Drone-as-a-Service Composition Under Uncertainty

TL;DR

This paper presents an uncertainty-aware Drone-as-a-Service framework that integrates scheduling, route planning, and composition, utilizing machine learning to optimize drone delivery under weather uncertainties.

Contribution

It introduces a novel DaaS service model with a new route-planning algorithm and two composition techniques, including a machine learning-based method for fast DaaS selection.

Findings

The approach effectively handles weather uncertainties in drone routing.

Machine learning classifiers improve DaaS composition speed and accuracy.

Experimental results demonstrate the system's efficiency and effectiveness.

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.