Efficient data sharing schemes are one of the key technologies in the Internet of Vehicles (IoV). However, the insufficient willingness of vehicle users to provide data makes the traditional blockchain-based IoV network have low throughput. The income of IoV providers decreases when the vehicle density increases on the road. In this paper, we investigated a mobile vehicle data sharing scheme based on the consortium blockchain. In detail, the consortium blockchain was used to limit the degree of decentralization and openness, and the optimal revenue strategy approach between vehicles and data-demand devices was obtained through the Stackelberg game. The load test library based on Node.js was used to simulate and compare the data transmission performance of the proposed consortium blockchain with traditional blockchain schemes. Simulation results show that the proposed scheme had higher buyer’s revenue, and the block transmission performance was significantly higher than that of traditional blockchain schemes.

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Multi-view subspace clustering (MVSC) can effectively group multi-view data distributed around several low-dimensional subspaces. Although encouraging results, most existing methods suffer from two typical limitations, resulting in clustering performance degradation. They ignore high-order correlations underlying the multi-view data, leading to degeneration of complementary power; in addition, they rely on much prior knowledge (e.g., pairwise constraints) for clustering enhancement. In this paper, a novel algorithm called Enhanced Multi-view Subspace Clustering (EMVSC) is proposed to address both limitations. EMVSC can effectively exploit high-order correlations and optimally use limited prior knowledge for better clustering performance. Specifically, EMVSC imposes twist tensor nuclear norm on multi-view tensor representation constructed by stacking view-specific self-representations; in addition, EMVSC exploits prior knowledge of pairwise constraints from whole dataset by employing constraint propagation, which propagates limited constraint knowledge from constrained samples to unconstrained samples. To efficiently optimize EMVSC, an extended intact augmented Lagrangian method is derived with good convergence. Experimental results on seven standard multi-view databases demonstrate its efficacy. INDEX TERMSMulti-view clustering, low-rank tensor representation, tensor singular value decomposition (T-SVD), constrained clustering.