Economic Analysis of Unmanned Aerial Vehicle (UAV) Provided Mobile Services

Abstract

Due to its agility and mobility, the unmanned aerial vehicle (UAV) is a promising technology to provide high-quality mobile services (e.g., fast Internet access, edge computing, and local caching) to ground users. The Internet service providers (ISPs) directly or commission the third-party UAV firms to provide UAV-provided services (UPS) to improve and make up for the shortage of their current mobile services for additional profit. Yet the UAV has limited energy storage and needs to fly to serve users locally, requiring an optimal energy allocation for balancing both hovering time and service capacity. For profit-maximizing purpose, when hovering in a hotspot, how the UAV should dynamically price its capacity-limited UPS according to randomly arriving users with private service valuations is another question. This paper first introduce a threshold-based assignment policy to show how the UAV decides to serve the users or not under complete information that a user’s service valuation can be observed when he arrives. Following this benchmark, we analyze the UAV’s optimal pricing under incomplete information about the users’ random arrival and private service valuations. It is proved that the UAV should ask for a higher price if the leftover hovering time is longer or its service capacity is smaller, and its expected profit approaches to that under complete user information if the hovering time is sufficiently large. Then, based on the optimal pricing, the energy allocation to hovering time and service capacity in a hotspot is optimized. We show that as the hotspot’s user occurrence rate increases, a shorter hovering time or a larger service capacity should be allocated. Finally, when a UAV faces multiple hotspot candidates with different user occurrence rates and flying distances, we prove that it is optimal to deploy the UAV to serve a single hotspot, by taking the optimal pricing and energy allocation of each hotspot into consideration. With multiple UAVs, however, this result can be reversed with UAVs’ forking deployment to different hotspots, especially when hotspots are more symmetric or the UAV number is large. Perhaps surprisingly, more UAVs may be deployed to the second-best hotspot rather than the first-best one.