RLTD: A Reinforcement Learning-based Truth Data Discovery scheme for decision support systems under sustainable environments

Abstract

The online world and associated information and communication technologies have generated digital networks by processing massive volumes of data and have a significant impact on the environmental sustainability. Mobile Crowd Source (MCS) is one of the digital technologies that can help humanity to better sense, understand and protect the environment by using vast amounts of data obtained to construct intelligent decision support systems (DSS). However, as the false data submitted by dishonest and malicious workers will cause the data-based DSSs to make wrong decisions and thus cause great harm, it is an urgent issue to propose an effective Truth Data Discovery (TDD) scheme for MCS. To tackle this issue, a Reinforcement Learning-based Truth Data Discovery (RLTD) scheme is proposed to obtain truth data in MCS at low cost in this paper. The main innovations of the RLTD scheme are as follows: (1) A novel trustworthiness-based TDD scheme is proposed to obtain truth data accurately at low cost, which can facilitate data-based DSSs in MCS. (2) Combined with Matrix Factorization (MF), we propose a worker recruitment method that only needs to recruit ϑn (ϑ≤1) workers for TDD in n tasks, which reduces the data collection cost significantly than previous TDD schemes. (3) We propose a Reinforcement Learning-based Site Selection (RLSS) method that intelligently selects as few sites as possible for worker recruitment with guaranteed high data quality. Experimental results demonstrate that the RLTD scheme can improve the accuracy of data collection by 1.31%–21.02%, reduce the data collection cost by 81.39%–85.50% compared to the traditional TDD schemes, and identify workers with accuracy of 86.67%–94.58%.