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Development of Internet of Vehicles (IoV) has aroused extensive attention in recent years. The IoV requires an efficient communication mode when the application scenarios are complicated. To reduce the verifying time and cut the length of signature, certificateless aggregate signature (CL-AS) is used to achieve improved performance in resource-constrained environments like vehicular ad hoc networks (VANETs), which is able to make it effective in environments constrained by bandwidth and storage. However, in the real application scenarios, messages should be kept untamed, unleashed, and authentic. In addition, most of the proposed schemes tend to be easy to attack by signers or malicious entities which can be called coalition attack. In this paper, we present an improved certificateless-based authentication and aggregate signature scheme, which can properly solve the coalition attack. Moreover, the proposed scheme not only uses pseudonyms in communications to prevent vehicles from revealing their identity but also achieves considerable efficiency compared with state-of-the-art work, certificateless signature (CLS), and CL-AS schemes. Furthermore, it demonstrates that when focused on the existential forgery on adaptive chosen message attack and coalition attack, the proposed schemes can be proved secure. Also, we show that our scheme exceeds existing certification schemes in both computing and communication costs.

In order to solve the problem that bearing vibration signal fault feature is difficult to extract effectively under noise, a fault diagnosis method based on Variational Mode Decomposition (VMD) optimized by Cuckoo Search (CS) and Particle Swarm Optimization (PSO) is proposed. The effect of VMD is affected by the number of modes and the penalty parameter. The Levy flight strategy and elimination mechanism of the CS algorithm is added to PSO algorithm and the position updating process is optimized. According to the correlation coefficient and envelope entropy of the Intrinsic Mode Function (IMF), the objective function is constructed to search for the optimal combination of VMD mode number and penalty parameter. The bearing fault type is determined by analyzing the IMF envelope spectrum of the optimal objective function. The fault diagnosis classification task upon the bearing sample data from Case Western Reserve University demonstrates that the proposed CS-PSO algorithm improves the model’s classification performance by 5%, which is on average 7.3% higher than other state-of-the-art models. This model can provide an important reference for the accuracy of bearing fault diagnosis.

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