Trustworthiness in vehicular network plays a vital role in facilitating data sharing among vehicles, to achieve better driving safety and convenience. Without trustworthiness assessment, a vehicle may not be able to trust other vehicles, and therefore simply drop the data shared from others, to avoid potential driving dangers. This problem was traditionally approached by protecting data security, however, the study of the trustworthiness of data generators (vehicles) is unfortunately omitted. We envision the existences of a vehicular social network on road, wherein vehicles exchanging data between each other are considered socially connected. Leveraging the trust propagation and fusion within a vehicular social network, the trustworthiness of individual vehicles can be accurately assessed. We adopt the three-valued subjective logic model to study trust between vehicles, and propose a holistic solution to trust assessment in vehicular social networks. The proposed solution enables objective and subjective trust assessment of vehicles, in a distributed manner. Simulation results indicate that the proposed solution offers a more accurate trust assessment and a quicker assessing time.

Since its introduction, IoT (Internet of Things) has enjoyed vigorous support from governments and research institutions around the world, and remarkable achievements have been obtained. The perception layer of IoT plays an important role as a link between the IoT and the real world; the security has become a bottleneck restricting the further development of IoT. The perception layer is a self-organizing network system consisting of various resource-constrained sensor nodes through wireless communication. Accordingly, the costly encryption mechanism cannot be applied to the perception layer. In this paper, a novel lightweight data integrity protection scheme based on fragile watermark is proposed to solve the contradiction between the security and restricted resource of perception layer. To improve the security, we design a position random watermark (PRW) strategy to calculate the embedding position by temporal dynamics of sensing data. The digital watermark is generated by one-way hash function SHA-1 before embedding to the dynamic computed position. In this way, the security vulnerabilities introduced by fixed embedding position can not only be solved effectively, but also achieve zero disturbance to the data. The security analysis and simulation results show that the proposed scheme can effectively ensure the integrity of the data at low cost.

In non-destructive testing (NDT) of metal welds, weld line tracking is usually performed outdoors, where the structured light sources are always disturbed by various noises, such as sunlight, shadows, and reflections from the weld line surface. In this paper, we design a cross structured light (CSL) to detect the weld line and propose a robust laser stripe segmentation algorithm to overcome the noises in structured light images. An adaptive monochromatic space is applied to preprocess the image with ambient noises. In the monochromatic image, the laser stripe obtained is recovered as a multichannel signal by minimum entropy deconvolution. Lastly, the stripe centre points are extracted from the image. In experiments, the CSL sensor and the proposed algorithm are applied to guide a wall climbing robot inspecting the weld line of a wind power tower. The experimental results show that the CSL sensor can capture the 3D information of the welds with high accuracy, and the proposed algorithm contributes to the weld line inspection and the robot navigation.

Large amounts of data are widely stored in cyberspace. Not only can they bring much convenience to people’s lives and work, but they can also assist the work in the information security field, such as microexpression recognition and sentiment analysis in the criminal investigation. Thus, it is of great significance to recognize and analyze the sentiment information, which is usually described by different modalities. Due to the correlation among different modalities data, multimodal can provide more comprehensive and robust information than unimodal in data analysis tasks. The complementary information from different modalities can be obtained by multimodal fusion methods. These approaches can process multimodal data through fusion algorithms and ensure the accuracy of the information used for subsequent classification or prediction tasks. In this study, a two-level multimodal fusion (TlMF) method with both data-level and decision-level fusion is proposed to achieve the sentiment analysis task. In the data-level fusion stage, a tensor fusion network is utilized to obtain the text-audio and text-video embeddings by fusing the text with audio and video features, respectively. During the decision-level fusion stage, the soft fusion method is adopted to fuse the classification or prediction results of the upstream classifiers, so that the final classification or prediction results can be as accurate as possible. The proposed method is tested on the CMU-MOSI, CMU-MOSEI, and IEMOCAP datasets, and the empirical results and ablation studies confirm the effectiveness of TlMF in capturing useful information from all the test modalities.

Attribute-Based Encryption (ABE) has been caught a wide range of attention owing to its advantage of achieving both fine-grained access control and protection for data. In recent years, the constructions of ABE have developed to lattice-based schemes, while most of them exist bottlenecks in efficiency and security as a result of constructing on the Learning With Errors (LWE) assumption and relying on a single central authority. In this paper, we propose a decentralized multi-authority Ciphertext-Policy Attribute-Based Encryption (CP-ABE) scheme from the Ring Learning With Errors (R-LWE) assumption, which supports flexible access policies and is built on a new trapdoor with efficient algorithms. Moreover, in our system, management and distribution for the private keys are accomplished by multiple authorities utilizing the technique of Shamir threshold secret sharing, which makes it more efficient and applicable to the distributed storing environments. We then prove that the scheme is selective secure under chosen plaintext attacks. The experimental results manifest that the proposed scheme can be carried out in a comparatively short amount of time, which confirms its practicality.