BeeCluster: drone orchestration via predictive optimization
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; ... Show more Show less
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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%.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
18th Annual International Conference on Mobile Systems, Applications, and Services
Association for Computing Machinery (ACM)
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.
Author's final manuscript