Eliciting Truthful Data From Crowdsourced Wireless Monitoring Modules in Cloud Managed Networks

Abstract

To facilitate efficient cloud managed resource allocation solutions, collection of key wireless metrics from multiple access points (APs) at different locations within a given area is required. In unlicensed shared spectrum bands collection of metric data can be a challenging task for a cloud manager as independent self-interested APs can operate in these bands in the same area. We propose to design an intelligent crowdsourcing solution that incentivizes independent APs to truthfully measure/report data relating to their wireless channel utilization (CU). Our work focuses on challenging scenarios where independent APs can take advantage of recurring patterns in CU data by utilizing distribution aware strategies to obtain higher reward payments. We design truthful reporting methods that utilize logarithmic and quadratic scoring rules for reward payments to the APs. We show that when measurement computation costs are considered then under certain scenarios these scoring rules no longer ensure incentive compatibility. To address this, we present a novel reward function which incorporates a distribution aware penalty cost that charges APs for distorting reports based on recurring patterns. Along with synthetic data, we also use real CU data values crowdsourced using multiple independent measuring/reporting devices deployed by us in the University of Oulu.

Keywords

• Unlicensed spectrum
• edge analytics
• scoring
• crowdsourcing
• shared spectrum
• real data