AeroDaaS: Towards an Application Programming Framework for Drones-as-a-Service
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918071865704448 |
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| author | Raj, Suman Singh, Rajdeep Astu, Kautuk Simmhan, Yogesh |
| author_facet | Raj, Suman Singh, Rajdeep Astu, Kautuk Simmhan, Yogesh |
| contents | The increasing adoption of UAVs with advanced sensors and GPU-accelerated edge computing has enabled real-time AI-driven applications in fields such as precision agriculture, wildfire monitoring, and environmental conservation. However, integrating deep learning on UAVs remains challenging due to platform heterogeneity, real-time constraints, and the need for seamless cloud-edge coordination. To address these challenges, we introduce AeroDaaS, a service-oriented framework that abstracts UAV-based sensing complexities and provides a Drone-as-a-Service (DaaS) model for intelligent decision-making. AeroDaaS offers modular service primitives for on-demand UAV sensing, navigation, and analytics as composable microservices, ensuring cross-platform compatibility and scalability across heterogeneous UAV and edge-cloud infrastructures. We implement and evaluate a preliminary version of AeroDaaS for two real-world DaaS applications. We require <=40 lines of code for the applications and see minimal platform overhead of <=20 ms per frame and <=0.5 GB memory usage on Orin Nano. These early results are promising for AeroDaaS as an efficient, flexible and scalable UAV programming framework for autonomous aerial analytics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_03802 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AeroDaaS: Towards an Application Programming Framework for Drones-as-a-Service Raj, Suman Singh, Rajdeep Astu, Kautuk Simmhan, Yogesh Distributed, Parallel, and Cluster Computing The increasing adoption of UAVs with advanced sensors and GPU-accelerated edge computing has enabled real-time AI-driven applications in fields such as precision agriculture, wildfire monitoring, and environmental conservation. However, integrating deep learning on UAVs remains challenging due to platform heterogeneity, real-time constraints, and the need for seamless cloud-edge coordination. To address these challenges, we introduce AeroDaaS, a service-oriented framework that abstracts UAV-based sensing complexities and provides a Drone-as-a-Service (DaaS) model for intelligent decision-making. AeroDaaS offers modular service primitives for on-demand UAV sensing, navigation, and analytics as composable microservices, ensuring cross-platform compatibility and scalability across heterogeneous UAV and edge-cloud infrastructures. We implement and evaluate a preliminary version of AeroDaaS for two real-world DaaS applications. We require <=40 lines of code for the applications and see minimal platform overhead of <=20 ms per frame and <=0.5 GB memory usage on Orin Nano. These early results are promising for AeroDaaS as an efficient, flexible and scalable UAV programming framework for autonomous aerial analytics. |
| title | AeroDaaS: Towards an Application Programming Framework for Drones-as-a-Service |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2504.03802 |