Benefiting from the Internet of Vehicles (IoV), modern vehicles are no longer just transportation, but enablers of safe, intelligent, and efficient driving and mobility services. Vehicles increasingly demand environmental information and entertainment resources during operation. These resources are hosted in the cloud, and vehicle terminals query and access them via keywords. However, the ciphertext of resource keywords and trapdoor are transmitted in complex network environments, where simple encryption methods are highly vulnerable to keyword guessing attacks (KGA). Furthermore, single query methods and suboptimal resource sharing mechanisms are no longer viable. Thus, we propose an encrypted resource sharing scheme resisting keyword guessing attacks (ERSRKGA) for the IoV. The ERSRKGA ensures information security through advanced encryption and decryption technologies, combined with re-randomization during transmission. Based on attribute-based encryption, vehicles obtain legitimate attribute weights and participate in resource sharing via a confidential attribute authentication mechanism. The scheme's flexible and refined query functionality meets vehicles' diverse search requirements, including AND, NOT, and wildcard operators, and realizes fine-grained access control. Subsequently, the correctness of ERSRKGA and its resistance to KGA are verified. We compare ERSRKGA with other state-of-the-art schemes in terms of functionality, computational complexity, time overhead, and energy consumption. Experimental results demonstrate that ERSRKGA outperforms the compared schemes.

Under specific environmental conditions such as dense fog or high dust levels, conventional RGB imaging technology faces significant challenges in capturing clear images. In contrast, infrared imaging technology, due to its unique characteristics, can effectively acquire images under these adverse conditions. However, the high cost associated with improving image quality through hardware enhancements in infrared imaging makes software-based image quality improvement crucial. Recent studies have demonstrated that deep learning networks hold significant potential for enhancing the quality of super-resolution images. To address the issues of gradient vanishing, insufficient feature utilization, and feature redundancy present in deep learning networks, this paper proposes a dual-channel hybrid convolutional residual network based on CNN with super-resolution of infrared images, which combines dual-feature extraction and dense linking. The network employs channel splitting to effectively reduce feature redundancy and leverages residual and mixed convolution techniques to enhance feature extraction and utilization. This approach efficiently preserves image details while eliminating noise. Comparative analysis using qualitative and quantitative metrics demonstrates the effectiveness of the proposed network for infrared image super-resolution tasks. The effectiveness of the network proposed in this paper in the task of super-resolution of infrared images is demonstrated by comparing it with other methods in terms of qualitative and quantitative metrics.

In the field of computer vision, image denoising remains a fundamental and challenging problem, playing a crucial role in the preprocessing of various image processing tasks. The introduction of Convolutional Neural Networks (CNNs) into the image denoising domain has yielded significant improvements across different levels of visual tasks. In recent years, models based on the Swin Transformer have also been applied to the image denoising field, demonstrating superior denoising performance that surpasses CNN-based methods, thus becoming advanced techniques in current image denoising research. This paper proposes a Swin-Conv module that combines the local modeling capabilities of residual convolutional layers with the non-local modeling capabilities of the Swin Transformer and integrates this module into the UNet architecture for image denoising. For the dataset used in the model training process, data augmentation techniques were employed to randomly enhance the dataset, thereby improving overall robustness. The results indicate that the proposed Swin Transformer Residual Conv U-Net model shows improvement over current advanced networks, achieving PSNR and SSIM values of 36.09 and 0.963 at $\sigma=15,33.87$ and 0.915 at $\sigma=25$, and 28.96 and 0.810 at $\sigma=50$.

Addressing challenges in infrared imaging, such as low contrast, blurriness, and detail scarcity due to environmental limitations and the target’s limited radiative capacity, this research introduces a novel infrared image enhancement approach using low‐light image guidance. Initially, the Cbc‐SwinIR model (coordinate‐based convolution‐ image restoration using Swin Transformer) is applied for super‐resolution reconstruction of both shimmer and infrared images, improving their resolution and clarity. Next, the MAXIM model (multiaxis MLP for image processing) enhances the visibility of low‐light images under low illumination. Finally, the AILI (adaptive infrared and low‐light)‐fusion algorithm fuses the processed low‐light image with the infrared image, achieving comprehensive visual enhancement. The enhanced infrared image exhibits significant improvements: a 0.08 increase in fractal dimension (FD), 0.094 rise in information entropy, 0.00512 elevation in mean square error (MSE), and a 12.206 reduction in peak signal‐to‐noise ratio (PSNR). These advancements in FD and information entropy highlight a substantial improvement in the complexity and diversity of the infrared image’s features. Despite a decrease in PSNR and an increase in MSE, this indicates that the newly introduced information enhances contrast and enriches texture details in the infrared images, resulting in pixel‐level variations. This methodology demonstrates considerable improvements in visual content and analytical value, proving relevant, innovative, and efficient in infrared image enhancement with broad application prospects.

For group signature (GS) supporting membership revocation, verifier-local revocation (VLR) mechanism seems to be a more flexible choice, because it requires only that verifiers download up-to-date revocation information for signature verification, and the signers are not involved. As a post-quantum secure cryptographic counterpart of classical number-theoretic cryptographic constructions, the first lattice-based VLR group signature (VLR-GS) was introduced by Langlois et al. (2014). However, none of the contemporary lattice-based VLR-GS schemes provide backward unlinkability (BU), which is an important property to ensure that previously issued signatures remain anonymous and unlinkable even after the corresponding signer (i.e., member) is revoked. In this study, we introduce the first lattice-based VLR-GS scheme with BU security (VLR-GS-BU), and thus resolve a prominent open problem posed by previous works. Our new scheme enjoys an $${\cal O}\left( {\log \,N} \right)$$ factor saving for bit-sizes of the group public-key (GPK) and the member’s signing secret-key, and it is free of any public-key encryption. In the random oracle model, our scheme is proven secure under two well-known hardness assumptions of the short integer solution (SIS) problem and learning with errors (LWE) problem.

With the development of the globalization economic integration in Internet of Things (IoT), it is very crucial to protect the wireless two-way authentication between users’ intelligent terminals and servers in the product authorization chain. In order to ensure that legitimate users connect to the wireless network correctly, a lightweight wireless mutual authentication scheme for the product authorization chain was proposed contrapose to the security defect of Kaul and Awasthi’s scheme, which easily suffered from offline password guessing attack. The improved scheme uses lightweight hash function and verifies the freshness of messages by using the send packet sequence number instead of timestamp, which can avoid strict clock synchronization between devices, and user passwords can be updated by themselves. Security analysis and cost and efficiency analysis show that the scheme presented in this paper has higher security, lower storage and communication costs, and lower computational complexity.

The recent development of wireless body area network (WBAN) technology plays a significant role in the modern healthcare system for patient health monitoring. However, owing to the open nature of the wireless channel and the sensitivity of the transmitted messages, the data security and privacy threats in WBAN have been widely discussed and must be solved. In recent years, many authentication protocols had been proposed to provide security and privacy protection in WBANs. However, many of these schemes are not computationally efficient in the authentication process. Inspired by these studies, a lightweight and secure anonymous authentication protocol is presented to provide data security and privacy for WBANs. The proposed scheme adopts a random value and hash function to provide user anonymity. Besides, the proposed protocol can provide user authentication without a trusted third party, which makes the proposed scheme have no computational bottleneck in terms of architecture. Finally, the security and performance analyses demonstrate that the proposed scheme can meet security requirements with low computational and communication costs.

With the rapid development of communication and microelectronic technology, the vehicular ad hoc network (VANET) has received extensive attention. However, due to the open nature of wireless communication links, it will cause VANET to generate many network security issues such as data leakage, network hijacking, and eavesdropping. To solve the above problem, this paper proposes a new authentication protocol which uses bilinear pairings and temporary pseudonyms. The proposed authentication protocol can realize functions such as the identity authentication of the vehicle and the verification of the message sent by the vehicle. Moreover, the proposed authentication protocol is capable of preventing any party (peer vehicles, service providers, etc.) from tracking the vehicle. To improve the efficiency of message verification, this paper also presents a batch authentication method for the vehicle to verify all messages received within a certain period of time. Finally, through security and performance analysis, it is actually easy to find that the proposed authentication protocol can not only resist various security threats but also have good computing and communication performance in the VANET environment.

Summary IP geolocation is usually used in fog computing to avoid high latency and discriminate malicious requests by judging the location of users. Existing delay measurement‐based IP geolocation approaches are not applicable to the network that has hierarchical topology and weak connectivity, and the precision of the classical Street‐Level Geolocation (SLG) method will decrease dramatically when the common routers are anonymous. In this paper, an IP geolocation method based on identification routers and local delay distribution similarity is proposed. The target IP's location at city‐level is firstly derived by matching its routing path with the identification routers that only forward packets to the same city. After that, the target IP's local delay between the nearest common router and the target IP is gathered, and the landmarks' are obtained at the same time. Finally, the location of the landmark that has the most similar local delay distribution with the target IP is taken as the geolocation result. Theoretical analysis and experimental results show that the proposed method can derive reliably geolocation results at city‐level for the target IP in the network with hierarchical architecture. Moreover, the geolocation accuracy of classical SLG method is improved obviously when the common routers are anonymous.

Smog causes low visibility on the road and it can impact the safety of traffic. Modeling traffic in smog will have a significant impact on realistic traffic simulations. Most existing traffic models assume that drivers have optimal vision in the simulations, making these simulations are not suitable for modeling smog weather conditions. In this article, we introduce the Smog Full Velocity Difference Model (SMOG-FVDM) for a realistic simulation of traffic in smog weather conditions. In this model, we present a stadia model for drivers in smog conditions. We introduce it into a car-following traffic model using both psychological force and body force concepts, and then we introduce the SMOG-FVDM. Considering that there are lots of parameters in the SMOG-FVDM, we design a visual verification system based on SMOG-FVDM to arrive at an adequate solution which can show visual simulation results under different road scenarios and different degrees of smog by reconciling the parameters. Experimental results show that our model can give a realistic and efficient traffic simulation of smog weather conditions.

HDFS is a popular distributed file system, widely used in many commercial fields, which can store TB, even PB level data. Fast data reading and writing is the most important problem for HDFS. However, with the volume of data increasing sharply, the traditional HDFS, built on the PC cluster platform, is no longer suitable for fast data reading and writing. GPU is a highly parallel computing unit. Its power of calculation, reading and writing is hundreds of times as fast as CPU. Hence, this paper proposes an improved distributed file system, which uses GPU as an accelerator. Firstly, the improved HDFS uses GPU instead of CPU response data reading and writing requests. Secondly, the improved HDFS uses GPU's cache as a buffer memory for data reading and writing. These two strategies significantly improve the performance of the distributed file system. The experimental results have proved the effectiveness of the improved algorithm.

The Local Binary Pattern (LBP) and its variants have been widely investigated in image processing and computer vision applications, e.g., texture classification due to their powerful ability to capture image features and computational simplicity. However, owing to the simple selection strategy of the threshold, the original LBP descriptor is sensitive to noise and illumination variations and tends to characterize different local patterns with the same binary code. Recently, the Completed Robust Local Binary Pattern (CRLBP) has been introduced to overcome these demerits, in which the Weighted Local Gray Level (WLG) is introduced to replace the traditional gray value of the center pixel, but the improvement is not significant and one additional parameter has to be tuned. To address these difficulties effectively, this paper proposes a hybrid framework of LBP, called Completed Hybrid Local Binary Pattern (CHLBP), in which a first order derivative and a second order derivative are combined to represent local patterns. In order to make CHLBP more robust and stable, more relationship information among pixels in the local region is exploited, that is, the Average Local Gray Level (ALG) is adopted to take place of the traditional gray value of the center pixel as well as the neighbor pixels. The results obtained from two representative texture databases show that the proposed method is robust to illuminant variations and viewpoint variations and can achieve impressive classification accuracy. The proposed model improves the classification results from 96.95% to 98.78% on the Outex database, and from 91.85% to 94.56% on the UIUC database as compared with the Completed Local Binary Pattern (CLBP), which is the benchmark method of LBP-based models.

The prediction of an image quality metric (IQM) should be consistent with subjective human evaluation. As the human visual system (HVS) is critical to visual perception, modeling of the HVS is regarded as the most suitable way to achieve perceptual quality predictions. Sparse coding that is equivalent to independent component analysis (ICA) can provide a very good description of the receptive fields of simple cells in the primary visual cortex, which is the most important part of the HVS. With this inspiration, a quality metric called sparse feature fidelity (SFF) is proposed for full-reference image quality assessment (IQA) on the basis of transformation of images into sparse representations in the primary visual cortex. The proposed method is based on the sparse features that are acquired by a feature detector, which is trained on samples of natural images by an ICA algorithm. In addition, two strategies are designed to simulate the properties of the visual perception: 1) visual attention and 2) visual threshold. The computation of SFF has two stages: training and fidelity computation, in addition, the fidelity computation consists of two components: feature similarity and luminance correlation. The feature similarity measures the structure differences between the two images, whereas the luminance correlation evaluates brightness distortions. SFF also reflects the chromatic properties of the HVS, and it is very effective for color IQA. The experimental results on five image databases show that SFF has a better performance in matching subjective ratings compared with the leading IQMs.

The theory of modern immunology provides a novel idea to study network intrusion detection and defence system. Inspired information processing in biology immune system is a highly parallel and distributed intelligent computation which has learning, memory, and associative retrieval capabilities. The architecture of multi-agent in depth defence based on immune principle is proposed. The agents of intrusion detection detect all intrusion which passes by the agent, including known and unknown. The information of new intrusion, which gotten from current monitored network is encapsulated and sent to the other network as vaccine by mobile agents. So the other network can prevent the same intrusion. Intrusion packets are prevented from gateway of intrusion source by response agent. The experimental results show that the new model actualizes an active and distributed prevention policy than that of the traditional passive intrusion prevention systems.

The concepts of self, nonself, antibody, antigen and vaccine in in-depth-defense system for network security was presented in this paper, the architecture of in-depth defense for network intrusion and detection based on immune principle is proposed. The intrusion information gotten from current monitored network is encapsulated and sent to the neighbor network as bacterin; therefore the neighbor network can make use of the bacterin and predict the danger of network. We can use communicate agent cooperated with response agent to achieve active defense formwork. The experimental results show that the new model not only actualizes an active prevention method but also improves the ability of intrusion detection and prevention than that of the traditional passive intrusion prevention systems.

Motion control system of underwater robot without rudder and wing is presented with hardware and software architecture. Considering coupling effects and thrust reduction of propellers, the control layer, perception layer and executive layer in underwater robot system architecture are modified. In control layer, a nonlinear controller is presented to handle coupling effects between the longitudinal dimension and other dimensions of underwater robot. The stability of the controller is verified with Lyapunov function. A virtual sonar based guidance law is proposed in perception layer to coordinate motions on different dimensions, which originates from the behavior of human beings. In executive layer a kind of thruster configuration for high speed traveling is introduced to handle thrust reduction of tunnel thrusters. Finally, the reliability and feasibility of the motion control system are demonstrated by sea trial