  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.
