Toward a Marketplace for Aerial Computing

Abstract

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.