A Trust-Aware Data Collection and Inference Framework for Sparse Mobile Crowdsensing

Sparse Mobile Crowdsensing (SMCS) has emerged as a prominent data engineering paradigm for large-scale, trust-aware environmental data acquisition, enabling diverse data-driven cyber-physical applications. However, the sparsity of the collected sensing data and the presence of untrustworthy workers present key challenges for optimizing data collection and inference accuracy. In particular, malicious workers may submit falsified data, significantly compromising system reliability and the accuracy of data-driven inference. To address these challenges, we propose a Trustworthy Worker Recruitment framework based on Proximal Policy Optimization (PPO-TWR) for trust-aware data collection and inference. The framework evaluates the trustworthiness of workers and leverages high-trust contributions to enhance both data acquisition and inference accuracy in sparse sensing scenarios. The main contributions are threefold: (1) We develop a trust evaluation algorithm that incorporates behavioral scoring to quantitatively evaluate each worker's reliability and contribution. (2) We design a multi-dimensional trust-aware data collection and inference strategy that jointly optimizes data coverage, inference accuracy, trust evaluation, and acquisition cost. (3) To improve the efficiency of PPO training, an action masking mechanism is introduced to mask invalid actions during training, thereby accelerating policy convergence. Extensive simulations demonstrate that the proposed framework significantly improves both data quality and inference accuracy while reducing the cost of data acquisition. Compared to baseline methods, our approach achieves a reduction of 22.23%-57.22% in collection cost and a 12.79%-56.45% improvement in inference accuracy, highlighting its effectiveness in trust-sensitive mobile crowdsensing scenarios.