# Dynamic Pricing and Capacity Allocation of UAV-provided Mobile Services

## TL;DR

This paper investigates the optimal dynamic pricing and capacity allocation strategies for UAVs providing mobile services, considering energy constraints, user demand, and multiple hotspots, to maximize profit.

## Contribution

It develops a comprehensive model for UAV capacity pricing and deployment, revealing optimal strategies under various operational scenarios and user demand conditions.

## Key findings

- UAV should set higher prices with longer remaining hover time.
- Optimal deployment involves serving a single hotspot with multiple UAVs, unless multiple UAVs are used.
- Profit approaches full information scenarios when hover time is sufficiently large.

## 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. Major Internet Service Providers (ISPs) want to enable UAV-provided services (UPS) to improve and enrich the current mobile services for additional profit. This profit-maximization problem is not easy as the UAV has limited energy storage and needs to fly closely to serve users, requiring an optimal energy allocation for balancing both hovering time and service capacity. 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. We prove 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. As the hotspot's user occurrence rate increases, a shorter hovering time or a larger service capacity should be allocated. Finally, when the 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. With multiple UAVs, however, this result can be reversed with UAVs' forking deployment to different hotspots.