Pricing Mechanisms for Crowd-Sensed Spatial-Statistics-Based Radio Mapping

Abstract

Networking on white spaces (i.e., locally unused spectrum) relies on active monitoring of spatio-temporal spectrum usage. Spectrum databases based on empirical radio propagation models are widely adopted but shown to be error-prone, since they do not account for local environments like trees and man-made buildings. As an economically viable alternative, crowd-sensed radio mapping acquires more accurate local spectrum data from spatially distributed users and constructs radio maps using spatial models such as Kriging and Gaussian Process. Success of such crowd-sensing systems presumes some incentive mechanisms to attract user participation. In this work, we design a crowd-sensing system for radio mapping, and develop several pricing mechanisms that maximize the expected utility, which trades off sampling performance (e.g., sample location and data quality) against sensing costs. Specifically, we develop optimal pricing with sequential offering, and probable-acceptance pricing with single-/multi- batch offering. For the later, we formulate user selection as an unconstrained submodular maximization problem. Simulation results are provided and the performance of proposed pricing schemes is compared.