Temporal knowledge graph reasoning (TKGR) excels at inferring missing event‐centric facts within a timeline, thereby mitigating the inherent incompleteness of real‐world data. Existing TKGR methods predominantly exploit intrasnapshot structural patterns and intersnapshot temporal dependencies. However, they often fail to incorporate valuable time‐invariant factual knowledge about events and struggle to jointly model entity–relation evolution dynamics. To address these challenges, we propose CKE 3 , a novel contrastive knowledge‐enhanced event evolution model that integrates time‐invariant entity background knowledge with time‐variant structural information for enhanced reasoning. Specifically, CKE 3 improves the learning of expressive entity and relation representations through an adaptive knowledge retrieval strategy, which constructs descriptive common sense knowledge associated with each entity to provide richer contextual information. Moreover, we introduce a knowledge‐aware hierarchical structural–temporal modeling module that jointly captures entity–relation structural interactions as well as sequential and periodic temporal patterns across knowledge graph snapshots. To further mitigate the impact of knowledge noise, we design a self‐supervised augmentation task that promotes the fusion of relevant information while alleviating noise. Extensive experiments on four public TKGR datasets demonstrate that CKE 3 consistently surpasses state‐of‐the‐art baselines, highlighting its effectiveness in accurate entity forecasting for the TKGR task.

The runs test, as one of the basic binary sequence randomness analysis methods, has been widely adopted in various international standards and industry. However, existing runs test schemes suffer from critical performance limitations, such as large lookup tables to calculate Hamming weight, causing cache inefficiency; data loading redundancy in computing Hamming weight. We propose a SIMD-accelerated optimization scheme to enhance the runs test performance. This scheme includes: (1) replacing software lookup tables with SIMD bit-counting instructions to optimize cache efficiency; (2) designing a parallel computing algorithm that consolidates the dual traversals for computing Hamming weight into a single traversal, to eliminate redundant data loading. The proposed scheme exhibits architectural independence, as it fully achieves performance improvements on x86 and ARM architectures. Notably, the proposed scheme achieves 161,134.39[Formula: see text]Mbps detection speed, that is 16.98 times compared to the current Fast NIST STS implementation.

The widespread integration of Artificial Intelligence (AI) in sixth-generation Internet of Things (6G-IoT) applications, introduces significant challenges for ensuring the trustworthy and dependability of AI models. The "black-box" characteristic of numerous Deep Neural Networks (DNNs) creates a notable obstacle for confirming their safety in intricate, ever-changing environments. Consequently, there is a need for extensive testing, requiring the gathering and labeling of a large number of test cases, a process that is both time-intensive and resource-consuming. While previous studies have adapted neuron coverage criteria for steering test case generation in DNNs. Yet, these criteria are white-box measures requiring access to model states and presenting their practical limitations. Conversely, black-box metrics, which focus on outputs, present a more feasible approach. Among these, black-box diversity metrics evaluate model robustness by generating diverse test cases, eliminating the need for internal model details. This paper presents a test case generation framework centered on diversity, known as BDTest. BDTest enhances test adequacy through five stages: (1) Mapping feature vectors extracted from an initial set of seed images onto a low-dimensional manifold utilizing UMAP; (2) Detecting sparse regions using DBSCAN; (3) Sampling key points from these regions via Latin Hypercube Sampling; (4) Reconstructing latent features and generating new images through ICA and GAN inversion; and (5) Measuring the diversity of the generated set using metrics such as the Log-Determinant. Experiments demonstrate that BDTest significantly improves test set diversity and error detection performance, achieving error rates of 59.36%, 59.76%, and 67.03% on VGG19, DenseNet121, and MobileNetV2, respectively, outperforming DeepXplore by an average of 12.43% and DLFuzz by 9.95% across all tested models. When retrained with the generated test cases, the model demonstrated improved accuracy on the original test set, alongside a significant enhancement in accuracy on the natural adversarial test set.

Traffic flow prediction plays a crucial role in the construction of smart cities. Although numerous models already exist for traffic flow prediction, they neither extracted the spatio-temporal features at different scales nor precisely considered the deviation between the traffic signals collected by the sensors and the trend signals at different scales. This leads to their inaccurate prediction results. To address the aforementioned issues, this paper proposes a multi-layer structure to extract spatio-temporal features at different scales and designs an information decomposition module to separate abnormal signals in traffic data. Furthermore, based on the above structure and modules, this paper constructs a new traffic flow prediction modelMulti-scaleAnomalyDecompositionGraphNeuralNetwork (MADGNN) for feature extraction and information decomposition at different scales. Firstly, the model encodes the input data to fully capture spatio-temporal dependencies. Then, this paper extracts spatio-temporal features at multiple scales based on a multi-layer structure containing multiple GRUs and subtracts the learned abnormal information from the input signal to achieve abnormal signal decomposition. Finally, we use multiple spatio-temporal hidden states for further information extraction and traffic prediction. The final prediction result of the model is obtained by adding up the prediction outputs of each layer. The experimental results show that, compared with DDGCRN, on the Railway datasets, the MAE and RMSE metrics are improved by an average of 3.92% and 2.30% respectively, and on the public dataset PEMSD8, the MAE and MAPE metrics are improved by 2.92% and 2.23% respectively.

Blockchain has been increasingly adopted in the Industrial Internet of Things (IIoT) for cross domain authentication, enabling trusted collaboration among multiple management domains and supporting decentralized trust management. However, existing authentication schemes, particularly those based on classical public-key cryptography such as RSA and ECC, are vulnerable to quantum attacks and still face significant challenges in computation overhead. These limitations make them insufficient for the practical demands of complex, multi-domain environments, especially in the context of future quantum threats. To address these challenges, this article proposes PQEdgeAuth, an edge-assisted authentication scheme for IIoT that integrates post-quantum secure authentication with an optimized consensus protocol. The scheme incorporates post-quantum cryptographic techniques to ensure long-term security against quantum adversaries, while employing location-based grouping and aggregation signatures to reduce consensus communication overhead and enhance scalability. Experimental results show that PQEdgeAuth reduces computation time by up to 35.23% and improves throughput by up to 54.38% compared to existing schemes, demonstrating its potential to offer a secure, scalable, and efficient solution for cross domain authentication in IIoT environments.

Low rank tensor ring based data recovery algorithms have been widely used in data-driven consumer electronics to recover missing data entries in the collecting data pre-processing stage for providing stable and reliable service. However, traditional recovery methods often fail to utilize the abundant prior knowledge of data and the non-local self-similarity of the data, thus leading to the failure to effectively capture the spatial relationships within high-dimensional data to recover them accurately. To address these problems, we present a novel Non-local Self-similarity and Low-rank Prior Knowledge based tensor ring completion method. Firstly, we incorporate the BM3D denoising operator within a Plug-and-Play framework to exploit the self-similarity in the data. Then a logarithmic determinant function is integrated to distinguish singular values in the cyclic unfolding matrix of the tensor and adopts a tensor ring completion approach based on weighted nuclear norms. Finally, in order to evaluate the effectiveness of our proposed method, we conducted a series of experiments by using the missing image dataset and the missing traffic data dataset respectively, and the experimental results show that our method achieves the highest level in terms of data recovery accuracy.

A typical supply chain consists of suppliers, manufacturers, distributors, and customers, with supply, production, and distribution being the key links. Coordinating and optimizing these stages to reduce waste and shorten delivery times poses a significant challenge. However, most existing research relies on heuristic algorithms and focuses primarily on the production and distribution stages. In complex large-scale scenarios, the computational efficiency and adaptability of heuristic algorithms often fall short. This paper investigates a combined scheduling problem and a heterogeneous vehicle routing problem. Unlike traditional heuristic approaches, we propose a novel deep-reinforcement learning model. Finally, a Multi-Rollout algorithm is employed for collaborative training. Experimental results demonstrate that the proposed algorithm delivers competitive performance compared to heuristic methods while significantly outperforming them in computation time for individual instances.

Deep reinforcement learning (DRL) has been widely applied to address inventory management problems. To tackle the challenges posed by factors such as backlog, multisource replenishment, and demand priority in multi-echelon inventory systems, this paper proposes a value decomposition-based multi-agent reinforcement learning (MARL) framework. The framework utilizes independent DQN networks, embedded with self-attention and GRU modules, to facilitate distributed learning of local action value functions, thus simplifying the complexity of the action space. Additionally, a hybrid network based on the multi-head attention mechanism is constructed to approximate the joint action value function, aiming to optimize the overall system cost. Experiments have been conducted on various types of supply chain networks, and the results demonstrate the effectiveness and scalability of the proposed framework.

Spatiotemporal signal forecasting is vital for promoting intelligence management in Internet of Everything applications. Benefiting from the powerful representation ability of deep learning, DNN based methods have shown promising performance in spatiotemporal forecasting. However existing methods perform suboptimally in few-shot distribution shift scenarios. As the representation ability results in stable fitting, the model struggles to adapt to discrepancy in the data domain. And it is challenging to modify the mapping relationship between the data and the latent space of representation with few-shot distributionally shifted target data. In this case, we propose a distribution centric prompt based transfer learning framework, which transforms the data distribution information into model-interpretable prompt embeddings confused with spatiotemporal sematic information in Intermediary Bridging Space which serves as a mediator between the data domain and the latent space. Thus the downstream model can learn a target distribution aligned representation regulated by the mediator. Though experiments on real-world datasets, we verify the effectiveness and extensibility of the proposed method.

Federated learning (FL) offers a promising distributed paradigm for managing massive data from Industry 5.0 devices while preserving privacy. However, significant challenges arise from inherent system heterogeneity and data heterogeneity across devices. These factors severely impede FL performance, leading to slow convergence and potential degradation of the global model’s accuracy. Random client selection strategies are often insufficient to mitigate these issues effectively. To address these limitations, we propose FedHRL: a heuristic-guided pointer network reinforcement learning framework for joint client selection and bandwidth allocation in FL. Specifically, our heuristic-guided soft actor–critic algorithm employs a transformer-based pointer network within its action network to tackle the combinatorial optimization problem of sequentially selecting clients and allocating bandwidth. This network identifies the optimal next client based on prior selections and available bandwidth constraints. Furthermore, to accelerate RL convergence and enhance policy effectiveness, we integrate a particle swarm optimization-based bandwidth reallocation strategy, which refines the RL agent’s bandwidth allocation decisions, feeding the optimization results back as an enhanced reward signal to expedite learning and improve overall performance. Experiments demonstrate that FedHRL accelerates FL training convergence while maintaining high model accuracy in heterogeneous environments.

The upcoming 6G technology, with its high speed and low latency, is poised to become a foundational technology for intelligent transportation systems. To handle the massive data generated by connected vehicles in 6G environments, federated learning methods are essential. However, traditional centralized federated learning approaches still face challenges related to data and device heterogeneity, which significantly affects training efficiency. To address these challenges, we propose FedCPC, a context-based adaptive pruning clustered federated learning method. Based on the positive correlation between similar data distributions and model representations, we use centralized kernel alignment (CKA) to group clients with similar data distributions, thus reducing the impact of data heterogeneity. Furthermore, we introduce a context-aware random forest multi-armed bandit method to determine appropriate pruning rates based on device capabilities and historical performance which addresses device heterogeneity concerns. Experimental results on open-source datasets demonstrate that FedCPC outperforms traditional FL methods in both learning efficiency and communication effectiveness.

Semantic segmentation constitutes an essential component of various visual tasks and is widely utilized in numerous applications. However, its implementation in real-world settings is frequently challenged by limitations pertaining to resources and latency. The early exit mechanism addresses these challenges by attaching Internal Segmentation Heads (ISHs) to intermediate layers of the network, facilitating a gradual segmentation of the entire image from less complex to more intricate regions. This methodology mitigates the aggregate computational burden by obviating the necessity to execute the entire network across all portions of the image. However, in antecedent methodologies, ISHs function in isolation from each other. This segregation engenders complications in which the latter ISHs, designated to manage more intricate and detailed regions, are deprived of access to high-resolution information. Furthermore, the subsequent ISHs are unable to fully capitalize on the computations executed by preceding ISHs, thereby resulting in inefficient utilization of computational resources. To ameliorate this challenge, we introduce the Cascade-Exit Semantic Segmentation Network (CESS). This innovative architecture propagates high-resolution features into the decoders of subsequent ISHs and establishes connections between the predictors of antecedent ISHs and those of the subsequent ones. Comprehensive empirical evaluations performed on the Cityscapes and CamVid datasets substantiate that our proposed methodology exhibits a superior balance between accuracy and inference time when juxtaposed with recently early exit techniques.

Deep neural networks (DNN) have extensive applications in image processing, medical diagnosis, autonomous driving, and various other domains. Previous research has found limitations in the error-awareness capacities of deep neural networks, especially when deployed in safety-critical systems. Several neuron coverage criteria have been suggested to direct the adequate testing and quality assessment of deep neural networks, drawing inspiration from conventional software quality assessment methodologies. Nevertheless, recent research has raised doubts about the validity of these criteria in guiding quality assessment of deep neural networks and measuring test adequacy. In this paper, we use ImageNet-A as an additional test dataset and employ three diversity metrics to assess the diversity of the hybrid datasets comprising both ImageNet and ImageNet-A. Subsequently, we employ the hybrid dataset in conjunction with eight DNNs, to investigate the statistical correlation between the three diversity metrics and the DNNs’ accuracy in predicting correct outcomes. Furthermore, we conduct a comparative analysis between the diversity metrics and DNN accuracy, as well as between coverage criteria and DNN accuracy. The classification task used in the experiment is also widely used in various sensors for ubiquitous intelligence. The results of our experiment indicate that test set diversity metrics serve as superior indicators of test adequacy compared to coverage criteria, thereby enabling more effective guidance for testing DNNs.

Industrial control systems rely on wireless devices and sensors, necessitating critical security. Physical layer authentication (PLA) is a promising mechanism for device authentication, utilizing its unique spatiotemporal characteristics and channel state randomness, which offers unforgeability and high informatics security with low computational overhead and efficiency in resource-constrained scenarios. However, existing PLA mechanisms face challenges in complex industrial wireless environments, including insufficient accuracy, computational complexity, inadequate noise consideration, and poor performance. To address these challenges, we propose a convolutional denoising autoencoder (CDAE) model that reduces feature dimensions, eliminates noise, and extracts key vectors. The weighted$k$-nearest neighbor algorithm classifies the extracted vectors for comprehensive authentication in control system networks. Accurate authentication enables efficient detection of malicious attacks. Simulation experiments show that using CDAE-extracted feature vectors achieves over 95% accuracy with only 1% training samples, surpassing channel state information-based authentication by 46.15%, validating the proposed mechanism’s effectiveness.

Unlike conventional IT systems, industrial control systems (ICSs) requires tailored attack detection methods due to its unique communication protocols. Existing attack detection methods lack the ability to consider both detection accuracy and time performance, particularly for highly stealthy fake data injection attacks (FDIAs). To address these challenges, this work proposes an online parallel attack detection method for ICS based on multibandpass filter. By building multiple adaptive filters based on energy equilibrium and time–frequency domain data transformation, we implement multifrequency band data segmentation. Hierarchical temporal memory (HTM) models are employed to parallelly fit the segmented data and detect anomalies. Simulation experiments demonstrate that our method outperforms the state-of-the-art Numenta method, achieving a 9% higher detection accuracy while reducing detection time to just 1/14 of Numenta’s. These results highlight the significant advantages of our method in striking a balance between detection accuracy and time performance. Our proposed method fills the gap in ICS attack detection and offers substantial improvements over existing techniques.

The emerging field of Artificial Intelligence of Things (AIoT) is propelled by the remarkable success of deep learning and hardware evolution, which has a significant impact on our daily lives. However, because of their notorious computing resource intensity, the widespread deployment of AIoT devices requires substantial electricity consumption as support, inevitably escalating energy consumption, and ultimately leads to a significant carbon emissions. Existing research on neural network compression and acceleration struggles to achieve energy-efficient inference on resource-constrained AIoT devices. To address this issue, we propose a software-hardware co-design approach that integrates advanced neural network optimization techniques with hardware power management capabilities to enable energy-efficient inference and ultimately achieve sustainable AIoT. We introduce a lightweight split and refinement block that adaptively reduces redundant computation in both channel and spatial dimensions. Several early exit (EE) branches are added to the backbone, which are controlled by a policy-based EE predictor. With the predicted EE index, a curve-fitting-based frequency scaling algorithm is presented to calculate the optimal frequency that minimizes energy overhead while maintaining latency constraints. Extensive experiments on CIFAR and CINIC classification tasks validate that our proposed method consistently reduces energy consumption for neural network inference while outperforming other competitive methods.

In the rapidly evolving landscape of the sixth generation (6G) network, smart contracts emerge as a pivotal technology for enforcing trustful rules. However, the conventional lifecycle model of smart contracts—encompassing stages from initiation to the termination of a contract instance—suffers from rigidity and lack of customization, leading to notable operational challenges. These challenges primarily manifest as heightened resource demands, including longer waiting periods and escalated transaction costs, which hinder the adaptability of smart contracts in the varied and dynamic contexts of 6G-connected environments. Driven by these issues, this article conducts a comprehensive analysis of the smart contract lifecycle. We introduce an innovative lifecycle model that offers customizable flexibility, allowing for the merging or separation of different stages in the smart contract process. Meanwhile, we propose a unique transaction data structure designed to integrate parameters of combined stages, each marked with distinct identifiers for differentiation. Further, we introduce an innovative address scheme for smart contract instances, which provides an identifier to simplify instance access while also maintaining a mechanism for traditional access methods. The verification results show that the model can save 52.72% of processing fee and 68.09% of completion time compared with the conventional method.

Quantum circuit mapping (QCM) is a crucial preprocessing step for executing a logical circuit (LC) on noisy intermediate-scale quantum (NISQ) devices. Balancing the introduction of extra gates and the efficiency of preprocessing poses a significant challenge for the mapping process. To address this challenge, we propose the mutual-influence-aware (MIA) heuristic method by integrating an initial mapping search framework, an initial mapping generator, and a heuristic circuit mapper. Initially, the framework utilizes the generator to obtain a favorable starting point for the initial mapping search. With this starting point, the search process can efficiently discover a promising initial mapping within a few bidirectional iterations. The circuit mapper considers mutual influences of SWAP gates and is invoked once per iteration. Ultimately, the best result from all iterations is considered the QCM outcome. The experimental results on extensive benchmark circuits demonstrate that, compared to the iterated local search (ILS) method, which represents the current state-of-the-art, our MIA method introduces a similar number of extra gates while achieving nearly 95 times faster execution.

Industrial control technology faces serious communication security threats. Authenticated key agreement(AKA) protocols are essential to secure communication between industrial control nodes, but they must be efficient and robust due to resource constraints. Existing AKA protocols based on encryption algorithms have limitations such as vulnerability to clone attacks. Also, there is a lack of protocol’s research that fully integrate advantages of physical and upper layer. We propose a novel cross-layer AKA protocol that leverages channel state information(CSI), which has uniqueness and real randomness, and is unforgeable, to enhance security and reduce computational overhead. Our protocol only requires simple operations such as algebraic, Hash and XOR. We introduce a new security verification model, superCK, which extends the well-known eCK and relaxes assumptions on attackers. Our protocol achieves 12 fully provable key security capabilities, and has 75.91% less computational overhead and 6.99% less communication overhead than the best ones, achieving an optimal trade-off between security and efficiency.

In this paper, we study the deployment of$K$heterogeneous UAVs to monitor Points of Interest (PoIs) in a disaster zone, where a PoI may represent a school building or an office building, in which people are trapped. A UAV can take images/videos of PoIs and send its collected information back to a nearby rescue station for decision-making. Unlike most existing studies that focused on only homogeneous UAVs, we here study the scheduling of$K$heterogeneous UAVs, where different UAVs have different energy capacities and functionalities that lead to different monitoring qualities (monitoring rewards) of each PoI. For example, one type of UAVs can take only visual images while the other type of UAVs can take both visual and thermal infrared images. In this paper, we investigate a problem of scheduling$K$heterogeneous UAVs to monitor PoIs so that the sum of monitoring rewards received by all UAVs is maximized, subject to energy capacity on each UAV. We propose the very first$\frac {1}{3}$-approximation algorithm for this scheduling problem. We also evaluate the performance of the proposed algorithm, using real parameters of commercial UAVs. Experimental results show that the performance of the proposed algorithm is promising, which is improved by 25%, compared with existing algorithms.

Recently, the use of wireless channel state information (CSI) to generate encryption keys in the physical layer has gained significant attention from researchers. Unlike classical cryptography, this approach relies on the variability of the wireless channel, channel reciprocity, and spatial decorrelation to ensure security, making it more lightweight and providing strong randomness. This article proposes a physical layer key generation scheme for wireless LAN MIMO systems based on feature fusion autoencoder (FFAEncoder) to address the issue of a high key disagreement rate (KDR). Our approach involves extracting amplitude and phase features separately, fusing them through multiplication operator in a neural network, and using an autoencoder to extract common features. The proposed scheme was evaluated on multiple data sets in different real-world scenarios, and it was found that the transmitter and receiver codeword’s mean squared error (MSE) and mean absolute error (MAE) were smaller than those of the current models, indicating better key generation performance. Additionally, the proposed scheme’s KDR was smaller, with a decay rate faster than that of the other two models in the same environment, and the primary key bits were 1/2 and 1/3 that of the other models, respectively, as the signal-to-noise ratio (SNR) increased.

Physical-layer secret key generation (PLKG) is a new research area that has emerged in recent years. It is aimed at scenarios where legitimate IoT devices communicate directly, interacting with confidential information for lower overhead and higher security by using wireless channel. When applying it to wireless feature extraction, noise removal is not taken into account in current deep learning networks. To address these problems, the PLKG scheme based on bidirectional convergence feature learning convolutional network (BCFL-based scheme) is proposed, which consists of neural network called BCFL and a new quantization method to achieve better secret key generation. Unlike existing PLKG schemes that enabling both parties to communicate for obtaining higher channel feature similarities, when training it, channel state information (CSI) obtained by channel estimation for two legitimate devices during coherent time is used as inputs; and mean square error (MSE) between two outputs is used as a result of loss function for iterative training. Thus, it can obtain better denoising ability with guaranteed low computational resource consumption, and two legitimate devices can obtain highly correlated channel features. Multiple quantization method is also proposed to address low secret key generation rate (KGR) and low-secret key randomness (KR). The results show that the proposed BCFL-based scheme has a lower MSE than other schemes in different scenarios, indicating that it has better capability to learn channel reciprocity; and secret key error rate (KER) and time consumption are only about 50% of other schemes, which is a significant performance improvement.

Nowadays, Industrial Internet of Things (IIoT) technology has made a great progress and the industrial control systems (ICSs) have been used extensively, which has brought more and more serious information security threats to the ICS at the same time. The authenticated key agreement (AKA) protocol is a common method to ensure the communication security. This work proposes a lightweight AKA protocol based on the elliptic curve cryptography (ECC) algorithm to adapt to the resource-constrained environment. We only employ hash operation, XOR operation, and ECC algorithm to encrypt the data in the authentication and key agreement phase, and avoid involving the register center while proceeding the key agreement, to give consideration to both performance and security. The security analyses indicate that our protocol can meet nine critical security requirements, more than all of the existing protocols, and the performance analysis carried out indicates that our protocol has less computational and communication overheads in contrast to other corelative protocols.

Internet of Things (IoT) devices and services have become increasingly ubiquitous in recent years as they greatly facilitate our daily life. To promote IoT data and semantic interoperability, we propose an edge–cloud framework. The edge end is responsible for handling customized data processing tasks and transferring the processed data, while the cloud end deals with semantic information processing. Additionally, we present an entity tree embedding algorithm at the cloud end to convert IoT entities and attributes into embedding vectors in a tree-structured way. Consequently, entity embeddings could reflect the semantic information at both Class and Property levels, which ameliorates our previous entity embedding method, leading to better embedding results and clustering effects. Finally, the entity tree embedding algorithm and the corresponding compression algorithm are evaluated. Results indicate that the Whitening algorithm is the best method to compress embedding vectors. More importantly, the entity tree embedding algorithm captures both the semantic and structural information of entities and attributes. Additionally, the clustering experiments show that the proposed embedding algorithm achieves better clustering results compared with the original entity embeddings and the uncompressed averaging method.

Blockchain exchanges have been widely used for IoT-based resource sharing. An exchange consists of several related transactions where participants exchange their assets. However, the exchange can take a long time to complete due to different reasons (such as IoT device failure). Meanwhile, exchange fees are an important aspect, because an exchange usually contains multiple transactions, and the exchange fee is the sum of the fees for each transaction. In this article, we explore ways to optimize the exchange process concerning the above issues. The method is based on the graph of an exchange, which is strongly connected. To save exchange time, we propose a way to split the exchange into smaller exchanges, since smaller exchanges have fewer transactions. The method first finds strongly connected subgraphs, separates them from the original graph, and adjusts the edges and their weights. To save transaction fees, we propose a method to reduce the number of transactions. Transactions that meet certain requirements are combined into one transaction, and corresponding weights are adjusted. The verification results show that the proposed method can optimize the exchange process, which can be used to save the exchange time and also can be used to save transaction fees.

With the development of the modern smart city, sharing-bikes require behaviors prediction for grid-level areas which is essential for intelligent transportation systems. A model which can predict bike sharing demand behaviours accurately can allocate sharing-bikes in advance to satisfy travel demands alongside saving energy, reducing traffic, cutting down waste for those sharing-bikes companies putting excessive sharing-bikes in unsaturated demand areas. In this paper, we abandon the traditional time series prediction method and use a more efficient deep learning method to solve the traffic forecasting problem. Moreover, instead of considering spatial relation and temporal relation relatively, we produced a deep multi-view spatial-temporal network to combine them into one prediction model framework. In the experimental section, we investigate in the experiment on enormous amount of real sharing-bikes application use data in the core region of Beijing to test the performance of the model framework with a 1 km$\times $1 km grid-level scale and compare it with other existing machine learning approaches and prediction models. And the 4G/5G/6G communication technology facilitate the real-time control of the space-time locations of sharing bikes dynamically. Thus, it provides the basis for high-frequency analysis of space-time patterns, especially supported by the 6G large-scale application in the future.

With the development of big data, data sharing has become a hot topic. According to the previous research on data sharing, there is a problem with regard to how to design an effective incentive mechanism to make users willing to share data. First, we integrate the incentives based on reputation and payment and introduce “credibility coins” as a cryptocurrency for data-sharing transactions, to encourage users to participate honestly in the data-sharing process based on federated learning. Second, we propose a dynamic incentive model based on the evolutionary game theory to model the game process of users in data sharing and analyze the stability of their strategies. Finally, based on the results of this analysis, we use the blockchain-based smart contract technology to dynamically adjust the participation benefits of users under different conditions in order to promote users to join consortium blockchains more often and steadily to participate in model training for federated learning and obtain better model accuracy. Our work is the first to apply the evolutionary game theory to the study of incentives in federated learning, and plays a leading role in the study of incentives in federated learning. Experimental simulation validation shows that our DIM-DS model can adequately motivate users to participate in the collaborative task of data sharing and maintain stability. The model can maximize the effectiveness of the federated learning model.

In the past few decades, the prevalence of Internet of Things (IoT) applications has brought both opportunities and challenges of different categories. One of the main concerns nowadays is semantic interoperability. Although plenty of augmented ontologies and resource description frameworks have been designed to achieve semantic interoperation, there is still no sustainable solution to interconnect the increasing number of heterogeneous devices and corresponding data. Aiming at this problem, this article presents a dataspaces model utilizing distributed approaches to represent semantic information as a sustainable solution for IoT semantic interoperability. In this model, an attention-based entity embedding approach is designed to convert IoT entities into low-dimensional dense vectors, then calculations, including entities, relations, etc., can be further conducted. Consequently, the entity embeddings could reflect the semantic information of Class equivalences among entities. To recognize entity relations, the generated entity vectors and the arithmetic results of two entities are combined as input features. By feeding the input features to a well-trained Tensor-train modified DNN, the relation between two entities could be recognized. Finally, experiments are conducted on two data sets to evaluate the proposed approaches. Results indicate that the generated entity vectors could effectively reflect the semantic similarity between entities. More importantly, while achieving parameter compression and better generalizing ability, the relation recognition approach improves the relation recognition accuracy on new entities compared with the state-of-art models.

In recent years, the blockchain technology has been widely used in the Internet of Things (IoT). One of the major concerns is how to adopt a smart contract to process data from IoT devices flexibly. While plenty of smart contract-based methods can be used, smart contracts are required to be deployed previously. This requires an additional step (to deploy a smart contract) and makes a smart contract separate from its data (transactions to trigger its interface), which generates limitations in IoT scenarios. In this article, we developed an approach to embed the smart contract and its data into the same transaction, eliminating the need for a predeployment step. Data is employed as parameters to invoke the interface of a smart contract, and the smart contract is used to process the data inside the transaction. With this method, a smart contract does not need other transactions from the user. Results indicate that the proposed method can eliminate the requirement of a separately deployed smart contract, saving costs, and waiting time for the predeployment.

The demand for real-time data processing has become increasingly attractive in Cyber-Physical Systems(CPSs), especially for data-intensive embedded real-time applications. In order to timely perceive and respond to environmental changes, the basic design requirement in such systems is to provide data service with high freshness. As modern CPSs become more complex, there are a broad set of system mode switch behaviors, some unforeseen, in a dynamic computational environment. However, conventional control algorithms can hardly handle such new scenarios, since most of them assume that the operational behavior is fixed. In this paper, we study the problem of how to maximize the freshness of data in multi-modal systems. We first use a recently proposed new conception, namely Age of Information (AoI) to quantify the freshness of data by combining the AoI metric with real-time constraints. Then, we propose, to our knowledge, the first freshness-aware scheduling solution to settle the problem via deep reinforcement learning(RL). To be specific, we develop an RL framework that can continuously update its scheduling strategies and maximize the freshness of data in the long term. Extensive simulation experiments are conducted and the results demonstrate that the proposed FAS-DQN outperforms other traditional state-of-the-art methods in terms of data freshness.

Computation offloading has become popular in recent years as it is an effective way to reduce the energy consumption and enhance the performance of smartphones. To deal with the heterogeneous architectures between the smartphone and the server, and to simplify deployment of the server, we propose and implement a lightweight offloading framework which supports offloading of compute-intensive tasks and deploying the server efficiently. Based on this framework, generic and developer-customized offloading services could be provided for different third-party applications. Furthermore, we design a multi-task offloading tactic for the framework to deal with intensive offloading requests from various mobile devices. When receiving an offloading request, the master node in server-side determines whether this task should be offloaded or not and which VM should handle this task, so that the overall execution time and energy consumption are optimized. We implement this framework and evaluate it by comparing the execution time, energy consumption and CPU utilization rate among three execution modes with three applications. We also conduct experiments of the multi-task offloading tactic in simulation environment. Experimental results indicate that this framework effectively reduces energy consumption and boosts performance for compute-intensive tasks, and the multi-task offloading tactic is valid for intensive offloading requests.

Temporal action detection is a challenging task in video understanding, due to the complexity of the background and rich action content impacting high-quality temporal proposals generation in untrimmed videos. Capsule networks can avoid some limitations of the invariance caused by pooling and inability from convolutional neural networks, which can better understand the temporal relations for temporal action detection. However, because of the extremely computationally expensive procedure, capsule network is difficult to be applied to the task of temporal action detection. To address this issue, this paper proposes a novel U-shaped capsule network framework with a k-Nearest Neighbor (k-NN) mechanism of 3D convolutional dynamic routing, which we named U-BlockConvCaps. Furthermore, we build a Capsules Boundary Network (CapsBoundNet) based on U-BlockConvCaps for dense temporal action proposal generation. Specifically, the first module is one 1D convolutional layer for fusing the two-stream with RGB and optical flow video features. The sampling module further processes the fused features to generate the 2D start-end action proposal feature maps. Then, the multi-scale U-Block convolutional capsule module with 3D convolutional dynamic routing is used to process the proposal feature map. Finally, the feature maps generated from the CapsBoundNet are used to predict starting, ending, action classification, and action regression score maps, which help to capture the boundary and intersection over union features. Our work innovatively improves the dynamic routing algorithm of capsule networks and extends the use of capsule networks to the temporal action detection task for the first time in the literature. The experimental results on benchmarks THUMOS14 show that the performance of CapsBoundNet is obviously beyond the state-of-the-art methods, e.g., the mAP@tIoU = 0.3, 0.4, 0.5 on THUMOS14 are improved from 63.6% to 70.0%, 57.8% to 63.1%, 51.3% to 52.9%, respectively. We also got competitive results on the action detection dataset of ActivityNet1.3.

Knowledge representation learning (knowledge graph embedding) plays a critical role in the application of knowledge graph construction. The multi-source information knowledge representation learning, which is one class of the most promising knowledge representation learning at present, mainly focuses on learning a large number of useful additional information of entities and relations in the knowledge graph into their embeddings, such as the text description information, entity type information, visual information, graph structure information, etc. However, there is a kind of simple but very common information — the number of an entity’s relations which means the number of an entity’s semantic types has been ignored. This work proposes a multi-source knowledge representation learning model KRL-NER, which embodies information of the number of an entity’s relations between entities into the entities’ embeddings through the attention mechanism. Specifically, first of all, we design and construct a submodel of the KRL-NER LearnNER which learns an embedding including the information on the number of an entity’s relations; then, we obtain a new embedding by exerting attention onto the embedding learned by the models such as TransE with this embedding; finally, we translate based onto the new embedding. Experiments, such as related tasks on knowledge graph: entity prediction, entity prediction under different relation types, and triple classification, are carried out to verify our model. The results show that our model is effective on the large-scale knowledge graphs, e.g. FB15K.

Energy harvesting rates of sensors in renewable (e.g., solar energy) wireless sensor networks are not only lower than their energy consumption rates but also temporally varying. Existing studies exploited spatial data correlations among sensors to reduce their energy consumptions, where the data correlations mean that the sensing data of nearby sensors have high similarities. They assumed that the sensing data of nearby sensors are very likely to highly correlated. They adopted a coarse-grained spatial-correlation model, in which sensors are partitioned into different clusters such that the sensors in the same cluster have high data similarities with each other. Then, only the sensor with the maximum residual energy in each cluster sends its sensing data, while the other sensors do not. We, however, notice that the data similarities among nearby sensors in real sensor networks may vary significantly, i.e., ranging from very similar to not similar at all. Since the existing algorithms require that the sensors in the same cluster have high data similarities with each other, the sensors in a network may be partitioned into many clusters and each cluster consists of only a few sensors, where two nearby sensors belong to two different clusters if the sensing data of the two sensors are not highly correlated. Therefore, in the existing studies, many sensors have to send all their data as there are many clusters. Unlike the existing studies, in this article, we first propose a fine-grained spatial correlation model, in which sensors are partitioned into only a few clusters and each cluster consists of many sensors. Then, each cluster master sensor sends all its data to the sink, while the majority of other sensors in the cluster transmit only their nonredundant data, thereby significantly saving sensor energy consumptions. We formulate a novel sensor clustering problem under the proposed model, which is to partition sensors into different clusters and choose a representative sensor for each cluster such that the amount of suppressed redundant data transmissions is maximized. We propose a randomized (0.5-ε)-approximation algorithm for the clustering problem, where E is a given constant with 0 <; ε ≤ 0.5. To further reduce sensor energy consumption, we consider temporal data correlations, where the sensing data by a sensor in a short period are likely to be highly correlated. We investigate a data utility maximization problem that allocates sensor data rates and routing so that the accumulative utility of both spatially and temporally correlated data received by the sink is maximized. We devise a near-optimal algorithm for the problem. We finally evaluate the performance of the proposed algorithms through experiments. the experimental results show that the proposed algorithms are very promising.

Bitcoin mining consumes tremendous amounts of electricity to solve the hash problem. At the same time, large-scale applications of artificial intelligence (AI) require efficient and secure computing. There are many computing devices in use, and the hardware resources are highly heterogeneous. This means a cooperation mechanism is needed to realize cooperation among computing devices, and a good calculation structure is required in the case of data dispersion. In this paper, we propose an architecture where devices (also called nodes) can reach a consensus on task results using off-chain smart contracts and private data. The proposed distributed computing architecture can accelerate computing-intensive and data-intensive supervised classification algorithms with limited resources. This architecture can significantly increase privacy protection and prevent leakage of distributed data. Our proposed architecture can support heterogeneous data, making computing on each device more efficient. We used mathematical formulas to prove the correctness and robustness of our system and deduced the condition to stop a given task. In the experiments, we transformed Bitcoin hash collision into distributed computing on several nodes and evaluated the training and prediction accuracy for handwritten digit images (MNIST). The experimental results demonstrate the effectiveness of the proposed method.

As an effective method, feature selection can reduce computational complexity and improve classification performance. A number of criteria exist for feature selection using labeled data, unlabeled data and pairwise constraints, most of which are based on the Euclidean distance. In this paper, we propose a filter method for feature selection with pairwise constraints, aiming to jointly evaluate a feature subset based on metric learning. Two criteria are designed based on the well-known Kullback-Leibler divergence for measuring the difference between must-link constraints and cannot-link constraints that can indicate the feature subset discrimination based on Keep It Simple and Straightforward (KISS) metric learning and Cross-view Quadratic Discriminant Analysis (XQDA) metric learning. To address the challenging feature selection problem, we formulate a sequential search algorithm guided by indicators that are simplified from the proposed criteria. Furthermore, we conducted several experiments on sleep staging based on electroencephalogram (EEG) recordings from the Sleep-EDF Database Expanded. The experimental results demonstrate the effectiveness of the proposed method compared with nine representative feature selection methods. On the data set from healthy volunteers and the data set from volunteers that had mild difficulty falling asleep, the classification average accuracies achieve 97.66% and 93.57% by using the proposed method, respectively.

Detection of abnormal network traffic is an important issue when builds intrusion detection systems. An effective way to address this issue is time series mining, in which the network traffic is naturally represented as a set of time series. In this paper, we propose a novel efficient algorithm, called RSFID (Random Shapelet Forest for Intrusion Detection), to detect abnormal traffic flow patterns in periodic network packets. Firstly, the Fast Correlation‐based Filter (FCBF) algorithm is employed to remove irrelevant features to decrease the overfitting as well as the time complexity. Then, a random forest which is built upon a set of shapelet candidates is used to classify the normal and abnormal traffic flow patterns. Specifically, the Symbolic Aggregate approXimation (SAX) and random sampling technique are adopted to mitigate the high time complexity caused by enumerating shapelet candidates. Experimental results show the effectiveness and efficiency of the proposed algorithm.

Smart contracts are required to be instantiated in the predeployed stage, which consumes computation resources from then on. It is a big waste in the blockchain whose nodes are composed of IoT devices, as those devices often have limited resources (such as limited power supplies or a limited number of processes to run). Meanwhile, IoT devices are heterogeneous and different smart contracts are required. If those smart contracts are instantiated previously, numerous meaningless addresses are required. In this paper, we propose to delay the instantiation of a smart contract when used and terminate it when not used, which is similar to the life cycle of a variable. Then, a new kind of variable (the wrapping variable) is used to hide details of the instantiation and the address. The smart contract is instantiated in the construction function of the wrapping variable, or even it is delayed to the time when there are requests for it. The smart contract terminates when the variable is out of its scope. Then, different instantiation methods are proposed. Finally, we perform the qualitative comparison between the proposed approach and the predeployment method, and it demonstrates that the proposed methods optimize the life cycle of the smart contract and save calculation resources.

Blockchain technology has emerged as a novel distributed ledger technology, facilitating data sharing and system management securely and efficiently without interventions from a central authority. However, blockchain technology alone is not suitable for enterprise-class applications, mainly due to the limitations in capacity expansion and verification speed of blockchain systems. This paper proposes a secure and effective construction scheme for blockchain networks to improve performance and address the effective management concerns of blockchain data based on transaction categories. We designed a network link protocol to construct a directed acyclic graph (DAG) blockchain network and used a sharding protocol to divide the DAG blockchain into multiple category shards to process transactions in parallel. We then extensively evaluated our proposed design on local clusters. The experimental results show that our link and shard protocols achieved high throughput and the category-based sharded DAG blockchain demonstrated high scalability.

Hadoop MapReduce is a leading open source framework that supports the realization of the Big Data revolution and serves as a pioneering platform in ultra large amount of information storing and processing. However, tuning a MapReduce system has become a difficult task because a large number of parameters restrict its performance, many of which are related with shuffle, a complicated phase between map and reduce functions, including sorting, grouping, and HTTP transferring. During shuffle phase, a large mount of time is spent on disk I/O due to the low speed of data throughput. In this paper, we build a mathematical model to judge the computing complexity of different operating orders within map-side shuffle, so that a faster execution can be achieved through reconfiguring the order of sorting and grouping. Furthermore, a three-dimensional exploring space of the performance is expanded, with which, some sampled features during shuffle stage, such as key number, spilling file number, and the variances of intermediate results, are collected to support the evaluation of computing complexity of each operating order. Thus, an optimized reconfiguration of map-side shuffle architecture can be achieved within Hadoop without extra disk I/O induced. Comparing with the original Hadoop implementation, the results show that our reconfigurable architecture gains up to 2.37χ speedup to finish the map-side shuffle work.

Scheduling real-time update transactions to maintain data freshness is an important issue in real-time database system design, especially for data-intensive embedded applications. Despite years of active study, almost all the past work assumes that the jitter of sensor update transactions can be ignored. But actually, this assumption is not practical in real Cyber-Physical Systems (CPSs), and the existing methods cannot be applied in the design of distributed CPSs. In this paper, we propose, to our best knowledge, the first approach to maintain temporal consistency of real-time data objects for dynamic-priority scheduling when transmission delays are considered. We first propose a method called JB-EDF which calculates periods and deadlines for update transactions with significantly lower workload than that by existing approaches. Next, in order to handle more update transactions, we further propose an enhanced solution called JB-EDF* which use JB-EDF as a building block. JB-EDF is more suitable for small scale applications, while JB-EDF* is more attractive for large scale applications. Finally, extensive simulation experiments are conducted. The results demonstrate that JB-EDF and JB-EDF outperform existing solutions in terms of both acceptance ratio and system workload, and the proposed methods are also time-efficient.

Due to the rapid development of the sharing economy, shared bikes have become one of the most popular and convenient traveling tools in intelligent transport systems. Aiming to save the time spent on waiting for or searching bikes at bike stations, the operators of bike-sharing systems need to dynamically dispatch bikes. Predicting the number of bikes for each station can help to optimize the repository of bikes. The usage of bikes is affected by several uncertain factors, so bike number prediction becomes a challenging and difficult problem. To manage this problem, we propose an algorithm called bimodal Gaussian inhomogeneous Poisson (BGIP) to predict the number of bikes. The BGIP includes three steps. First, the inhomogeneous Poisson process is adopted to describe the process that people arrive at a bike station to pick up or return bikes. Second, the bimodal Gaussian function is used to describe the intensity function of inhomogeneous Poisson process. In order to dynamically uncover the changing trend in the usage state of bikes, we propose a method to measure the influences of external factors on the usage of bikes. Third, the number of bikes is predicted by calculating the mean usage of bikes on the basis of checking-out and checking-in sequences. Experiments demonstrated that our algorithm outperformed the baseline algorithms in solving the bike prediction problem: accurately predicting the number of bikes and determining whether there is at least one bike available at a bike station.

Several privacy protection technologies have been designed for protecting individuals’ privacy information in data publishing. It is often easy to make additional information loss of a dataset without measuring the strength of privacy protection it required. To apply appropriate strength of privacy preservation, the authors put forward privacy score, a new metric for making a comprehensive evaluation of the privacy information contained in the pre‐published dataset. Using this measure, publishers can apply the privacy techniques to the pre‐published dataset in accordance with the privacy level it belongs to. The privacy score is determined by the amount as well as the quality of privacy information in which the pre‐published dataset is contained. Furthermore, the authors present a data sensitivity model based on analytic hierarchy process for assigning a sensitivity score to each possible value of a sensitive attribute. The reasonability and effectiveness of the proposed approach are verified by using the Adult dataset.

Now, mobile applications grow at an exponential speed and their evolution activities are very active, while there is little research on the evolution of mobile apps. To have a better understanding of the evolution of mobile apps and find similarities or patterns in their evolution process, we conduct an empirical study on long spans in the lifetime of 8 typical open-source mobile apps, which covers 348 official releases. First, we try to verify whether Lehman's laws still apply to mobile apps or not, extract a variety of metrics of the apps, and use statistical hypothesis testing to validate these laws. We find enough data that support a subset of Lehman's laws, while the rest do not. Second, we make some novel observations, eg, the growth of mobile apps is nonsmooth, and some versions of the apps have a great growth in their evolution. Enough data confirming that software instability increases great with the addition of third-party method invocations, and automatic build and manage tool based on contract is introduced into project as apps continue evolving.