The Internet of Things (IoT) technology and systems have penetrated every aspect of our lives and generated enormous economic benefits. At the same time, research on the data security of IoT systems has been one of the key topics in IoT fields. Network attacks and intrusions have become the main threats to the data security of the IoTs, which have become the main obstacles to the development and application of the IoTs. In this article, we propose an intrusions and attack detection model to ensure the data security of IoT systems by using the Transformer model and multiwavelets learning. Based on the architecture of IoT systems, we first proposed a multi-level intrusion detection model to detect attack data in the cloud layer and edge terminal layer. In this detection model, a Transformer model and discrete wavelet transform (DWT) based approach is proposed to ensure the effectiveness and accuracy of the model. To extract and make full use of frequency information of traffic data in an IoT network, we embed DWT technique and multiwavelets learning into the Transformer model to propose a novel DWT-based Transformer architecture, which achieves outstanding performance in detecting intrusion actions. Simulating on IoT system in laboratory environment, the proposed security prediction model achieves pretty good performance in predicting intrusion actions.

The purpose of this paper is to explore an intelligent identification method of autonomous vehicle moving trajectory based on friction nano-generator. This method uses friction nano-generator to obtain energy from the friction between the vehicle tire and the ground, and realizes the perception and recognition of the vehicle motion state. On this basis, through the analysis and processing of the motion state data, an intelligent identification model of the moving trajectory of autonomous vehicles is established to realize the intelligent prediction and control of the driving trajectory of vehicles. Therefore, a large number of vehicle movement state data is collected, and the data are preprocessed and feature extracted, and an intelligent recognition model of vehicle movement trajectory is constructed by machine learning method. Finally, the accuracy and stability of the model are verified by experiments, and the feasibility and practicability of the method are proved. The results show that the intelligent identification method of autonomous vehicle trajectory based on friction nano-generator has high accuracy and practicability. In the field verification environment, the lateral position deviation, heading angle deviation and minimum radius of curvature of the trajectory recognition algorithm for autonomous vehicles are 0.2193m, 10deg and 5.9m, respectively. The lateral deviation of the real vehicle test is kept within 0.5m, and the lateral acceleration is infinitely close to zero. This autonomous path identification is extremely stable. This method can not only realize intelligent prediction and control of vehicle trajectory, but also provide data support for self-learning and optimization of autonomous vehicles.

Summary Mobile social networks (MSNs) are developed from mobile ad hoc networks. Nodes in such networks usually have social characteristics. In recent years, researchers are trying to use the social characteristics of the network to propose new data forwarding metrics, so as to design more efficient routing algorithms. However, most of the proposed algorithms only consider local context information, which leads to the performance of the routing is not optimized enough. In this paper, we introduce two key metrics, namely, social relationship and social activity. The metrics will be used to search the best data forwarding nodes to improve the probability of data delivery. We propose a prediction‐based social‐aware opportunistic routing (PSOR). In the proposed method, node's social profiles are used to search relay candidates set, and the discrete‐time semi‐Markov prediction model is used to find the probability distribution of node transition between communities. Many simulation experiments based on real traces show that the proposed PSOR algorithm is more efficient to maximize the packet delivery probability than other state‐of‐the‐art algorithms.

The rapid development of the social network has brought great convenience to people's lives. A large amount of cross-media big data, such as text, image, and video data, has been accumulated. A cross-media search can facilitate a quick query of information so that users can obtain helpful content for social networks. However, cross-media data suffer from semantic gaps and sparsity in social networks, which bring challenges to cross-media searches. To alleviate the semantic gaps and sparsity, we propose a cross-media search method based on complementary attention and generative adversarial networks (CAGS). To obtain high-quality feature representations, we build a complementary attention mechanism containing the focused and unfocused features of images to realize the consistent association of cross-media data in social networks. By designing the cross-media adversarial learning process, we can obtain a common semantic representation of cross-media data and further alleviate the semantic gap and sparsity issues for social networks. Finally, we perform a similarity calculation to realize an accurate cross-media search. We construct four search tasks utilizing two standard cross-media data sets to verify the search performance of the proposed CAGS.

With the rapid development of Cooperative Intelligent Transportation System (C-ITS), it becomes an urgent problem to effectively evaluate the data transmission efficiency of code dissemination protocols with network coding in the Dedicated Transportation Sensor Network (DTSN). First, this paper builds the overall structure of DTSN to remotely monitor the railway infrastructure in the C-ITS. Second, we propose a time model of page dissemination to reduce the deviation between the predictive data of existing models and the real-world data of code dissemination. Third, a Firefly Algorithm is designed to further improve the data transmission efficiency. The algorithm makes the feasibility rules to handle constraints, and it searches the optimal page granularity to minimize the time of page dissemination through iteration. Experiments show that the simulation results are consistent with the prediction results of proposed model, and code images can be distributed quickly and efficiently in the DTSN.

Fabric defect detection (FDD) plays an important role in the quality control in textile industry. In this study, the authors propose an efficient FDD method by using the saliency analysis of multi‐scale local steering kernel (LSK). In the proposed method, a given RGB fabric image is first converted into the Commission International Eclairage (CIE) L*a*b colour space and then the LSK in each colour channel is computed by the singular value decomposition and the centre surrounding definition. Next, the matrix cosine similarity is employed to measure the similarity between different LSK features for generating the desired defective maps. Finally, a multi‐scale averaging fusion scheme is applied to integrate the obtained defective maps at different scales for the final defective map. The experimental results indicate that the proposed method achieves the state‐of‐the‐art performance on FDD compared to the other competitors.

Summary Recommender systems have shown great potential to address information overload problems, namely, to help users find interesting and relevant objects within a huge information space. To achieve more accurate recommendation, in this paper, we proposed a recommendation algorithm Improved weighted Network‐Based Inference (INBIw) that improves on the original weighted network‐based inference by introducing a tunable parameter β to depress the influence of high‐degree nodes. In order to evaluate the recommendation performance of INBIw, ranking position rate and hitting rate are calculated. The results of experiment based on MovieLens data set show that the INBIw outperforms previous methods, including the global ranking method, collaborative filtering, network‐based inference, and weighted network‐based inference with respect to ranking position rate and hitting rate. Specifically, it performs well and gives a more accurate prediction. After further analysis, we discovered that the recommendation results of INBIw are insensitive to the amount of data and length of the recommendation list. Thus, INBIw can deal with data sparsity and is able to satisfy the varied requirements of real situations. Copyright © 2016 John Wiley & Sons, Ltd.

With the advent of social networks and cloud computing, the amount of multimedia data produced and communicated within social networks is rapidly increasing. In the meantime, social networking platforms based on cloud computing have made multimedia big data sharing in social networks easier and more efficient. The growth of social multimedia, as demonstrated by social networking sites such as Facebook and YouTube, combined with advances in multimedia content analysis, underscores potential risks for malicious use, such as illegal copying, piracy, plagiarism, and misappropriation. Therefore, secure multimedia sharing and traitor tracing issues have become critical and urgent in social networks. In this article, a joint fingerprinting and encryption (JFE) scheme based on tree-structured Haar wavelet transform (TSHWT) is proposed with the purpose of protecting media distribution in social network environments. The motivation is to map hierarchical community structure of social networks into a tree structure of Haar wavelet transform for fingerprinting and encryption. First, fingerprint code is produced using social network analysis (SNA). Second, the content is decomposed based on the structure of fingerprint code by the TSHWT. Then, the content is fingerprinted and encrypted in the TSHWT domain. Finally, the encrypted contents are delivered to users via hybrid multicast-unicast. The proposed method, to the best of our knowledge, is the first scalable JFE method for fingerprinting and encryption in the TSHWT domain using SNA. The use of fingerprinting along with encryption using SNA not only provides a double layer of protection for social multimedia sharing in social network environment but also avoids big data superposition effect. Theory analysis and experimental results show the effectiveness of the proposed JFE scheme.

Example learning-based superresolution (SR) algorithms show promise for restoring a high-resolution (HR) image from a single low-resolution (LR) input. The most popular approaches, however, are either time- or space-intensive, which limits their practical applications in many resource-limited settings. In this paper, we propose a novel computationally efficient single image SR method that learns multiple linear mappings (MLM) to directly transform LR feature subspaces into HR subspaces. In particular, we first partition the large nonlinear feature space of LR images into a cluster of linear subspaces. Multiple LR subdictionaries are then learned, followed by inferring the corresponding HR subdictionaries based on the assumption that the LR-HR features share the same representation coefficients. We establish MLM from the input LR features to the desired HR outputs in order to achieve fast yet stable SR recovery. Furthermore, in order to suppress displeasing artifacts generated by the MLM-based method, we apply a fast nonlocal means algorithm to construct a simple yet effective similarity-based regularization term for SR enhancement. Experimental results indicate that our approach is both quantitatively and qualitatively superior to other application-oriented SR methods, while maintaining relatively low time and space complexity.

With the advent of social networks and cloud computing, the amount of multimedia data produced and communicated within social networks is rapidly increasing. In the mean time, social networking platform based on cloud computing has made multimedia big data sharing in social network easier and more efficient. The growth of social multimedia, as demonstrated by social networking sites such as Facebook and YouTube, combined with advances in multimedia content analysis, underscores potential risks for malicious use such as illegal copying, piracy, plagiarism, and misappropriation. Therefore, secure multimedia sharing and traitor tracing issues have become critical and urgent in social network. In this paper, we propose a scheme for implementing the Tree-Structured Harr (TSH) transform in a homomorphic encrypted domain for fingerprinting using social network analysis with the purpose of protecting media distribution in social networks. The motivation is to map hierarchical community structure of social network into tree structure of TSH transform for JPEG2000 coding, encryption and fingerprinting. Firstly, the fingerprint code is produced using social network analysis. Secondly, the encrypted content is decomposed by the TSH transform. Thirdly, the content is fingerprinted in the TSH transform domain. At last, the encrypted and fingerprinted contents are delivered to users via hybrid multicast-unicast. The use of fingerprinting along with encryption can provide a double-layer of protection to media sharing in social networks. Theory analysis and experimental results show the effectiveness of the proposed scheme.