Vehicle-based mobile crowdsensing has gained widespread attention due to its low cost and efficient data collection mode. One common method to improve the accuracy of sensing data in this context is truth discovery. However, the emergence of privacy leakage and data misuse has reduced users’ motivation to participate in sensing tasks. Meanwhile, existing solutions for privacy-preserving truth discovery generally suffer from low computational efficiency and frequent interactions between users and servers. Hence, this paper proposes a novel privacy-preserving truth discovery scheme based on secure multi-party computation. For the purpose of high efficiency and strong privacy protection, we utilize the Secret Sharing method to securely decompose data and construct a Secure Multi-party Computation protocol to compute the ground truth. In addition, the weight value generated by truth discovery is employed as a quantitative data quality indicator that dynamically adjusts the user’s rewards and constructs a data quality-driven incentive mechanism. Finally, we demonstrate the high performance of our method through a detailed analysis, showing its effectiveness even in scenarios with numerous users.

Traffic sign recognition is a crucial aspect of autonomous vehicle research, and deep learning techniques have significantly contributed to its progress. Nevertheless, the distribution of traffic sign information in natural complex road conditions is long-tailed, and traffic sign identification in complex road conditions has become a significant barrier to autonomous vehicle applications. The imbalanced distribution of information on the dataset migrates to the feature space during training, resulting in imbalanced classifier prediction. In this paper, we propose the gradient rebalanced traffic sign recognition (GRTR) method to address this problem for the first time. GRTR first evaluates the prediction and classification bias of the classifier using the fitted deviation between the model’s output probability and the ground-truth distributions. Then, GRTR dynamically adjusts the correction and compensation factors following the classifier’s prediction and classification biases. GRTR rebalances the positive and negative sample gradients for each category based on the synergistic effect of the correction and compensation factors to prevent the transfer of distribution imbalance and to significantly enhance the performance of the traffic sign classifier under difficult road conditions. Experimental results demonstrate that our GRTR achieves state-of-the-art performance on long-tailed traffic sign and multilabel datasets. Note to Practitioners —Most traffic sign recognition algorithms are still designed based on the assumption of a balanced distribution of traffic signs in the dataset. Real-world autonomous vehicles require traffic sign recognition on datasets with severely imbalanced distributions. This paper proposes a general approach to solving the long-tailed traffic sign recognition problem.

As a powerful and promising paradigm, vehicular mobile crowdsensing (VMCS) recruits sensing devices on vehicles to collect sensory data, offering advantages such as low cost, wide coverage, and high data quality. Task allocation plays a crucial role in determining the efficiency and performance of vehicular mobile crowdsensing systems. However, existing task allocation methods are mostly designed for offline, single-task crowdsensing scenarios and rarely consider privacy protection, which cannot meet the dynamic and complex requirements of real-world applications. Therefore, we propose an online task allocation and privacy preservation scheme called Lya-FCGA (Lyapunov optimization with Fuzzy Controller-based Genetic Algorithm). To maintain long-term system stability, we utilize Lyapunov optimization to transform long-term objectives into per-time-slot queue control problems. Additionally, we introduce a fuzzy control system to adaptively adjust the genetic algorithm parameters, enhancing the accuracy and efficiency of task allocation. Moreover, differential privacy technology is employed to protect workers' privacy and increase their participation willingness. Extensive experiments on real datasets demonstrate that our scheme reduces sensing costs while ensuring long-term system stability and effectively protecting privacy, showcasing great advantages and potential.