A Cost-Efficient Framework for Crowdsourced Data Collection in Vehicular Networks

TL;DR: This work proposes a cost-efficient framework for crowdsourced data collection in vehicular networks while ensuring the accuracy of the response and designs an answer gathering scheme that considers both the length of the aggregation tree and data delivery and minimizes the communication cost for collecting answers from participants.

Abstract: Vehicular networks, which are recognized as an innovative technology for information collection due to the powerful sensing capability and strong mobility, have been an important platform for geographic crowdsourcing services and are particularly useful in environmental data collection. Crowdsourced data collection in vehicular networks must be performed in a communication-efficient manner due to the resource-constrained wireless communication links. Moreover, it is a pressing problem to improve the quality of responses because workers may provide data of poor quality or even fabricate data to defraud rewards. While the existing work has focused on spatial/temporal task coverage and monetary rewards, in this work, we propose a cost-efficient framework for crowdsourced data collection in vehicular networks while ensuring the accuracy of the response. Crowdsourced data collection consists of two main steps: 1) task assignment and 2) crowdsourced answer gathering. We first propose a task assignment scheme that maximizes the overall data quality and reduces the amount of data transmission. We estimate the data quality level based on a Gaussian mixture model and reduce the amount of data transmission by carefully selecting a subset of vehicles for crowdsourced tasks. We then design an answer gathering scheme that considers both the length of the aggregation tree and data delivery and minimizes the communication cost for collecting answers from participants. Extensive experiments on both synthetic data sets and real data sets show that our proposed framework achieves promising results.