With the widespread adoption of mobile devices, an increasing number of users are engaging in social interactions through these devices. Mobile social networks have thus emerged in response. Existing research has shown that utilizing mobile social relations can effectively enhance the performance of recommendation systems. However, most studies only exploit single social relations such as pairwise relations, overlooking the effect of high-order complexity of user relations which contain some potentially beneficial information. What's more, they ignore the impact of the trusters who provide some potential feedbacks in the mobile social network. Therefore, this paper proposes our framework H2TRec using hypergraph convolution in the pretraining stage to learn high-order neighbor information in the mobile social network and utilizing the metapath-based GAT to model users' bidirectional trust relations. First, mobile social communities are partitioned by random walk based on the fusion graph which consolidates all different nodes and relations. Second, each mobile social community is represented as a hyperedge to construct the hypergraph and the high-order neighbor prior knowledge is learned using hypergraph convolution. Third, implicit relations are mined and the metapath-based GAT is utilized to model the preferences of users and items. Notably, unlike previous work, we consider mobile users' out-degree and in-degree features, which enhance the user embeddings. Additionally, a loss term aiming to improve centrality is added to make the preference features of mobile social communities more prominent. Extensive experiments on five popular real-world datasets demonstrate that our H2TRec can improve precision compared with state-of-the-art methods. We release the source code athttps://github.com/kangkang-yun/H2TRec.

Accurate long-term traffic forecasting improves traffic efficiency and safety in Intelligent Transportation Systems (ITS). However, previous methods typically overlook the complex mixed characteristics. So they struggle to capture the intricate features of newly added data effectively under distribution shifts, which exacerbates the complexity of spatial-temporal variations. Moreover, these methods fail to model global and local dynamic spatial correlations effectively, efficiently, and comprehensively. To mitigate these issues, this paper proposes an innovative Spatial-Temporal Wavelet Attention Network (STWAN). STWAN first decomposes traffic data into stable trends and fluctuating events, effectively dealing with the adverse effects of distribution shifts. Then, spatial-temporal encoder captures component-specific temporal variations and extracts dynamic global-local spatial correlations comprehensively with linear computational complexity. Additionally, transformer attention and forecasting decoder model the latent patterns, while trend-event fusion module integrates essential information for accurate forecasts. Comprehensive experiments across two real-world traffic forecasting tasks indicate that STWAN significantly outperforms state-of-the-art methods in terms of accuracy and robustness, attaining a maximum reduction of 5.54% in MAE and showcasing its robustness and broad applicability in long-term forecasting.

Coverage maximization under full connection constraints involves many factors and poses huge challenge in mobile wireless sensor networks. Most of research works on this issue are based on the disk model, which have high complexity and long iterations, and therefore cannot be applied to dynamic complex networks. In this paper, the problem of confident information coverage maximization under full connectivity constraints (CIC-CC) is defined based on the confident information coverage model (CIC). To address this problem, a 3-stage connectivity constrained coverage maximization algorithm (3-CCC) is proposed with the time complexity of O(T*n2). 3-CCC contains three stages: maximizing coverage (MC), full connectivity (FC), and maximizing connectivity constrained coverage (MCCC). These three stages can be used in whole or in part to achieve coverage maximization with connectivity constraints depending on the network state. The stable matching mechanism, greedy algorithm, and Q-learning are utilized to improve the algorithm’s efficiency. Experiments show that the proposed algorithm has good performance in terms of iteration number, running time, and coverage rate.

Industrial Internet of Things (IIoT) is evolving toward decentralization and autonomous operation. Nodes in decentralized IIoT collaboratively sense data and communicate to participate in meta-computing and provide diverse intelligent services. Area coverage reliability evaluates the information collaborative intelligence sensing and communication capabilities of nodes in decentralized IIoT for a target area. It serves as a crucial performance evaluation metric for the meta-computing and intelligent services of decentralized IIoT. Existing area coverage reliability evaluation algorithms do not consider the dynamic node states in decentralized IIoT, and overlook the capability of nodes to collaborate in meta-computing. To address these issues, this article proposes a novel confident information coverage (CIC)-based area coverage reliability metric (CACREL), which comprehensively considers node multistate, node collaboration, network coverage rate, and network connectivity. To effectively evaluate CACREL, a CIC-based coverage reliability evaluation algorithm (CACR) is proposed. Specifically, CACR transforms complex networks into grid networks and utilizes a coverage table (CT) to describe different coverage states of each grid, which reduces the complexity of the area coverage reliability evaluation. Additionally, CACR employs a reliable path algorithm to converge the CT of each grid to the sink node based on grid connectivity. Simulation results demonstrate that the proposed CACR can accurately evaluate the area coverage reliability of decentralized IIoT and enhance its service performance.

Wireless sensor network plays a crucial role in sensing everything in Internet of Everything (IoE) applications. Network reliability, which measures the ability of the network to satisfy specific requirements, is one of the core factors influencing the quality of service of the network and a vital support for ensuring the normal operation of IoE applications. Existing reliability evaluation methods are mainly based on minimum cutsets or paths, which are inefficient and not suitable for large-scale networks. Furthermore, most work either focuses on coverage functionality or connectivity functionality, lacking energy awareness. To address these limitations, this article proposes a multidimensional target full-coverage reliability (TFCR). TFCR comprehensively considers various factors affecting network reliability. To evaluate TFCR, a graph-empowered confident information coverage (CIC) and signal-to-interference and noise ratio (SINR)-based energy-aware reliability algorithm (CSERA) is proposed. This algorithm evaluates network coverage based on the CIC model. Additionally, graph neural networks and the SINR-based fade tail connectivity (FTC) model are used to evaluate network connectivity functionality. CSERA balances computational accuracy and efficiency, providing reliability evaluation values within an acceptable margin of error. Extensive simulations and comparative experiments from multiple perspectives demonstrate the superiority of the proposed method CSERA over existing approaches.

The Cloud-Fog Automation is a newly proposed digital industrial automation architecture aimed at accelerating the integration and collaboration of communication, computing, and control towards next-generation cyber-physical systems (CPSs). Data reliability is one of the key considerations for achieving Cloud-Fog Automation. Sensor nodes serve as infrastructures for data collection within industrial CPSs and are essential for maintaining ultra-high data reliability. However, the underlying sensor nodes communicate frequently, are damage-prone and difficult to identify, which dramatically shortens the network lifetime and poses great challenges to data reliability. Motivated by this fact, this paper co-designs communication architecture, algorithms, and computing models for next-generation industrial CPSs with Cloud-Fog Automation to ensure data reliability and functional security. First, a four-layer energy-efficient communication architecture is proposed and a cluster head computing algorithm based on double deep Q-learning (CH-DDQ) is designed inside the architecture. Besides, a 2-stage hyBrid fault detection scheme (2-Brain) is proposed for underlying sensor nodes. 2-Brain first incorporates the Obstacle Triple Jump Protocol (OTP) and OTP packets to improve hard fault detection performance. Then, an unsupervised sensor reading soft fault detection model (SR-SFD) based on contrastive learning, momentum, and tensor is adopted to learn discriminative representations of sensor readings and identify soft faults. Simulations and a case study in the nuclear power industry manifest CH-DDQ improves the network lifetime by 5.4%~484.3% compared to three peer methods, and OTP performs better than baselines by 33.1% on average. Additionally, SR-SFD exhibits high efficiency in sensor soft fault detection and other application scenarios.

The large-scale applications of wireless sensor networks (WSNs) place higher demands on their reliability. WSNs deployed in the intelligent tunnel are often affected by environmental interference or malicious intrusions, which lead to untrusted paths and affect the reliability of transmission. A trusted path ensures that the collected information can be successfully and reliably transmitted to the sink node. To solve the transmission reliability problem of wireless sensor networks, a trusted multi-source shortest path-based transmission reliability (TMSPR) algorithm is proposed in this paper. A lightweight trust management model with a node relation matrix (RM) is applied to identify and exclude untrusted nodes, thereby establishing secure transmission links. Meanwhile, the shortest transmission path is selected based on the minimum path to save the energy of the nodes. The information transmitted through trusted multi-source shortest paths (TMSPs) can successfully reach the sink node, which significantly improves the transmission reliability of the network. Furthermore, a transmission reliability indexTRelis defined as a probabilistic measure to assess reliability. Simulation results demonstrate that the proposed algorithm exponentially reduces both computation time and memory usage, while enhancing transmission reliability by approximately 5%.

Identifying unseen faults is a crux of the digital transformation of process manufacturing. The ever-changing manufacturing process requires preset models to cope with unseen problems. However, most current works focus on recognizing objects seen during the training phase. Conventional zero-shot recognition methods perform poorly when they are applied directly to these tasks due to the different scenarios and limited generalizability. This article yields a tensor-based zero-shot fault diagnosis framework, termed MetaEvolver, which is dedicated to improving fault diagnosis accuracy and unseen domain generalizability for practical process manufacturing scenarios. MetaEvolver learns to evolve the dual prototype distributions for each uncertain meta-domain from seen faults and then adapt to unseen faults. We first propose the concept of the uncertain meta-domain and then construct corresponding sample prototypes with the guidance of class-level attributes, which produce the sample-attribute alignment at the prototype level. MetaEvolver further collaboratively evolves the uncertain meta-domain dual prototypes by injecting the prototype distribution information of another modality, boosting the sample-attribute alignment at the distribution level. Building on the uncertain meta-domain strategy, MetaEvolver is prone to achieving knowledge transferring and unseen domain generalization with the optimization of several devised loss functions. Comprehensive experimental results on five process manufacturing data groups and five zero-shot benchmarks demonstrate that our MetaEvolver has great superiority and potential to tackle zero-shot fault diagnosis for smart process manufacturing.

Internet of Things (IoT) reliability evaluation contributes to the sustainable computing and enhanced stability of the network. Previous algorithms usually evaluate the reliability of IoT by enumenating the states of nodes and networks, which are difficult to handle IoT with hundreds of nodes because the computational cost. In this paper, a novel algorithm, TMCRA, is proposed to evaluate the reliability of IoT in complex network environment, which consider both coverage and connectivity. For coverage, TMCRA employs the Confident Information Coverage (CIC) model to divide the target area into independent grids and calculates the coverage rate. In terms of connectivity, TMCRA forming the Virtual Backbone Network (VBN) based on two proposed methods: TMA and MGIN, and evaluate connectivity by analyzing the VBN rather than the whole network. The TMA and MGIN are two algorithms for constructing Minimum Connected Dominant Sets (MCDS), which are suitable for different scale networks. Finally, based on the data of coverage and connectivity, TMCRA utilizes tensors for the unified modeling and representation of network structure, and calculates IoT reliability based on the tensors. Simulations are carried out for various sizes of IoT to show the advantages and effectiveness of the proposed approach in reliability evaluation.

\beginabstract Social network has obtained extensive attention in recommender system. Existing social recommendation models mostly leverage social relations to capture potential interactions between users and items, thereby enhancing recommendation performance. However, these methods ignore the fine-grained bidirectional trust weight and the constraint on the relative positions of entities in social network and user-item interaction network. To this end, in this paper, we propose a social recommendation framework with Trust mining and multi-semantic reGularization (TrustGo). Specifically, we firstly construct a trust network based on the observed social network and establish a high-quality item implicit network. Then, we integrate the trust network, item implicit network, and user-item interaction network into a heterogeneous network. We introduce a meta-path based aggregation in this heterogeneous network to map the users and items into a latent space. And then, by using an ensemble method, we can obtain the final prediction ratings. Considering the users' different behaviors in social network and user-item interaction network, we define two semantic spaces, i.e., the social semantic space and user-item interactional semantic space. And a multi-semantic regularization module is designed to adjust the relative positions of entities in the two kinds of semantic spaces, respectively. Extensive experiments on three real-world datasets demonstrate that our TrustGo model is superior to other state-of-the-art recommendation models. \endabstract

Artificial Intelligence of Things (AIoT) is an emerging paradigm that integrates artificial intelligence (AI) and Internet of Things (IoT) technologies to provide intelligent IoT solutions. The AIoT system acquires data in real time through IoT sensors, performs intelligent data analysis tasks anywhere in the terminal–edge–cloud continuum, and provides accurate decision-making services based on data predictions. Cloud–edge collaboration can reduce security risks for AIoT data prediction by sharing data features instead of raw data. However, sensitive user data may still be inferred by attackers through model parameter analysis, causing irreparable harm and serious consequences. Therefore, data prediction based on cloud–edge collaboration while maintaining privacy constraints remains a significant challenge. In this article, a differentially private federated tensor completion method is proposed for cloud–edge collaborative AIoT data prediction. This method embeds differential privacy (DP) mechanisms with cloud–edge collaboration. Each edge is capable of processing and analyzing data, and collaborative learning with other edges by sharing privacy-preserving model parameters. For model security, objective perturbation is applied to ensure that the tensor completion method satisfies DP. To achieve higher accuracy, parallel tensor decomposition is introduced to avoid the update conflicts problem of federated tensor completion. Through theoretical analysis, our method can provide data protection for tensor completion with high-security promise. The experiments are performed on both synthetic and real-world data sets to demonstrate the superior performance of our method in preserving data privacy.

Recurrent neural networks (RNNs) and their variants can efficiently capture the features of time-series characteristic data and are widely used for intelligent transportation tasks. Internet of Vehicles (IoV) edge devices deploying RNN models are an important impetus for the development of intelligent transportation system (ITS) and provide convenient services for users and managers. However, the input data of some transportation tasks have high dimensional characteristics, resulting in the number of training parameters and computational complexity of RNN models being too large, making it difficult to deploy high-performance RNN models on resource-constrained IoV edge devices. To overcome this problem, we compress the training parameters of the RNN model using the proposed multi-tree compact hierarchical tensor representation-Dtensor Block Decomposition (DBD), which reduces the computational complexity of the model and speeds up the training process of the model, thus making the network model lightweight. We evaluate the performance of Dtensor Block-Long Short-Term Memory (DB-LSTM) and Improved Dtensor Block-LSTM (IDB-LSTM) models on multiple real datasets and compare them with the current state-of-the-art LSTM compression models. Experimental results demonstrate that our proposed method can massively compress the number of training parameters of the models on different datasets and shorten the training time of the models without degrading the testing accuracy of the models. In addition, our proposed DB-LSTM and IDB-LSTM models have better comprehensive performance compared with other models and are more suitable for deployment on resource-constrained IoV edge devices.

The widespread applications of the Hybrid Internet of Things (HIoT) have put forward higher requirements for network reliability. Coverage reliability is one of the important metrics of reliability, and reliable coverage ensures network data perception and transmission to improve the Quality of Service (QoS). In this article, we define Confident Information Coverage Reliability (CICR) based on the Confident Information Coverage Model (CIC), which comprehensively considers sensor multistate, sensor energy, coverage rate, and connectivity robustness to evaluate coverage reliability. Furthermore, a Tensor-based Confident Information Coverage Reliability Algorithm (T-CICR) is proposed based on tensor modeling to evaluateCICR. The algorithm uses a tensor-based Markov model to predict sensor multistate. Three tensors of coverage rate, sensor multistate, and sensor energy are constructed to provide unified representations. Simulation results show that our proposed algorithm can significantly improve coverage reliability in terms of duty cycle, coverage rate requirement, sensing range, Root Mean Square Error (RMSE) threshold, connectivity robustness requirement, and link reliability.

Multimedia cognitive computing as a revolutionary emerging concept of artificial intelligence emulating the reasoning process like human brains can facilitate the evolution of intelligent transportation systems (ITS) to be smarter, safer, and more efficient. Massive multimedia traffic big data is an important prerequisite for the success of cognitive computing in ITS. However, traditional data-centralized artificial intelligence approaches often face the problems of data islands and data famine due to concerns about data privacy and security. To this end, we propose the concept of cognitive federated learning leveraging federated learning as the learning paradigm for cognitive computing, which solves the preceding concerns by sharing updated models rather than raw data. Nevertheless, the exchange of numerous model parameters not only generates significant communication overhead but also suffers from the risk of privacy leakage due to inference attacks. This article aims to design a novel lightweight and privacy-enhanced cognitive federated learning architecture to facilitate the development of ITS. First, a privacy-enhanced model protection scheme with homomorphic encryption as the underlying technology is proposed to simultaneously defend against the inference attacks launched by external malicious attackers, honest-but-curious cognitive platforms, and internal participants. Furthermore, a novel tensor ring-block decomposition and its corresponding deep computation model converting the weight tensor into a set of matrices and third-order core tensors are proposed, which could reduce the communication overhead and storage requirements without compromising model performance. Experimental results on real-world datasets show that the proposed approach performs well.

Recent research about camouflaged object detection (COD) aims to segment highly concealed objects hidden in complex surroundings. The tiny, fuzzy camouflaged objects result in visually indistinguishable properties. However, current single-view COD detectors are sensitive to background distractors. Therefore, blurred boundaries and variable shapes of the camouflaged objects are challenging to be fully captured with a singleview detector. To overcome these obstacles, we propose a behavior-inspired framework, called Multi-view Feature Fusion Network (MFFN), which mimics the human behaviors of finding indistinct objects in images, i.e., observing from multiple angles, distances, perspectives. Specifically, the key idea behind it is to generate multiple ways of observation (multi-view) by data augmentation and apply them as inputs. MFFN captures critical boundary and semantic information by comparing and fusing extracted multi-view features. In addition, our MFFN exploits the dependence and interaction between views and channels. Specifically, our methods leverage the complementary information between different views through a two-stage attention module called Co-attention of Multi-view (CAMV). And we design a local-overall module called Channel Fusion Unit (CFU) to explore the channel-wise contextual clues of diverse feature maps in an iterative manner. The experiment results show that our method performs favorably against existing state-of-the-art methods via training with the same data. The code will be available at https://github.com/dwardzheng/MFFN_COD.

Writer retrieval is crucial in document forensics and historical document analysis. However, due to the difference in syntactic structure between Chinese and other languages, the existing methods may not be directly applied to Chinese writer retrieval. Previous work on Chinese writer retrieval does not overcome the performance degradation problem when the number of samples grows. In this paper, we propose a novel compound uniform encoding algorithm (CUE) for Chinese writer retrieval, which mainly consists of a combined feature extraction module (CFE) and a prototype substitution module (PS). The CFE module combines two complementary features from image filter response and character contour. It counts local symmetries and edge co-occurrence pairs. PS module substitutes the outliers with the class prototypes to alleviate the influence of the outliers. Finally, the weighted Chi-square distance is applied to measure the similarity between writer and text. To verify the superiority of our proposed method, experiments are conducted on four public datasets and our built dataset. The results validate that CUE outperforms the state-of-the-art algorithms on mAP metric.

The emergence of new applications of Internet of Things (IoT) makes its security and reliability become one of the most concerning issues and requires more breakthroughs. To ensure reliable operation of IoT, network reliability measures are essential for quantifying the performance of such networks. In this paper, we focus on the problem of coverage reliability of IoT intrusion detection systems based on Attack-Defense Game Design. A comprehensive coverage reliability algorithm is proposed based on Monte Carlo simulations. The algorithm employs Byzantine attack and defense ideas to determine network node attributes and uses confident information model to calculate the network coverage area. Furthermore, we propose a system reliability metric based on the analytic hierarchy process method, which takes advantage of node attributes, network coverage and connectivity. The metric is used to compare algorithms in simulated experiments, and a series of simulation comparisons illustrate the superiority and usability of the proposed approach.

The development of 5G has brought new opportunities for the application of Internet of Things (IoT). The integration of 5G and IoT technologies promote high availability, resilience, and reliability of the network infrastructures. IoT deployment optimization is the core issue of 5G IoT. Traditionally, IoT node deployment methods mostly used disk coverage model or probabilistic detection coverage model, which only utilizes the sensing capability of a single IoT node, which results in higher deployment costs. In this article, we study the network resilience of coverage estimation error and solve the coverage problem of resilient deployment of smart nodes in 5G IoT. The coverage formulation in the deployment optimization method is defined based on the confident information coverage (CIC). In order to obtain the optimal deployment with a given coverage quality and with a given budget, the mixed-integer linear programming models (CICILP-COST) and (CICILP-ERROR) are proposed based on the CIC model. After analyzing the model complexity, the proposed models are solved by the variable relaxation algorithm (CICVR-COST) and dichotomous search algorithm (CICDS-ERROR), respectively. Simulations on air pollution datasets in Lyon, France, show that the proposed model yields a lower cost optimal deployment than existing peer schemes.