# Drone-as-a-Service Composition Under Uncertainty

arXiv: https://arxiv.org/abs/2103.06513 · HTML: https://mlsanity.com/paper/2103.06513/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 43 references that are in the map.
- First posted on arXiv 11 Mar 2021.

## Builds on (most cited first, 3 of 3)

- [Vehicle Routing Problems for Drone Delivery](https://mlsanity.com/paper/1608.02305/) (2016)
- [Enhanced discrete particle swarm optimization path planning for UAV vision-based surface inspection](https://mlsanity.com/paper/1706.04399/) (2017)
- [DroTrack: High-speed Drone-based Object Tracking Under Uncertainty](https://mlsanity.com/paper/2005.00828/) (2020)

## Built on by (most cited first, 3 of 3)

- [Spatiotemporal Data Mining: A Survey on Challenges and Open Problems](https://mlsanity.com/paper/2103.17128/) (2021)
- [LLM-DaaS: LLM-driven Drone-as-a-Service Operations from Text User Requests](https://mlsanity.com/paper/2412.11672/) (2024)
- [Deep Heuristic Learning for Real-Time Urban Pathfinding](https://mlsanity.com/paper/2411.05044/) (2024)

## Abstract (arXiv)

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.