@article{fan_xin_jia_zhang_wang_2023, title={COBATS: A Novel Consortium Blockchain-Based Trust Model for Data Sharing in Vehicular Networks}, volume={6}, ISSN={["1558-0016"]}, DOI={10.1109/TITS.2023.3286432}, abstractNote={Achieving efficient and secure shared data in vehicular networks is important for the development of smart transportation. Sharing data among intelligent vehicles not only enriches vehicle services but also improves traffic safety and efficiency. However, due to the specific nature of vehicular networks, security and privacy concerns prevent data providers from participating in the data sharing process. In addition, the quality of the data shared in the vehicular network is uneven and unreliable, and the reliability and authenticity of data sharing need to be further improved. In this paper, we propose a novel consortium blockchain-based trust model in vehicular networks (COBATS) to achieve secure storage and data sharing. To improve the quality of data sharing, we also design a trust management model capable of filtering malicious recommendations, which reduces the hazard of malicious nodes and ensures high-quality data sharing among vehicles. Moreover, we present a consensus mechanism with joint Proof-of-Stake (PoS) and Practical Byzantine Fault Tolerance (PBFT) to reduce resource consumption and improve the algorithm’s efficiency. The simulation results show that COBATS can improve the security and quality of data sharing. Furthermore, our model also can effectively handle certain attacks.}, journal={IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS}, author={Fan, Qi and Xin, Yang and Jia, Bin and Zhang, Yang and Wang, Pinxiang}, year={2023}, month={Jun} }
 @article{zhang_liang_jia_wang_2023, title={Scheduling and Process Optimization for Blockchain-Enabled Cloud Manufacturing Using Dynamic Selection Evolutionary Algorithm}, volume={19}, ISSN={["1941-0050"]}, DOI={10.1109/TII.2022.3188835}, abstractNote={The blockchain-enabled cloud manufacturing is an emerging service-oriented paradigm, and the scheduling and process optimization for blockchain-enabled cloud manufacturing (SPO-BCMfg) are crucial to achieving the service-oriented goal. The blockchain-enabled cloud manufacturing paradigm improves the collaboration capabilities and information security over the ordinary cloud manufacturing while incorporating distributed storage, consensus mechanism, and cloud-edge collaboration. The above characteristics make SPO-BCMfg a multiobjective scheduling optimization problem. This article establishes the multiobjective SPO-BCMfg model based on a dynamic selection evolutionary algorithm to address the problem. First, we carry out the architecture and the modeling of the blockchain cloud manufacturing system. Then, a novel dynamic selection evolutionary algorithm is proposed, which is used to schedule and optimize the model for the process. In the stage of evolution, the algorithm uses a diversity-based population partitioning technique that utilizes the dynamic distance to realize the selection of elite solutions. The method was experimented on the SPO-BCMfg problem facing five and eight objectives. The experimental results show that the algorithm has a strong processing capacity in terms of convergence and diversity compared with the other advanced evolutionary algorithms.}, number={2}, journal={IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS}, author={Zhang, Yang and Liang, Yongquan and Jia, Bin and Wang, Pinxiang}, year={2023}, month={Feb}, pages={1903–1911} }
 @article{zhang_liang_jia_wang_zhang_2022, title={A blockchain-enabled learning model based on distributed deep learning architecture}, volume={6}, ISSN={["1098-111X"]}, DOI={10.1002/int.22907}, abstractNote={Aiming to address the unsatisfactory performance of existing distributed deep learning architectures, such as poor accuracy, slow network communication, low arithmetic speed, and insufficient security, we propose and design a learning model based on a distributed deep learning and blockchain architecture. We use a hybrid parallel algorithm based on blockchain (HP‐B) to build a distributed deep consensus learning model. The HP‐B algorithm is grouped according to the performance of computing nodes participating in training, network links and training samples, and the grouped computing equipment performs optimal distributed computing. The purpose of this approach is to solve the security and scalability concerns and improve the convergence speed and accuracy of deep learning. The proposed method achieves good results on the CIFAR‐100, CIFAR‐10, and IMAGENET data sets. Finally, the distributed deep learning model based on blockchain is combined with the generative adversarial network to solve the segmentation problem of medical imaging data, and the experimental results are superior to those of other networks.}, journal={INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS}, author={Zhang, Yang and Liang, Yongquan and Jia, Bin and Wang, Pinxiang and Zhang, Xiaosong}, year={2022}, month={Jun} }