Multidimensional Trust Evaluation and Task Match Based Workers Recruitment Scheme for MCS

Recruiting trust workers to achieve high data quality at low cost has become a promising approach in Mobile Crowdsensing (MCS). However, most existing trust evaluation methods only adopt a single-dimensional trust model, neglecting the fact that a worker's trustworthiness can vary across different task types, which leads to suboptimal task–worker matching, poor data quality, and inefficient cost utilization. To this end, we propose a Multidimensional Trust Evaluation and Task Matching (MTE-TM) based workers recruitment scheme to improve data quality while reducing costs for MCS. First, we represent worker trustworthiness by a composite of expected trust values and variance, enabling task-specific trust assessment that extends traditional single-dimensional trust to a multidimensional domain. A novel Expectation-Maximization (EM)-based trust evaluation mechanism is also introduced to improve accuracy. Second, we design a new worker selection method that combines a worker's trust level and the width of the confidence interval to compute their Upper Confidence Bound (UCB) index, which effectively guides worker selection toward optimal outcomes. Third, we propose an Optimized Data Quality Matching (ODQM) algorithm that assigns tasks to workers with high priority and low bid prices under budget constraints, thereby further improving data quality. The experimental results demonstrate the significant performance improvements of our scheme, achieving 45.54$\sim$95.55% optimization in trust evaluation, 65.01$\sim$72.95% improvement in data quality, and a notable reduction in regret.