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

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 indepen- dent 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

Crowd-sourcing with uncertain quality - an auction approach

This article addresses two important issues in crowd-sourcing: ex ante uncertainty about the quality and cost of different workers and strategic behaviour. We present a novel multi-dimensional auction that incentivises the workers to make partial enquiry into the task and to honestly report quality-cost estimates based on which the crowd-sourcer can choose the worker that offers the best value for money. The mechanism extends second score auction design to settings where the quality is uncertain and it provides incentives to both collect information and deliver desired qualities

A Semi-supervised Sensing Rate Learning based CMAB Scheme to Combat COVID-19 by Trustful Data Collection in the Crowd

Mobile CrowdSensing (MCS), through employing considerable workers to sense and collect data in a participatory manner, has been recognized as a promising paradigm for building many large-scale applications in a cost-effective way, such as combating COVID-19. The recruitment of trustworthy and high-quality workers is an important research issue for MCS. Previous studies assume that the qualities of workers are known in advance, or the platform knows the qualities of workers once it receives their collected data. In reality, to reduce their costs and thus maximize revenue, many strategic workers do not perform their sensing tasks honestly and report fake data to the platform. So, it is very hard for the platform to evaluate the authenticity of the received data. In this paper, an incentive mechanism named Semi-supervision based Combinatorial Multi-Armed Bandit reverse Auction (SCMABA) is proposed to solve the recruitment problem of multiple unknown and strategic workers in MCS. First, we model the worker recruitment as a multi-armed bandit reverse auction problem, and design an UCB-based algorithm to separate the exploration and exploitation, considering the Sensing Rates (SRs) of recruited workers as the gain of the bandit. Next, a Semi-supervised Sensing Rate Learning (SSRL) approach is proposed to quickly and accurately obtain the workers' SRs, which consists of two phases, supervision and self-supervision. Last, SCMABA is designed organically combining the SRs acquisition mechanism with multi-armed bandit reverse auction, where supervised SR learning is used in the exploration, and the self-supervised one is used in the exploitation. We prove that our SCMABA achieves truthfulness and individual rationality. Additionally, we exhibit outstanding performances of the SCMABA mechanism through in-depth simulations of real-world data traces.Comment: 18 pages, 14 figure