Pricing Mechanisms for Crowd-Sensed Spatial-Statistics-Based Radio   Mapping
  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 tradeoff between radio mapping performance (location and data quality), crowd-sensing cost and uncertainty in offer outcomes (i.e., possible expiration and rejection). Specifically, we consider sequential offering, where one best price offer is sent to the best user in each round, and batched offering, where a batch of multiple offers are made in each round. For the later, we show that EU is submodular in the discrete domain, and propose a mechanism that first fixes the pricing rule and selects users based on Unconstrained Submodular Maximization (USM); it then compares different pricing rules to find the best batch of offers in each round. We show that USM-based user selection has provable performance guarantee. Proposed mechanisms are evaluated and compared against utility-maximization-based baseline mechanisms. 
Xuhang Ying, Sumit Roy and Radha Poovendran
cs.NI