A Lightweight Blockchain-Based Model for Data Quality Assessment in Crowdsensing

Abstract

By allocating tasks to participants, crowdsensing has shown large potential in addressing large-scale data sensing problems. Considering the problem of unfair payment, negative work of participants, and cooperative cheating, how to assess data quality of tasks reliably is an important problem in crowdsensing. Therefore, a lightweight blockchain-based model for data quality assessment is proposed in this article. First, there are two data quality assessment processes in the model. One is implemented in the selection of participants and the other is implemented in data quality assessment. Second, consensus mechanism and smart contracts are redesigned to be suitable for crowdsensing. The lightweight consensus mechanism delegated proof of reputation (DPoR) is proposed in the blockchain-based model instead of proof of work (PoW). Furthermore, three smart contracts, verifiers selection contract (VSC), participants employment contract (PEC), and data verify contract (DVC), are generated to constrain the behaviors of the involved parties. Finally, expectation–maximization (EM) algorithm with multiverifiers is proposed to evaluate the performance of task participants. Experiments on the open data sets Wine Quality show that our new method outperforms the existing methods in improving the quality of sensing task.