose a novel resilient drone service composition framework for delivery in dynamic weather conditions. We use a skyline approach to select an optimal set of candidate drone services at the source node in a skyway network. Drone services are initially composed using a novel constraint-aware deterministic lookahead algorithm using the multi-armed bandit tree exploration. We propose a heuristic-based resilient service composition approach that adapts to runtime changes and periodically updates the composition to meet delivery expectations. Experimental results prove the efficiency of the proposed approach.
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