Pricing Mechanisms for Crowd-Sensed Spatial-Statistics-Based Radio Mapping
Xuhang Ying, Sumit Roy, Radha Poovendran

TL;DR
This paper develops and analyzes pricing mechanisms for incentivizing user participation in crowd-sensed radio mapping, optimizing expected utility to improve spectrum data collection accuracy.
Contribution
It introduces novel EU-based pricing strategies for sequential and batched offers, with provable guarantees and submodular optimization techniques.
Findings
EU-based mechanisms outperform baseline methods
Submodular properties enable efficient user selection
Mechanisms improve spectrum mapping accuracy
Abstract
Networking on white spaces (i.e., locally unused spectrum) relies on active monitoring of 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 built environments like trees and man-made buildings. As an economically viable option, crowd-sensed radio mapping acquires more accurate local spectrum data from mobile 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 consider the scenario where the platform who constructs radio environment maps makes one-time offers to selected users, and collects data from those who accept the offers. We design pricing mechanisms based on expected utility (EU) maximization, where EU captures the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
