Pricing Mechanism for Quality-Based Radio Mapping via Crowdsourcing

Abstract

White Space (WS) Networking crucially relies on the active monitoring of spatio-temporal spectrum usage (to identify WS opportunities). To achieve this, one way is to gather spectrum data via wide-area sensor deployment and construct better Radio Environment Maps (REMs) with spatial models such as Kriging and Gaussian Process (GP). An economically viable alternative is via incentivized crowdsourcing, i.e., outsourcing sensing tasks to mobile users who have sensorized high-end client devices like tablets or smartphones, and providing proper incentives to compensate for users' sensing costs. In crowdsourced REM, features that impact REM performance and economic cost include user locations and the heterogeneity of user devices, which impact data quality and sensing costs. In this work, we emphasize the use of a hardware noise term in the GP model to account for data quality, and adopt mutual information to quantify sampling performance; we further design a pricing mechanism that allows the platform to maximize its expected utility at each stage and send optimal price offers to users sequentially, with joint consideration of sampling value, data quality and cost. We conduct simulations to evaluate the performance. Simulation results show that our mechanism outperforms two baseline mechanisms, and benefits from more users and less hardware noise (i.e., better data quality).