Optimal Pricing of User-Initiated Data-Plan Sharing in A Roaming Market

TL;DR

This paper develops an optimal pricing scheme for incentivizing personal hotspot sharing among local users and travelers in roaming markets, accounting for information uncertainty, heterogeneity, and multiple travelers, to improve data sharing efficiency.

Contribution

It introduces a novel pricing model for pH-enabled data sharing that considers uncertainty, heterogeneity, and multiple travelers, providing unique solutions and insights.

Findings

Existence of a unique optimal price balancing sharing success and cost.

Heterogeneity in pHs' behavior can benefit or hinder travelers.

Increased traveler density raises expected sharing costs.

Abstract

A smartphone user's personal hotspot (pH) allows him to share cellular connection to another (e.g., a traveler) in the vicinity, but such sharing consumes the limited data quota in his two-part tariff plan and may lead to overage charge. This paper studies how to motivate such pH-enabled data-plan sharing between local users and travelers in the ever-growing roaming markets, and proposes pricing incentive for a data-plan buyer to reward surrounding pH sellers (if any). The pricing scheme practically takes into account the information uncertainty at the traveler side, including the random mobility and the sharing cost distribution of selfish local users who potentially share pHs. Though the pricing optimization problem is non-convex, we show that there always exists a unique optimal price to tradeoff between the successful sharing opportunity and the sharing price. We further generalize the optimal pricing to the case of heterogeneous selling pHs who have diverse data usage behaviors in the sharing cost distributions, and we show such diversity may or may not benefit the traveler. Lacking selfish pHs' information, the traveler's expected cost is higher than that under the complete information, but the gap diminishes as the pHs' spatial density increases. Finally, we analyze the challenging scenario that multiple travelers overlap for demanding data-plan sharing, by resorting to a near-optimal pricing scheme. We show that a traveler suffers as the travelers' spatial density increases.