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BeeCluster: drone orchestration via predictive optimization
Name
BeeCluster.pdf
Description
Accepted version
Size
2.6 MB
Format
Adobe PDF
Checksum (MD5)
9fb39053023ba734c363352e97f985c3
Author(s)
He, Songtao
Bastani, Favyen
Balasingam, Arjun
Gopalakrishna, Karthik
Jiang, Ziwen
Alizadeh Attar, Mohammadreza
Balakrishnan, Hari
Cafarella, Michael J
Kraska, Tim
Madden, Samuel R
Date Issued
June 2020
Journal
18th Annual International Conference on Mobile Systems, Applications, and Services
Publisher
Association for Computing Machinery (ACM)
Citation
He, Songtao et al. "BeeCluster: drone orchestration via predictive optimization." 18th Annual International Conference on Mobile Systems, Applications, and Services, June 2020, Toronto, Canada, Association for Computing Machinery, June 2020. © 2020 ACM.
Version
Author's final manuscript
Abstract
The rapid development of small aerial drones has enabled numerous drone-based applications, e.g., geographic mapping, air pollution sensing, and search and rescue. To assist the development of these applications, we propose BeeCluster, a drone orchestration system that manages a fleet of drones. BeeCluster provides a virtual drone abstraction that enables developers to express a sequence of geographical sensing tasks, and determines how to map these tasks to the fleet efficiently. BeeCluster's core contribution is predictive optimization, in which an inferred model of the future tasks of the application is used to generate an optimized flight and sensing schedule for the drones that aims to minimize the total expected execution time. We built a prototype of BeeCluster and evaluated it on five real-world case studies with drones in outdoor environments, measuring speedups from 11.6% to 23.9%.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
http://dx.doi.org/10.1145/3386901.3388912
