TY - GEN
T1 - Toward a Marketplace for Aerial Computing
AU - Balasingam, Arjun
AU - Gopalakrishnan, Karthik
AU - Mittal, Radhika
AU - Alizadeh, Mohammad
AU - Balakrishnan, Hamsa
AU - Balakrishnan, Hari
N1 - We thank Ahmed Saeed, Venkat Arun, and Akila Saravanan for helpful discussions and for assisting with data collection.We thank the anonymous MobiSys reviewers for their thoughtful feedback. This research was supported in part by the NSF under Graduate Research Fellowship grant #2389237. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. The NASA University Leadership Initiative (grant #80NSSC20M0163) provided funds to assist the authors with their research, but this article solely reflects the opinions and conclusions of its authors and not any NASA entity.
We thank Ahmed Saeed, Venkat Arun, and Akila Saravanan for helpful discussions and for assisting with data collection. We thank the anonymous MobiSys reviewers for their thoughtful feedback. This research was supported in part by the NSF under Graduate Research Fellowship grant #2389237. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. The NASA University Leadership Initiative (grant #80NSSC20M0163) provided funds to assist the authors with their research, but this article solely reflects the opinions and conclusions of its authors and not any NASA entity.
PY - 2021/6/24
Y1 - 2021/6/24
N2 - The rapid proliferation of commodity drones has expanded interest in building applications that acquire imagery, video, and sensor data at scale. In addition, recent work on drone programming frameworks have simplified the development of aerial computing apps that gather this data. These advancements have popularized the drones-as-a-service model, where large drone fleets serve multiple apps simultaneously. This paper proposes a marketplace for aerial computing, where apps can gather aerial data on demand and providers can offer up their drones for aerial computing. We introduce Aerialis, a drones-as-a-service platform that schedules tasks to drones by arbitrating bids submitted by apps. Aerialis allows apps with different semantics and spatiotemporal preferences to express how much they would like to pay for each aerial computing task. It then aggregates requests across apps, and schedules tasks on drones according to a marketplace policy (e.g., maximizing revenue or guaranteeing quality-of-service to apps). We build a prototype of Aerialis, and implement urban sensing apps to monitor air pollution, measure road traffic, and profile cellular throughput. We discuss operational challenges in deploying Aerialis, and show how the measurements collected from our real-world experiments offer valuable insights for engineers and city planners.
AB - The rapid proliferation of commodity drones has expanded interest in building applications that acquire imagery, video, and sensor data at scale. In addition, recent work on drone programming frameworks have simplified the development of aerial computing apps that gather this data. These advancements have popularized the drones-as-a-service model, where large drone fleets serve multiple apps simultaneously. This paper proposes a marketplace for aerial computing, where apps can gather aerial data on demand and providers can offer up their drones for aerial computing. We introduce Aerialis, a drones-as-a-service platform that schedules tasks to drones by arbitrating bids submitted by apps. Aerialis allows apps with different semantics and spatiotemporal preferences to express how much they would like to pay for each aerial computing task. It then aggregates requests across apps, and schedules tasks on drones according to a marketplace policy (e.g., maximizing revenue or guaranteeing quality-of-service to apps). We build a prototype of Aerialis, and implement urban sensing apps to monitor air pollution, measure road traffic, and profile cellular throughput. We discuss operational challenges in deploying Aerialis, and show how the measurements collected from our real-world experiments offer valuable insights for engineers and city planners.
KW - Aerial sensing
KW - Incentives
KW - Marketplace
KW - Optimization
UR - http://www.scopus.com/inward/record.url?scp=85122639928&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85122639928&partnerID=8YFLogxK
U2 - 10.1145/3469259.3470485
DO - 10.1145/3469259.3470485
M3 - Conference contribution
AN - SCOPUS:85122639928
T3 - Proceedings of the 7th Workshop on Micro Aerial Vehicle Networks, Systems, and Applications, DroNet 2021
SP - 1
EP - 6
BT - Proceedings of the 7th Workshop on Micro Aerial Vehicle Networks, Systems, and Applications, DroNet 2021
PB - Association for Computing Machinery
T2 - 7th Workshop on Micro Aerial Vehicle Networks, Systems, and Applications, DroNet 2021
Y2 - 24 June 2021
ER -