Graph neural networks (GNNs) have attracted significant attention in human action recognition (HAR) tasks due to their ability to model spatial-temporal relationships between body joints in skeletal graphs. However, prior solutions typically suffer from high computational overhead and significant inference latency, particularly on resource-constrained platforms like edge devices. To mitigate this, we propose Edge-STGNN, a speed-optimized neural architecture search (NAS) framework, for the automated design of spatial-temporal GNNs (STGNNs) tailored for action recognition on edge devices. Specifically, Edge-STGNN defines a search space that includes diverse temporal convolutional layers, attention mechanisms, and network depth options. Based on this search space, it constructs the supernet using a single-path training approach. Subsequently, an evolutionary algorithm-based search strategy is applied to identify optimal architectures. Furthermore, Edge-STGNN integrates an efficient speed predictor to reduce evaluation time, and employs a small-scale proxy dataset to lower search costs. Comprehensive experiments on multiple datasets and edge devices demonstrate that Edge-STGNN effectively identifies architectures with fewer parameters, reduced computational complexity, and faster inference speeds, while preserving acceptable recognition accuracy. Additionally, by incorporating speed constraints, Edge-STGNN enables a flexible trade-off between accuracy and speed tailored to specific application demands. For example, on the NTU RGB+D 60 xview60 benchmark, compared to all baseline methods with reported speeds, Edge-STGNN achieves a 2.4× to 9.4× inference speedup while maintaining accuracy within a ±1% margin.

Internet of Things (IoT)-based bird monitoring system faces challenges from occlusion, arbitrary postures, and similar species. To address these, we exploit two key properties of bird images: self-correlation within individual birds and appearance similarity across species, revealing self-correlation and consanguinity relationships. We propose a similarity cues-aware consanguinity relationship mining (CoSimR) framework, which leverages these relationships for robust classification. CoSimR comprises two modules: Consanguinity Relationship Mining (CRM) and Cross-species Avian Prediction (CAP). CRM can capture skeletal structures by generating the correlation tokens, while consanguinity tokens encode p information across five taxonomic levels (class, order, family, genus, species). CAP leverages a consanguinity-driven multi-loss function, including homogeneity loss for intra-species consistency and affinity loss for cross-species similarity, to guide discriminative feature learning. Experiments conducted on two fine-grained bird image classification datasets demonstrate that the CoSimR model achieves better performance compared with state-of-the-art methods. It highlights the effectiveness of integrating biological hierarchy with visual features, paving the way for similarity cues-aware approaches in fine-grained classification tasks.

With the rapid development of the Internet of Vehicles (IoV), achieving trustworthy vehicle position verification while preserving location privacy has become a key requirement in intelligent traffic supervision scenarios such as defense control zones and urban restricted-access areas. Existing privacy-preserving schemes have difficulty simultaneously supporting accurate determination of complex-shaped prohibited areas and efficient computation, and still face malicious attacks such as interference with verification procedures, tampering with communication processes, and privacy inference when determining the positional relationship between vehicles and prohibited areas. To address these issues, this paper proposes an efficient privacy-preserving position verification (PPPV) scheme based on secure multi-party computation (MPC). The scheme supports arbitrary polygonal prohibited areas, including convex, concave, and self-intersecting polygons, thereby improving its applicability in complex IoV supervision scenarios. Based on an improved cross-product determination method, this paper constructs an efficient PPPV protocol under the semi-honest model, achieving near-plaintext computational efficiency while protecting the privacy of both vehicle locations and area boundaries. To resist malicious attacks, this paper further combines Paillier homomorphic encryption, the cut-and-choose method, and zero-knowledge proof to construct a secure PPPV protocol under the malicious model, which can effectively prevent protocol deviations, result tampering, and inference attacks. This paper also conducts formal security proof based on the real/ideal model paradigm, and evaluates the performance of the scheme through benchmark experiments and attack experiments. Experimental results show that the scheme achieves a good balance among efficiency, applicability, and security, providing a deployable trustworthy position verification mechanism for next-generation IoV intelligent supervision applications.

How to automatically recognize the bird species has caused concerns in ecological systems and intelligent ecological monitoring system due to the increasing threat to bird ecological society and bird species diversity. However, traditional monitoring systems are susceptible to specific challenges when operating in complex environments, such as complex environments, multifarious postures and backlight scenarios. To effectively address these challenges, we present a novel bird ecological intelligent detection system (TransSIL) for fine-grained bird image classification (FBIC) in diverse ecological to learn discriminative features by explicitly incorporating silhouette structural information alongside critical visual cues. Specifically, the approach begins with a silhouette token construction module to estimate the bird silhouette and extract silhouette tokens. Then, a silhouette relationship mining module is developed to fuse visual and silhouette tokens and capture long-range dependencies between them. In addition, to learn bird distinctive features at multiple levels, a critical cues awareness module is embedded within TransSIL. The performance of TransSIL was evaluated on two bird datasets: CUB200-2011 and NABirds. The framework demonstrates significant improvements over existing ecological intelligent surveillance methods. By utilizing silhouette and visual dependencies, we anticipate that our approach will ultimately contribute to the conservation of avian ecological societies.

With the widespread adoption of Electronic Health Records (EHRs), ensuring data privacy while enabling efficient sharing and processing has emerged as a critical challenge in healthcare information systems. Existing approaches often struggle to balance stringent security requirements with practical performance needs. To address these challenges, this paper proposes a blockchain-based privacy protection scheme for EHRs that integrates Designated Verifier Aggregate Signatures (DVAS) and Fully Homomorphic Encryption (FHE). In the proposed scheme, healthcare institutions manage EHRs through smart contracts, while FHE is employed to encrypt and protect sensitive medical data used in research. By selectively applying FHE to critical data and minimizing encryption overhead for non-sensitive information, our design significantly enhances computational efficiency and storage performance. Furthermore, the DVAS algorithm minimizes redundant data transmission, ensures data integrity, and restricts signature verification to authorized parties only. Experimental evaluations based on computational time, storage cost, and security analysis demonstrate that the proposed scheme achieves robust security while meeting performance requirements, effectively balancing confidentiality and efficiency in healthcare information systems.

With the integration of intelligent medical devices and wireless sensor networks, telemedicine queries have significantly enhanced the efficiency and accessibility of medical services. However, the security and privacy of medical query records remain a critical challenge. While existing privacy-preserving query frameworks in Wireless Body Area Networks (WBANs) primarily focus on protecting the privacy of medical records, they often lack robust mechanisms for authenticating query users. This deficiency may enable malicious entities to gain unauthorized access, thereby resulting in data leakage. To address this issue, we propose a privacy-preserving query scheme with unlinkability by integrating double-Schnorr-based anonymous authentication and 1-out-of-n oblivious transfer (OT). Specifically, anonymous authentication is achieved via a double-Schnorr-based credential construction with session-dependent pseudonym blinding, enabling the server to verify user legitimacy without revealing users’ real identities. OT is implemented through a short-hash-based keyword partition mechanism and masked encryption, ensuring that the curious server cannot infer user queries while fulfilling them correctly. Additionally, query privileges are restricted to legitimate users by verifying anonymous credentials issued during the registration phase. The formal security analysis confirms that the proposed scheme achieves essential security attributes, including anonymous authentication, query privacy, and unlinkability. Experimental results demonstrate that the proposed scheme not only integrates anonymous authentication but also outperforms baseline schemes in both computational and communication efficiency. Therefore, the proposed scheme is more suitable for real-world deployment within IoT-based healthcare ecosystems.

Integrated Sensing and Communications (ISAC) systems face challenges in balancing sparse human resource allocation with high-quality data acquisition, especially in scenarios involving highly mobile workers and uneven task coverage. While Sparse Mobile CrowdSensing (SMCS) offers a resource-efficient solution by inferring unobserved data from partial sensing tasks, two critical issues remain unaddressed: (1) existing data inference methods often assume collected data is trustworthy, neglecting the detrimental impact of malicious workers on data quality, and (2) the lack of privacy-preserving mechanisms in sparse ISAC may deter worker participation, compromising service quality. To address these, we propose a Quality-driven Privacy-preserving Truth Discovery scheme (QPTD), which optimizes sparse sensing and data quality with privacy constraints in ISAC systems. QPTD integrates a trust-aware Deep Reinforcement Learning (DRL) algorithm for dynamic cell selection, prioritizing cells with high-trust workers and minimizing recruitment costs, while a matrix encryption protocol ensures end-to-end privacy protection for both workers and tasks. By leveraging Matrix Factorization (MF) on encrypted sparse data aggregated from high-trust workers, QPTD infers unobserved tasks and safeguards sensitive information. Theoretical analysis proves the robustness of our privacy guarantees against adversarial attacks. Evaluations on real-world datasets demonstrate that QPTD improves ISAC system performance by increasing total data quality by 1.73%–142.07%, reducing estimation errors by 24.82%–75.32%, and cutting recruitment costs by up to 62.03% while ensuring service performance.

This is a corrigendum for the article “LyDRL: Lyapunov-guided Deep Reinforcement Learning for Stable Task Offloading in Connected Autonomous Vehicles” published in ACM Trans. Autonom. Adapt. Syst. 20, 3, Article 24 (September 2025), 29 pages.

Dynamic multiobjective optimization problems (DMOPs) frequently arise in real-world applications, where environments evolve over time. A central challenge in solving DMOPs is the ability to accurately and efficiently track optimal solutions as the environment changes. Existing dynamic multiobjective evolutionary algorithms (DMOEAs) based on single-strategy mechanisms often perform well only under specific environmental conditions, while multistrategy cooperative frameworks tend to suffer from delayed decision-making in rapidly changing, highly uncertain scenarios To address these shortcomings, this article introduces a fuzzy inference-based adaptive multistrategy-DMOEAs (FIAMS-DMOEA). This approach is designed to enhance real-time responsiveness to sudden environmental changes and mitigate decision-making latency. In the proposed method, a randomly dynamic fuzzy system serves as the foundation for population-level adaptability, enabling the algorithm to cope with highly uncertain environmental dynamics. This is further augmented by a multistrategy adaptive response mechanism (MSAR) that improves overall adaptability. By leveraging fuzzy rules, the algorithm can perceive environmental uncertainty in real time and design differentiated migration strategies, thereby establishing a robust foundation for subsequent optimization. The adaptive response mechanism centers on a multistrategy weight update method based on individual dimensional effects, which evaluates individual contributions from a dimension-specific perspective. Extensive experimental results on a suite of 32 benchmark DMOPs demonstrate that FIAMS-DMOEA consistently outperforms five state-of-the-art DMOEAs in overall performance.

This article investigates optimization-driven learning techniques to address the critical challenge of balancing communication efficiency with convergence acceleration in distributed multiagent systems. While existing accelerated methods typically necessitate multiple internode communications per iteration, we propose two novel methods, heavy-ball exact fusion (HBEF) and Nesterov-accelerated exact fusion (NAEF), that maintain a single-communication operation while achieving enhanced convergence. By fusing momentum mechanisms with bias correction, the developed methods not only precisely preserve convergence guarantees but also demonstrate accelerated convergence compared to baseline exact diffusion and contemporary accelerated counterparts. A distinctive dual acceleration method is further proposed through momentum parameter coordination. Rigorous convergence analysis reveals the momentum parameter's critical role in acceleration behavior. Extensive numerical evaluations across representative machine learning tasks validate the proposed methods' superiority in both transient convergence speed and steady-state accuracy. Notably, they achieve state-of-the-art performance at half or one-third the communication cost, effectively bridging the long-standing gap between communication efficiency and rapid convergence in distributed learning.

In industrial cyber-physical systems (CPS), maintaining large-scale, long-lived software stacks demands rapid retrieval of protocol-compatible and system-context-aware code segments to avoid costly downtime and safety issues. Existing code search methods lack system-aware reasoning and suffer from rigid semantic fusion, leading to fragmented understanding of intercomponent dependencies in industrial software. To tackle these pain points, this article proposes an industrial-oriented hierarchical feature and graph-based code search (HFGCS) framework with sample-aware hierarchical fusion and hub-centric heterogeneous graph reasoning, customized for CPS maintenance requirements. It integrates a dynamic layer aggregation (DLA) module for adaptive multigranularity semantic fusion (capturing syntax-to-logic features based on query intent) and an integrated hub context encoder (IHCE) module that constructs a heterogeneous CPS graph with a global hub node to propagate cross-component dependencies (control logic, communication protocols, sensor/actuator bindings, and runtime constraints). A learnable gating network dynamically balances these representations to achieve intent-aligned and system-consistent code retrieval. Extensive experiments on CodeSearchNet-C show HFGCS improves MRR by 7.3%–15.9% over state-of-the-art baselines with strong cross-backbone robustness. Industrial validation confirms it shortens development cycles, enhances efficiency and accuracy, and prevents protocol mismatches in safety-critical CPS software. Feasible for deployment with topology-aware reasoning, HFGCS serves as a scalable and reliable retrieval engine, providing high-quality compatible code references to underpin the stable operation of industrial CPS and aligning with the core scope of Industrial Informatics. The code and data used in our study are available at online.

Automatic facial expression recognition (FER) from facial videos is a key component in enabling machines to understand human emotional states, which is crucial for affective robots designed to be interactive companions and applied in smart healthcare. However, FER is susceptible to challenges such as occlusion, arbitrary orientations, and illumination, making it difficult to implement precise FER models in robots. To address these issues, we propose a texture affinity cues-aware relationship representation method (FTATrans), which learns to associate facial texture with facial expressions in videos. The research reveals two key findings: 1) interaction of facial textures, and 2) texture affinity effects. On this basis, FTATrans mainly consists of two key networks: semantic-information feature generation (SFG) and texture-affinity relationship mining (TAR). In particular, the semantic relationships between different facial regions can be learned through SFG. TAR is used to capture the texture affinity relationship and integrate them with the overall facial expression information. Additionally, a loss function focused on expression-specific texture variations is proposed to guide the model in learning discriminative expression information. Experiments conducted on five video-based FER datasets demonstrate that the FTATrans model achieves state-of-the-art performance.

Cloud-fog Federated Learning (FL) is promising for collaborative model training in large-scale edge networks. In cloud-fog FL with constrained communication resources, selecting high-quality client models for aggregation is critical to boost the global model. However, the clients in real world hold multimodal and heterogeneous data, while existing selection strategies rarely consider the imbalance of communication cost and model convergence rates across modalities, thus seriously degrading the efficiency of model aggregation. Moreover, most previous multimodal fusion methods require aligned multimodal samples. However, the data of modality-heterogeneous clients may be isolated and unaligned in FL, so these previous methods cannot be applied to such scenarios, thereby hindering the knowledge fusion across various modalities. To address the above issues, we propose a cloud-fog FL system named COOL, which achievesunimodal aggregationat the fog layer andmultimodal fusionat the cloud layer. First, we propose a Modality-aware Online Client Selection (MOCS) strategy to assist unimodal aggregation. Unlike previous selection strategies, MOCS realizes the dynamic selection budget allocation for various modalities by monitoring the convergence gap of modalities, thus striking the performance balance among modality-heterogeneous clients. Second, to overcome the limitation of previous methods that require aligned multimodal data, we propose a Multimodal Fusion strategy with Feature Synthesis (MFFS). MFFS realizes multimodal fusion with isolated samples via adaptive feature synthesis and cross-modal attention training, thus building a more powerful multimodal predictor while preserving the client data privacy. Finally, experimental results demonstrate that COOL has superior performance compared to the existing algorithms.

Edge computing deploys computing and storage resources at the network edge, thereby providing services closer to terminal users. However, in edge networks, the mobility of terminals, the diversity of requests, and the dynamic nature of wireless channels pose significant challenges for efficiently allocating limited wireless and caching resources among multiple terminal devices. To address the issues of unbalanced network load and high caching costs caused by resource allocation in edge networks, we propose a Distributed Online Joint Optimization Scheme (DOJS). Specifically, we design a joint optimization scheme, referred to as DOJS, which combines centralized user association at the cloud with distributed cache placement at the base stations. This scheme analyzes the impact of terminal device association policies on caching costs and develops a caching cost model that integrates the activity level and content request probability of terminal devices. Based on this model, the relationship between user association selection and caching costs is analyzed, and a Game Theory-based User Association (GTUA) selection algorithm is proposed. In order to adapt to the dynamic characteristics of terminal-user requests in mobile edge networks, we develop a dynamic cache update method LS-TD3, which combines Long Short-Term Memory (LSTM) and Twin Delayed Deep Deterministic policy gradient (TD3). Specifically, we integrate the LSTM layer into the policy model framework of reinforcement learning to better predict the content popularity from dynamic time data, thus improving the accuracy of cache decision making. To further reduce computational complexity and enhance overall system performance, we employ a distributed optimization strategy to improve the dynamic caching decision process. Extensive experimental results demonstrate the superiority of the proposed algorithm in achieving inter-node load balancing and minimizing caching costs.

Deep graph clustering aims to uncover latent structural patterns within user behavior graphs to effectively group users based on behavioral similarity, thereby enhancing the efficiency of personalized service recommendation. However, most existing generative clustering methods heavily rely on encoder–decoder architectures and their pretraining procedures, which not only increase training complexity but also result in insufficient robustness and unstable optimization. To address these limitations, this paper proposes an Anchor-Guided Graph Clustering Network (AGCN) that eliminates the need for pretraining. The proposed method introduces an anchor-guided learning strategy that automatically selects high-confidence anchor samples farthest from the distribution boundary and utilizes the k-means++ algorithm to generate pseudo-labels in the diffusion feature space. Furthermore, AGCN integrates an anchor expansion strategy and a commonality enhancement mechanism to significantly improve clustering discriminability and recommendation accuracy. Theoretical analysis and extensive experiments on multiple real-world datasets demonstrate that the proposed method outperforms state-of-the-art approaches in terms of both clustering accuracy and stability.

Coordinate classification is an efficient approach to 2-D human pose estimation (HPE), treating keypoint predictions as sub-pixel bins along horizontal and vertical axes, thereby avoiding the computationally intensive upsampling process required in traditional heatmap-based methods. In this article, we introduce the Position Encoding Coordinate Classification (PECC) system, which enhances coordinate classification by embedding position information directly into keypoint feature representations through a novel position encoding mechanism. We further design a tailored attention mechanism, Filtering Amplified Attention (FAA), optimized for coordinate classification. FAA provides finer relative positional information, improving the system’s ability to model relationships between keypoints and enhancing coordinate localization accuracy. Our method maintains the efficiency of coordinate classification by utilizing 1-D vectors, significantly reducing model parameters and computational cost. Additionally, the incorporation of positional encoding enhances the system’s ability to effectively model and exploit spatial information within a coordinate-classification-based pose estimation framework. Extensive experiments on mainstream datasets demonstrate that PECC achieves superior accuracy and robustness in 2-D HPE, advancing the state-of-the-art in this domain.

With the swift advancement of the Internet of Things (IoT) and artificial intelligence (AI), various technologies have been integrated into wearable medical health devices, improving users’ awareness of their physical states and enabling the analysis of a greater amount of human data. However, these sensitive pieces of information are prone to tampering or theft during storage and transmission, posing security risks. In this article, we propose a multiattribute sketch secure data sharing scheme for IoT wearable medical devices based on blockchain (MASS). We introduce a multiattribute sketch storage method that stores the encrypted hash of health data transmitted by medical wearable devices on the blockchain. This work also designs a ciphertext-policy attribute-based encryption (CP-ABE) access control mechanism that effectively addresses the secure sharing of data from wearable medical devices among healthcare professionals. Experimental findings indicate that with the rise in the number of medical health data documents, the costs associated with index generation and search time decrease by 55.3% and 10.83%, respectively. Additionally, as the frequency of data access increases, there is a 13.5% reduction in encryption time, and the implementation of multiattribute sketches results in a 24.8% and 11.3% reduction in index generation and search times, respectively.

Trajectory data analysis is crucial in various applications but presents significant privacy risks, as location data can reveal sensitive information. Existing privacy protection methods, such as spatiotemporal K-anonymity and L-diversity, are vulnerable to semantic inference attacks, where public data is exploited to re-identify users. To address these challenges, we propose dummy-trajectory synthesis (DTS), an efficient privacy protection scheme for location-based social networks (LBSNs). DTS enhances privacy by leveraging users’ frequent behavioral sequences to generate synthetic dummy trajectories. Unlike traditional approaches, DTS considers both geographic and semantic data by segmenting historical trajectories into time periods using the OPTICS clustering algorithm. This enables the identification of regions with specific semantic attributes and the mining of semantic trajectory sequences. DTS optimizes dummy trajectory generation by combining Euclidean distances and semantic similarity, ranking historical points and establishing transition relationships. Experimental results show that DTS significantly improves privacy protection and performance compared to existing methods, without compromising service quality. DTS offers a robust solution for protecting trajectory data privacy in LBSNs against transition probability attack for joint time periods.

Vehicle Re-Identification (Re-ID), which is a significant application in the Internet of Things, aims to accurately retrieve the remaining images of a given vehicle across different cameras views. The improvement in vehicle Re-ID performance largely stems from better addressing the issues of inter-class similarity and intra-class variance. Existing methods, relying solely on max or average pooling after using attention modules, fail to obtain significantly complete and pure global and local features, and neglect the false guidance that some unique individual information on images bring to re-identification. Moreover, models combining global and local features have shown good results in vehicle Re-ID, but these successes neglect the interaction between features across different convolutional layers, resulting in the loss of crucial details for vehicle Re-ID. To tackle these issues, we introduce a Patches Enhancement and hierarchical Fusion Network (PEFN) based on a multi-branch architecture, divided into a Global and Local Attention Supplement (GLAS) branch, and an Enhanced Hierarchical feature fusion (EnHi) branch. The GLAS branch, through the Identity-related Feature Remodeling (IDFR) module’s staged supplementation of spatial and channel features, has achieved the enhancement of both global and local features and effectively mitigated the negative impacts of individual information. The EnHi branch enhances the robustness of feature representation by interacting hierarchical features. Extensive experiments on two large-scale vehicle re-identification datasets demonstrate that our PEFN method outperforms state-of-the-art vehicle re-identification approaches. Specifically, without utilizing extra data and re-ranking, our model achieves 85.15% mAP on the VeRi776 dataset. Code is available at https://github.com/711L/PEFN.

Assigning tasks to reliable workers to obtain reliable data is a critical issue in Mobile CrowdSensing (MCS). The challenge is compounded by the problem of Information Elicitation Without Verification (IEWV), which renders traditional data quality evaluation methods ineffective. While some studies attempt to address this, they often struggle to assess workers’ dynamic trustworthiness, resulting in unreliable data. To overcome these challenges, we propose the Trust and Time-sharing Task Allocation based Truth Discovery (TTTA-TD) scheme, designed to ensure reliable data collection in MCS. This scheme includes three components: (a) Classification-based Trust Evaluation (CTE) that classifies workers based on behavior and applies tailored penalties—lenient for honest workers and stricter for malicious ones, (b) Trust-based Truth Data Discovery (TTDD), which improves truth data accuracy by integrating trust scores, and (c) Trust and Time-sharing Task Allocation (TTTA) which allocates tasks to ensure data reliability and minimize time-sharing disparities. Experimental results show that the TTTA algorithm reduces average time-sharing by 93.95%. The TTDD algorithm improves truth estimates across all dataset qualities, and the TTTA-TD scheme enhances data reliability by 0.35%, 2.06%, and 7.41% in high, medium, and low-quality datasets respectively.

The Pre-trained Foundation Models (PFMs) based on edge inference reduce communication latency from the cloud server to the user mobile devices, and support different edge nodes to provide adaptive Artificial Intelligence (AI) services to the users. The complex network environment has attracted widespread attention to the communication security of cross-domain AI services. Authentication protocol is an important way to ensure the communication security among entities. However, existing cross-domain authentication protocols have not taken into account the communication delay caused by complex cryptographic operations on the mobile devices. To address these issues, a Cross-Domain Inference Authentication (CDIA) protocol for PFMs is proposed in this paper. The complex cryptographic operations are executed collaboratively by the servers from different domains to realizes decentralized authentication. Besides, the correctness and security of CDIA protocol is proved by formal analysis. The security of CDIA protocol is verified by using the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool. The security of negotiated group key is proved by Real-or-Random (RoR) model. Finally, the performance of CDIA protocol is evaluated to show its efficiency. Compared with related protocols, the computing cost and communication overhead of CDIA protocol are at least 52.38% and 11.9%, respectively.

The digital economy is one of the most dynamic, fastest growing, and impactful domains in modern economic development. To fully exploit the potential value of data as an economic resource, many governments have implemented policies to encourage and regulate data circulation and trade. Despite the increasing adoption of blockchain-based data trading platforms, a significant challenge persists: verifying data quality without compromising privacy. This deficiency reduces data utilization efficiency and limits its economic potential We propose HES, a decentralized scheme for data quality verification in transactions, which integrates homomorphic encryption, blockchain, and zero-knowledge proofs to balance privacy and verifiability. The proposed method introduces data meta-certificates to represent the original data and employs cryptographic methods to enable zero-knowledge verification. This mechanism supports the verification of both data content and quality while preserving privacy. Data buyers can verify datasets without accessing specific content or involving third-party intermediaries. In this decentralized framework, data buyers can independently verify whether a dataset meets their requirements based solely on its content and quality indicators. Our experiments show that the proposed approach solves the challenge of reaching data quality consensus in distributed scenarios, enabling data buyers and sellers to swiftly align on quality standards. This facilitates the secure circulation and trade of data assets while unlocking their economic value. The findings offer a robust, privacy-preserving consensus mechanism for evaluating data quality in transactions.

Mobile crowd sensing (MCS), as an innovative data acquisition model in the Internet of Things (IoT), employs an incentive mechanism based on users’ reputation evaluation, which is a mainstream reward allocation method. However, in the existing incentive mechanisms based on reputation evaluation, unidirectional incentive strategies and nonadaptive reputation models result in unequal reward allocation. To tackle this issue, we propose an effective interactive incentive mechanism based on adaptive reputation evaluation. Specifically, we generate user status thresholds to classify, rate, and weight user behaviors, based on the average quality thresholds of tasks released or data submitted by different users in each interaction round. Meanwhile, we achieve multiparty consensus by incorporating the obtained user reputation values and combining them with the cumulative reputation values from multiple rounds to obtain adaptive reputation evaluation results. Moreover, we design an interactive incentive strategy that measures users’ incentive values based on their reputation evaluation results in each round, mutually punishing malicious behaviors from both the publisher’s and the worker’s perspectives. Extensive experiments have demonstrated that our method consistently outperforms existing advanced incentive mechanisms.

With the rapid growth of Internet of Things (IoT) applications, ensuring the data privacy and security has become a critical concern. The security of integrated circuits (ICs) in IoT systems is of paramount importance. As IC integration increases, circuit testing becomes increasingly challenging. Design For Testability (DFT) techniques improve testability, but scan-based methods can be easily exploited by unauthorized users to steal confidential information. For this reason, researchers have proposed various defense techniques, but each technique has its limitations. In order to effectively protect the scan chains of ICs from illegal access, this paper presents a novel DFT framework based on hidden, randomly inserted key seeds and Physical Unclonable Function (PUF). A multiple-input shift register is used as a test key generator, which receives the hidden test key seed from randomly selected scan chains, and then generates a unique key for each test pattern. An authentication module is also introduced to verify the validity of the test patterns. Through key verification, if a test vector contains valid key seed, the design will perform normal scan operations. Conversely, if the seed hidden in a test vector cannot generate the expected test key, the scan output data will be scrambled by the random PUF responses, thereby effectively preventing attackers from inferring sensitive information. Theoretical analysis and experimental results show that the proposed secure DFT protects the cryptographic chip from all known scan-based side-channel attacks, while requiring no additional test preparation time and incurring only negligible low area overhead.

With the development of the Internet of Things (IoT) towards supporting next-generation security and distributed applications, the integration of quantum communication technology provides a promising way to enhance data security, computing power, and perception accuracy. The large-scale interconnection of quantum nodes enables the formation of quantum enhanced IoT networks, with high fidelity entanglement serving as the cornerstone of secure and efficient communication. However, the inherent limitations of quantum resources and channel noise pose significant challenges to routing and resource management. This article constructs the quantum path selection problem as a multi criteria path selection problem, designs a weight scoring function to obtain the optimal path in the current network for each request, and proposes a two-stage routing and allocation strategy TPROA, namely the idea of "guarantee before utilization", aiming to maximize network throughput and improve resource utilization in resource constrained network environments. The simulation results show that compared with traditional methods, TPROA significantly improves quantum resource utilization and network throughput. The proposed method lays the foundation for scalable quantum IoT integration.

Mobile Edge Computing (MEC) pushes computing resources from the network center to the network edge to provide efficient and reliable computing services. However, due to the mobility and diversity of mobile users (MUs), edge servers (ESs) are likely to be overloaded, which leads to a rapid decline in the quality of service provided by the MEC system. This paper introduces unmanned aerial vehicles (UAVs) to solve the problem of ES overload. This paper presents an effective Multi-UAV Collaborative Decision-Making System (MCDS) tailored to optimize task offloading and resource allocation in MEC networks. The proposed system seeks to reduce task computing delays and energy consumption while guaranteeing timely task completion and compliance with resource constraints. We design a distributed two-stage optimization algorithm to jointly optimize the task offloading decision and resource allocation of the collaborative computing system (UAVs, ESs, and MUs). To address the flight cooperation problem of multi-UAVs, we also proposed a task-aware scheduling algorithm for multi-UAVs. In addition, we conducted a large number of simulation experiments. Experimental results show that our two-stage optimization scheme is lower than other benchmark algorithms in terms of task completion delay, energy consumption and total cost of objective function. Specifically, compared with seven baseline algorithms under three different system setting scenarios, our algorithm reduces the total cost by an average of 38.79%, the energy consumption by an average of 40.70%, and the delay by an average of 15.67%.

Federated learning (FL) has emerged as a leading methodology for facilitating collaborative edge learning (EL) across Artificial Intelligence of Things (AIoT) devices, enabling efficient model training and bolstering privacy protection. Nevertheless, current EL methods that depend on trusted servers engender apprehensions concerning potential data leakage and misuse. Moreover, the untrusted AIoT environment increases security threats in EL collaboration. In addressing these challenges, we introduce an innovative swarm reputation (SR)-based decentralized autonomous organization (DAO) autonomous FL framework, SRFL. Within SRFL, we utilize DAO nodes as autonomous units for processing local services, effectively diminishing the communication overhead attributed to frequent interactions, the SR-based DAO committee oversees the FL process and ensures model consistency. SRFL seamlessly integrates FL with the distributed consensus process and introduces an SR-based consensus mechanism to enhance the collaboration process’s trustworthiness. SR utilizes a hierarchical reward and punishment mechanism, designed to equitably reward honest participants and hammer penalize those undermining the system’s stability. Through extensive experimentation with SRFL, employing different models and datasets, we have substantiated its superior performance in efficiency and robustness.

Mobile Crowd Sensing (MCS) is a new sensing paradigm that uses portable intelligent mobile devices (smartphones, wearable devices, etc.) carried by users to capture dynamic changes of social and urban information. Thanks to the ubiquity of mobile devices, MCS is able to collect data efficiently and achieve some valuable practical applications. Research reviews on MCS mostly focus on a single direction such as task assignment or incentive mechanism. This paper comprehensively reviews the research work on task allocation, incentive mechanisms, quality control and privacy protection in recent years. First, the system architecture and workflow of MCS are described and representative applications of MCS are illustrated. Furthermore, taking MCS as the target object, this paper expounds and discusses key issues such as task allocation, incentive mechanisms, quality control, and privacy protection, and introduces the latest research results in these fields. Subsequently, we provide a systematic review of the datasets reported in the literature. Ultimately, we point out the limitations of existing studies and look forward to future research directions to provide valuable references for related researchers.

As a data-free knowledge transfer paradigm, federated learning (FL) provides a novel solution for knowledge fusion in smart cities, especially in the field of Internet of Vehicles (IoV). However, the bandwidth bottleneck in the IoV limits the efficiency of federated collaboration, while trust issues associated with aggregation servers reduce users’ willingness to collaborate. To address these challenges, this article proposes a multiple collaborative iterative distillation-based swarm learning (CISL) framework for IoV. CISL leverages multiple collaborative iterative distillations to transform federated collaboration into serverless cross-device and cross-decentralized autonomous organization (DAO) knowledge transfer and fusion, enabling trustworthy swarm collaboration under bandwidth-constrained conditions. Moreover, it adaptively adjusts the inheritance and elimination of shared knowledge (SK) to enhance model adaptability and improve single-vehicle performance. Specifically, CISL proposes a collaborative iterative distillation mechanism that progressively integrates knowledge of other vehicles within the DAO, achieving cross-device SK fusion. Meanwhile, CISL introduces a multisage collaborative distillation mechanism, enabling each DAO to collaboratively distill and integrate SK from other DAOs, thereby expanding its knowledge domain. Additionally, CISL employs a dynamic balancing strategy to adaptively regulate the inheritance and elimination of SK, optimizing local models and enhancing their performance. Comprehensive experiments conducted on six benchmarks across two scenarios demonstrate that, compared to state-of-the-art methods, CISL exhibits superior adaptability and robustness across different datasets and task scenarios.

Next-Generation Crowdsensing Networks (NGCNs) have become increasingly critical for smart cities, where data privacy and quality are pivotal concerns. Traditional trust mechanisms in crowdsensing mainly rely on static trust models, which are insufficient for dynamic security requirements. Zero-Trust security represents a promising opportunity, yet coming with notable challenges in NGCNs, including Unknown Workers Online Recruitment (UWOR), Information Elicitation Without Verification (IEWV), Privacy Preserving Data Evaluation (PPDE), and Dynamic Trust Abrupt Shift (DTAS). To address these challenges, we propose a Credibility-aware and Privacy-preserving Data collection scheme with Zero-trust (CPDZ) for secure and quality data collection in NGCNs. First, our CPDZ scheme encompasses a quality worker recruitment strategy with combinatorial multi-armed bandit models, utilizing Thompson Sampling for the secure and efficient resolution of the UWOR. Second, an active dispatching scheme for unmanned aerial vehicles is crafted to collect data as a gold standard to assist in overcoming the IEWV challenge. Third, as for the PPDE challenge, we propose a lightweight privacy-preserving scheme for dependable truth discovery and secure trust verification. Fourth, the DTAS challenge is managed by a dual verification scheme that integrates short-term and long-term trust assessments, ensuring stability and adaptability of the zero-trust security in our CPDZ scheme. Experiments confirm the superiority of our CPDZ scheme, showing a 12.5% increase in recruitment revenue and a 57.8% reduction in relative error compared to existing approaches.

Task offloading is recognized as a promising approach to enhance the computational performance of Connected Autonomous Vehicles (CAVs). Some applications of CAVs, such as metaverse applications, require substantial resources, posing significant challenges to CAVs with limited computing and storage capacities. CAVs can offload resource-intensive applications to the Vehicular Edge Computing (VEC) server, which has strong computing capabilities. To fully utilize the resources in the CAV system, partial offloading is employed. However, the local computing resources are limited for continuously generated partial offloading tasks. This results in many partially locally executed tasks experiencing long processing times or being discarded, which is detrimental to delay-sensitive tasks on CAVs. This article proposes Lyapunov function-guided reinforcement learning for the CAVs task offloading computational framework, LyDRL. Specifically, LyDRL first uses the Lyapunov function to transform the long-term objective optimization problem into subproblems determined at each time slot. In each time slot, deep reinforcement learning is used to obtain the optimal task offloading decision while satisfying the constraints. Simulation results show that compared with the existing algorithms, the proposed strategy can ensure the stability of the CAVs system and achieve the lowest system overhead.

Data recovery is a fundamental task for sparse network monitoring with a significant impact on many downstream tasks, such as congestion control, network capacity planning, and traffic engineering. To better capture the network dynamically and quickly respond to network failure, network monitoring systems take finer temporal granularity to collect data to form a real-time view of the network. Unfortunately, the current data recovery for network monitoring relies on matrix and tensor completion algorithms, which fail to satisfy the requirements of real-time recovery. To combat this problem, in this paper, we propose Real-TimeEvent-based Streaming NetworkMonitoring Data Recovery (EventMon) that achieves ultra-low latency data recovery in network measurement data streams. Specifically, we leverage a mixture of offline and online architecture, with the offline component learns to capture the historical spatial-temporal correlation while the online component is a novel streaming encoder that updates factor matrices incrementally. To enable training our event-level stream processing module, we devise a novel Stream2Batch algorithm to enable mini-batch style training and ensure the encoder generates the same results with one-by-one stream processing. We conduct extensive experiments on three network monitoring datasets and our evaluation and analysis of the experimental results demonstrate shows that the proposed method outperforms existing schemes in terms of accuracy, inference latency, and high processing throughput.

This study investigates the pattern of information interaction at the cortical level during deception, aiming to reveal the cognitive processes involved in the deception task. Our study involves the 64-channel EEG signals of 28 subjects (14 for innocent and 14 for guilty groups) acquired under the guilty knowledge test (GKT) lie-detection protocol. Additionally, we establish the functional connectivity network at the cortical level considering volume conduction effects, use a data-driven approach to select the regions of interest (ROIs) on the subject's cortex based on scalp electrical activity, and perform cortical current density estimation on 15 ROIs. The nonlinear dependence between the cortical waveforms of the ROIs is quantified based on mutual information, and a network of cortical mutual information connections is constructed in four frequency bands: delta, theta, alpha, and beta. The feature extraction and classification process are performed in each frequency band, and the mutual information connections statistically different between the innocent and guilty groups are first selected as features using statistical tests. Moreover, the optimal feature subset (OFS) is found by combining the SVM classifier and the wrapper feature selection strategy. Furthermore, the most important mutual information connections (MIMICs) per frequency band are obtained by refining the OFS according to the classification performance curve. The average test accuracies of MIMICs in the delta, theta, alpha, and beta bands reached 99.76%, 96.42%, 84.04%, and 97.61%, respectively. Finally, the physiological significance of each frequency sub-band and the physiological function of MIMICs are combined to explore the cognitive mechanism of lies and provide new evidence for cognitive activity in lying states.

Mobile Crowdsensing (MCS) has emerged as a promising sensing paradigm for accomplishing large-scale tasks by leveraging ubiquitously distributed mobile workers. Due to the variability in sensory data provided by different workers, identifying truth values from them has garnered wide attention. However, existing truth discovery schemes either offer limited privacy protection or incur high participation costs and lower data aggregation quality due to malicious workers. In this paper, we propose an Efficient and Trusted Bilateral Privacy-preserving Truth Discovery scheme (ETBP-TD) to obtain high-quality truth values while preventing privacy leakage from both workers and the data requester. Specifically, a matrix encryption-based protocol is introduced to the whole truth discovery process, which keeps locations and data related to tasks and workers secret from other entries. Additionally, trust-based worker recruitment and trust update mechanisms are first integrated within a privacy-preserving truth discovery scheme to enhance truth value accuracy and reduce unnecessary participation costs. Our theoretical analyses on the security and regret of ETBP-TD, along with extensive simulations on real-world datasets, demonstrate that ETBP-TD effectively preserves workers’ and tasks’ privacy while reducing the estimated error by up to 84.40% and participation cost by 54.72%.

Enhancing the communication rate and quality has become the primary goal for the development of next-generation mobile communication networks, and traditional techniques such as MIMO and increasing the transmit power of the base station (BS) have not achieved a leapfrog effect. The emergence of Intelligent Reflective Surfaces (IRS) provides more reliable technical support for providing high-energy and high-rate communications. However, IRS-aided joint optimization with communication rate and quality of service constraints is a non-convex non-smooth optimization problem, and the optimal global solution cannot be obtained due to its computational complexity. In this paper, we propose an intelligent Advanced Non-convex Non-smooth Scheme (ANNS) in IRS-aided Next Generation Mobile Communication Networks for making the transmission rate of mission communication and communication quality effective. To ensure that the inequality constraints in the joint optimization problem are not violated and the equation constraints are satisfied, a hybrid deep reinforcement learning and data-experience-driven constraint security layer network is proposed, which maps the constraint violations into the safe feasible domain by mapping the constraint variables into the constraint variables mapping method, and the convergence of the algorithm is theoretically demonstrated. Experimental results show that the proposed ANNS performs superior optimization compared to SAC, DDPG, and A2C for solving non-convex non-smooth problems. The proposed ANNS has the potential to be generalized to other mobile computing applications with non-convex non-smooth characteristics.

Mobile Crowdsensing (MCS) has emerged as a compelling paradigm for data sensing and collection, leveraging the widespread adoption of mobile devices and the active participation of numerous users. Despite its potential, MCS faces critical challenges, particularly in recruiting reliable workers and acquiring high-quality sensing data. Most existing approaches assume prior information on worker quality and are vulnerable to collusion attacks, especially having not comprehensively considered workers’ reliability and stability. To address these problems, we propose a Multi-Armed Bandit (MAB) based Worker Recruitment Scheme (MWRS) integrated with the Tripartite Stackelberg Game (TSG) for MCS. Specifically, a trust evaluation and truth inference mechanism is introduced to assess the trustworthiness of workers through active truth detection. To enhance recruitment quality, we employ a trust-aware worker selection mechanism that utilizes a modified Upper Confidence Bound (UCB) algorithm, achieving an optimal balance between exploration and exploitation. Furthermore, the interactions among participants are modeled using a TSG framework, which formulates their respective payoffs to determine optimal decision-making strategies, thus achieving mutually beneficial outcomes. Extensive evaluations on real-world datasets demonstrate that our proposed scheme improves total quality by up to 30.8% and reduces regret by up to 80.3% compared to existing methods.

In pursuit of an immersive virtual experience within the Cyber-Physical Metaverse Systems (CPMS), the construction of Avatars often requires a significant amount of real-world data. Mobile Crowd Sensing (MCS) has emerged as an efficient method for collecting data for CPMS. While progress has been made in protecting the privacy of workers, little attention has been given to safeguarding task privacy, potentially exposing the intentions of applications and posing risks to the development of the Metaverse. Additionally, existing privacy protection schemes hinder the exchange of information among entities, inadvertently compromising the quality of the collected data. To this end, we propose a Quality-aware and Obfuscation-based Task Privacy-Preserving (QOTPP) scheme, which protects task privacy and enhances data quality without third-party involvement. The QOTPP scheme initially employs the insight of “showing the fake, and hiding the real” by employing differential privacy techniques to create fake tasks and conceal genuine ones. Additionally, we introduce a two-tier truth discovery mechanism using Deep Matrix Factorization (DMF) to efficiently identify high-quality workers. Furthermore, we propose a Combinatorial Multi-Armed Bandit (CMAB)-based worker incentive and selection mechanism to improve the quality of data collection. Theoretical analysis confirms that our QOTPP scheme satisfies essential properties such as truthfulness, individual rationality, and ε-differential privacy. Extensive simulation experiments validate the state-of-the-art performance achieved by QOTPP.

Instruction reordering is an essential optimization technique used in both compilers and multi-core processors to enhance parallelism and resource utilization. Although the original intent of this technique is to benefit the program, some improper reordering can significantly impact the program correctness, which we call instruction reordering vulnerability (IRV). However, existing methods detect IRV by defining CPU instruction reordering rules to schedule execution paths while neglecting compiler reordering, and thus generate false positives that require manual filtering and resulting in inefficiency. To bridge this gap, in this paper, we propose the IRV detection method, , which analyzes IRV characteristics and extracts vulnerability patterns, integrating program dependency analysis for compiler reordering and memory model constraints for CPU reordering. Specifically, we use static analysis based on specific patterns to narrow the analysis scope, and adopt log-based dynamic analysis to confirm vulnerability by checking the log constraints. We built the IRV benchmark to compare IRHunter with five state-of-the-art tools (i.e., GENMC, Nidhugg, CBMC, SHB, BiRD). IRHunter detected all 19 errors, doubling the best model checking tools' performance, with half the false positive rate of leading data race detectors. It was 10× faster on small programs and outperformed data race detectors on large programs.

In modern parallel system architectures, prefetchers are essential to mitigating the performance challenges posed by long memory access latencies. These architectures rely heavily on efficient memory access patterns to maximize system throughput and resource utilization. Prefetch aggressiveness is a central parameter in managing these access patterns; although increased prefetch aggressiveness can enhance performance for certain applications, it often risks causing cache pollution and bandwidth contention, leading to significant performance degradation in other workloads. While many existing prefetchers rely on static or simple built-in aggressiveness controllers, a more flexible, adaptive approach based on system-level feedback is essential to achieving optimal performance across parallel computing environments. In this paper, we introduce an Adaptive Prefetch Aggressiveness Control (APAC) framework that leverages Reinforcement Learning (RL) to dynamically manage prefetch aggressiveness in parallel system architectures. The APAC controller operates as an RL agent, which optimizes prefetch aggressiveness by dynamically responding to system feedback on prefetch accuracy, timeliness, and cache pollution. The agent receives a reward signal that reflects the impact of each adjustment on both performance and memory bandwidth, learning to adapt its control strategy based on workload characteristics. This data-driven adaptability makes APAC particularly well-suited for parallel architectures, where efficient resource management across cores is essential to scaling system performance. Our evaluation with the ChampSim simulator demonstrates that APAC effectively adapts to diverse workloads and system configurations, achieving performance gains of 6.73$\%$in multi-core systems compared to traditional Feedback Directed Prefetching (FDP). By improving memory bandwidth utilization, reducing cache pollution, and minimizing inter-core interference, APAC significantly enhances prefetching performance in multi-core processors. These results underscore APAC’s potential as a robust solution for performance optimization in parallel system architectures, where efficient resource management is paramount for scaling modern processing environments.

On event-based social platforms such as Meetup and Douban, online groups serve as more than virtual communities for users to share experiences, they also provide an essential pathway for users to discover and participate in offline events. As the number of groups grows, it imposes the need of the study of online group recommendation. Despite there being many existing approaches to solve this problem, they all ignore the phenomenon that the groups that users participate in often contain a number of similar users. This phenomenon implies that similar users play a crucial role in identifying the groups that users are likely to join. In order to exploit similar users to improve the recommendation performance, we propose an effective multi-scale contrastive learning system for online Group Recommendation services, which is with a two-Tower model in event-based social networks (Tower4GR). Specifically, we first adopt the two-tower model to capture the interactive signals within the sequences and groups. We then incorporate the features of similar users into the sequence encoder, and aggregate the relevant users’ features into the group encoder, through which the preferred groups of similar users are more likely to be discovered by the target user. Finally, we propose an effective multi-scale contrastive learning framework for the two-tower architecture. It derives self-supervision signals from both same-scale data and cross-scale data, thereby extracting more meaningful data patterns. Moreover, the framework strengthens the cooperative associations between two towers. Extensive experiments on three real-world datasets from Meetup demonstrate the superiority of our proposed model over existing state-of-the-art models.

As a promising distributed computing paradigm, federated learning (FL) has been widely applied to industrial manufacturing. To enhance the safety of FL system against growing privacy attacks, secret sharing techniques are introduced to realize secure model aggregation. However, the huge extra transmission in vanilla secret sharing aggravates the inherent communication bottleneck of FL system in industrial cellular networks. To reduce transmission overhead, this article proposes a lightweight FL system based on rotated partial secret sharing (FedRPS), which divides industrial terminals into several subgroups and allows them to alternately share partial local secrets. Our FedRPS ensures the global model to converge at a high rate with incomplete local parameters. Moreover, we theoretically analyze the convergence error of FedRPS, and discuss the impact of unbalanced data in subgroups on model convergence. To further improve global model performance under non-IID settings, we design a multiparty greedy grouping (MPGG) strategy to balance the training data in each subgroup. Finally, we conduct experiments on several datasets, which demonstrate our FedRPS trades for significant improvement on communication with only small loss of model accuracy. In addition, we also simulate the privacy attack scenarios to verify the better defense of FedRPS compared with previous complete secret sharing.

Deep reinforcement learning, with its extensive applications and remarkable performance, is emerging as a pivotal technology garnering researchers’ attention. During the training process, there are frequent interaction and data exchange between agents and the environment, and the interaction information during training is closely tied to the training environment. Consequently, this process introduces a high risk of environmental privacy leakage. Malicious third parties may potentially steal state transition matrix or environmental information about the application domain of agent training, resulting in the compromise of user privacy. To address this issue, we propose novel differentially private value-based and policy-based deep reinforcement learning algorithms. Our methods have an advantage of being adaptable to various environmental privacy concerns. We also evaluate them in a customized experimental environment. Comparative experiments are conducted between the original and differentially private versions of the algorithms. The results indicate that our proposed approach can provide differential privacy protection to environmental information with minimal impact on algorithm performance, ultimately achieving a good balance between privacy and utility.

Federated learning (FL), as an effective method to solve the problem of “data island”, has become one of the hot and widespread concern topics in recent years. However, with the using of FL technology in the practical applications, an increasing number of FL tasks make the training management be more complex and the trade-off of multi-task becomes difficult. To overcome this weakness, this work proposes a privacy-preserving FL framework with multi-tasks using partitioned blockchain, which can run several different FL tasks by multiple requesters. First, a temporary committee is formed for an FL task to facilitating visualization, organization and management of security aggregation. Second, the proposed framework combines Paillier homomorphic encryption with Pearson correlation coefficient to protect users' privacy and ensure the accuracy of global model. Finally, a new blockchain-based reward method is presented to inspire participants to share their valuable data. The experimental results show that the global model accuracy of our proposed framework is able to reach 98.43%. Obviously, the proposed framework is more suitable for practical application environment, especially in industrial application field.

Relational triple extraction is a critical step in knowledge graph construction. Compared to pipeline-based extraction, joint extraction is gaining more attention because it can better utilize entity and relation information without causing error propagation issues. Yet, the challenge with joint extraction lies in handling overlapping triples. Existing approaches adopt sequential steps or multiple modules, which often accumulate errors and interfere with redundant data. In this study, we propose an innovative joint extraction model with cross-attention mechanism and global pointers with context shield window. Specifically, our methodology begins by inputting text data into a pre-trained RoBERTa model to generate word vector representations. Subsequently, these embeddings are passed through a modified cross-attention layer along with entity type embeddings to address missing entity type information. Next, we employ the global pointer to transform the extraction problem into a quintuple extraction problem, which skillfully solves the issue of overlapping triples. It is worth mentioning that we design a context shield window on the global pointer, which facilitates the identification of correct entities within a limited range during the entity extraction process. Finally, the capability of our model against malicious samples is improved by adding adversarial training during the training process. Demonstrating superiority over mainstream models, our approach achieves impressive results on three publicly available datasets.

Abstract Delta compression, as a complementary technique for data deduplication, has gained widespread attention in network storage systems. It can eliminate redundant data between non‐duplicate but similar chunks that cannot be identified by data deduplication. The network transmission overhead between servers and clients can be greatly reduced by using data deduplication and delta compression techniques. Resemblance detection is a technique that identifies similar chunks for post‐deduplication delta compression in network storage systems. The existing resemblance detection approaches fail to detect similar chunks with arbitrary similarity by setting a similarity threshold, which can be suboptimal. In this paper, the authors propose Chunk2vec , a resemblance detection scheme for delta compression that utilizes deep learning techniques and Approximate Nearest Neighbour Search technique to detect similar chunks with any given similarity range. Chunk2vec uses a deep neural network, Sentence‐BERT, to extract an approximate feature vector for each chunk while preserving its similarity with other chunks. The experimental results on five real‐world datasets indicate that Chunk2vec improves the accuracy of resemblance detection for delta compression and achieves higher compression ratio than the state‐of‐the‐art resemblance detection technique.

The Energy Internet (EI) aims to build a sustainable energy ecosystem by connecting diverse energy sources and prosumers. Mobile Crowd Sensing (MCS) enables efficient data collection for monitoring and aggregation from distributed devices. Given the complex behavior of workers driven by self-interest, recruiting trustworthy, high-quality, and inexpensive workers remains a significant challenge in research and practice. Previous studies often assume that worker characteristics are known or can be obtained after data collection. However, evaluating worker qualities is quite challenging in the face of multi-source data and complex workers. To address this, we propose a Completion Rate Learning based Multi-Armed Bandit reverse Auction (CRL-MABA) scheme for identifying and selecting high-quality workers in MCS. Our CRL-MABA scheme first proposes a Spatial-Temporal Upper Confidence Bound (STUCB) method to recruit workers, considering both the quality of workers for exploitation and the spatiotemporal features for exploration. In addition, the Dual-Stage Data Estimation Mechanism (DSDEM) and Long-Term and Short-Term Memory Learning (LTSTML) are designed to identify workers accurately and efficiently. Importantly, our proposed scheme avoids the impractical assumptions in previous works while satisfying important criteria such as truthfulness, individual rationality, and computational efficiency. The effectiveness of our scheme is demonstrated through extensive experimental results, which show its superiority over existing strategies.

Sparse mobile crowdsensing (SMCS) is a prospective solution for large-scale data sensing through mobile devices of Internet of Things (IoT) systems where IoT systems cannot obtain the sensing data of the area under extreme environments. The unsensed area data can be obtained by the data inference algorithm trained by the sensed data of recruited workers. However, recruited workers may upload false data in exchange for payment, and the platform is unable to distinguish between true and false data. In this article, our goal is to maximize the SMCS platform’s total profit, where the platform cannot verify the authenticity of the sensed data, and the requester’s payment is based on the sensing task’s data quality. To meet the objective, we propose the area-constrained truthful worker recruitment-based sensing map recovery (ATWR-SMR) scheme, which includes the area constraint, the area-constrained truthful worker recruitment, and the sensing map recovery. 1) The area constraint establishes the importance of areas by history data differences in the sliding window. 2) The truthful worker recruitment identifies trustworthy workers by the truthful upper confidence bound algorithm and recruits low-cost trustworthy workers to sense high-importance areas. 3) The sensing map recovery infers the unsensed data by the deep matrix factorization algorithm trained by the history truthful data set. Finally, we verify the effectiveness of the ATWR-SMR scheme in improving the total profit of the platform through extensive comparison experiments based on the China air quality data set.

Mobile crowdsensing (MCS) is a crucial component in the Industrial Internet of Things (IIoT), mainly due to its role in collecting data and enhancing applications. Nonetheless, it faces challenges in maintaining data quality and cost efficiency. Low-quality workers and their deceptive data bids undermine the trustworthiness of MCS data collection. Despite this, prior studies have not sufficiently scrutinized the validity of data and bids. These issues could render MCS services ineffective and unaddressed, hindering IIoT development. In response, we propose an Intelligent Data and Bid dual truth discovery (DTD) scheme. Initially, the scheme applies a detection algorithm to identify the features of ground truth data sensed by unmanned aerial vehicles. The approach uses features to evaluate the data trust from unknown workers and filter out low-quality workers. Subsequently, the scheme evaluates the bid trust from reliable workers by calculating their bid confidence intervals. Upon completing this assessment, the scheme identifies high-quality workers. This process hinges on a contribution value incorporating both data and bid trust. Ultimately, the scheme assigns these high-quality workers to sense the subsequent tasks. This approach significantly improves the data quality and reduces costs for MCS in IIoT. The experimental results demonstrated that the DTD scheme outperforms the existing main schemes in terms of sensitivity (improvement 44%), specificity (improvement 5%), accuracy (improvement 18%), and F1-score (improvement 47%). It also reduced data bias by 24 percentage points and reduced costs by 38 percentage points.

Mobile Edge Computing (MEC) offers low-latency and flexible computing services for mobile devices (MDs) in industrial Internet-of-Things (IIoT). Edge servers (ESs) in general belong to different subjects and will focus on their own interests. They may be reluctant to provide computation resources to MDs without appropriate incentives. Meanwhile, there is a trust issue in trading computation resources between ESs and MDs. Due to the complex interaction between ESs and MDs, it is a challenge for ESs to gain satisfactory revenue through reasonable resource pricing strategies, and for MDs to improve their quality of experience (QoE) through efficient computation offloading strategies. This paper proposes a Stackelberg Game-based Computation offloading and resource pricing Scheme (SGCS) in blockchain-enable MEC for IIoT. Firstly, a blockchain-based resource trading framework is designed to enable trusted resource transactions. Secondly, a multi-leader multi-follower Stackelberg game is presented to analyse the complex interactions in the multi-ES and multi-MD environments. Finally, the Iterative Proximal Algorithm (IPA) for MDs’ offloading decision and the Sub-gradient based Iterative Pricing Algorithm (SIPA) for ESs’ pricing decision are proposed respectively, which guarantees that the game converges to a Stackelberg Equilibrium (SE). Compared with MADDPG, GA and PSO-GA (i.e., benchmark strategies), the average disutility of MDs with our proposed scheme is reduced by 6.95%-13.07%, 3.09%-20.41%, and 2.36-17.22%, respectively. Moreover, with the increase of the number of MDs, our proposed scheme has better robustness, which can effectively deal with large-scale scenarios.

Data fusion in the Internet of Things (IoT) is based on the intersection and union problems of sets without complete set restrictions to achieve accurate and reliable location services. However, protecting users’ privacy in IoT is challenging, for which a solution is proposed herein in conjunction with secure multi-party computation (MPC). Using the proposed solutions, users’ privacy can be ensured while achieving accurate and reliable location positioning. Current privacy-preserving schemes used for data fusion require a set’s elements belonging to an appropriate complete set, and the range of elements used by such schemes is usually integers. Moreover, a unified range of all elements in a set cannot be determined in real-time for practical scenarios. Limiting the range of elements to integers is also impossible, because the type of data collected by IoT devices is uncertain. To address these issues, new encoding and transformation methods are proposed herein to map a set of rational numbers to the Cartesian coordinate system. The NTRU encryption scheme is used to design the intersection and union MPC protocols of rational numbers’ sets without any complete set restriction under the semi-honest model. Then, to prevent possible malicious behaviors in the semi-honest model protocols, MPC protocols for the intersection and union of rational numbers’ sets are designed without any complete set. The security of the protocols is demonstrated by applying the real/ideal model paradigm. The comparison of the proposed protocols with other schemes shows that our protocols have higher computational efficiency.

Mobile crowd sensing (MCS) is a cloud–edge–terminal collaboration model that relies on edge terminal devices, or “workers,” to sense data and build applications for cloud-hosted platforms. However, to ensure high-quality application development, recruiting truthful workers in the edge network is crucial. With the emergence of artificial intelligence (AI), the Internet of Things (IoT) is entering a new era, known as AI of Things (AIoT). This article proposes an AI-enabled MCS system, which includes MLM-WR, a cloud–edge–terminal collaboration data collection scheme for AIoT. MLM-WR leverages swarm intelligence to match truthful workers with sensing tasks, enabling efficient and effective data collection for AIoT applications. The matching theory is applied from two perspectives: 1) truthful workers discovery and 2) sensing difference discovery. To identify truthful workers, we adjust their credibility based on the deviation of their sensing data with ground truth data (GTD) obtained through collaboration with the unmanned aerial vehicle (UAV). In the sensing difference discovery, we obtain workers’ sensing attribute reliability by calculating attribute data errors and incorporate absolute and relative sensing location preferences to determine workers’ sensing quality at different locations. Additionally, MLM-WR employs the particle swarm optimization (PSO) algorithm to assign workers while considering sensing attribute and location reliability and recruitment cost, thus addressing the tradeoff between recruitment cost and data quality. The effectiveness of our approach is demonstrated through extensive evaluations, where MLM-WR outperformed the state-of-the-art approaches.

Mobile crowdsourcing (MCS) is an emerging paradigm that harnesses the collective power of the crowd to tackle large-scale tasks. To ensure the high-quality worker selection, various combinatorial multiarmed bandit (CMAB)-based schemes have been proposed. However, previous schemes often overlook critical issues. First, the post-unknown worker recruitment (PUWR) problem emerges when the quality of a worker remains unknown despite reported worker data. Second, the presence of Sybil Requesters is often neglected, who manipulate ratings to deceive workers for malicious purposes. To tackle these challenges, we present an innovative scheme called bi-directional trust verification-based CMAB (BTV-CMAB). First, we propose a truth quality discovery approach that effectively addresses the PUWR problem by estimating worker quality. Additionally, we employ a BTV mechanism to assess the Degree of Trust (DoT) of requesters and the reputation of workers. To select top-notch workers for MCS, we combine the worker quality and reputation into an upper confidence bound (UCB) index. The effectiveness of the BTV-CMAB scheme is supported by theoretical proof, which demonstrates its ability to ensure truthfulness and individual rationality. Furthermore, experimental results reveal promising improvements achieved by our scheme, including a 17.44%, increase in the platform’s revenue and a significant decrease in regret of up to 88.26%. To the best of our knowledge, this study is the first to propose utilizing a BTV mechanism to effectively address the PUWR problem and counter the threat of Sybil attacks in the CMAB-based worker recruitment process.

Deep neural networks (DNNs) have shown remarkable performance in the super-resolution (SR) task, which can upscale low-resolution images to satisfy application demands on image quality. However, the high computational intensity of DNN models poses a challenge to executing SR tasks on resource-constrained edge platforms. To leverage heterogeneous computational resources (e.g., CPU, GPU, and NPU) to speed up image reconstruction through concurrent inference, we propose a novel framework, called ESHP, for Efficient Super-resolution on edge platforms with Heterogeneous Processors. Our proposed ESHP framework boasts several advantageous characteristics: 1) it substantially speeds up SR processing over the existing approaches by leveraging all available heterogeneous hardware; 2) it uses deep reinforcement learning (DRL) to enable adaptive and optimal scheduling based on runtime states; 3) it strikes a balance between SR performance and computational cost during inference; and 4) it does not modify the original architecture of given SR model. We have conducted extensive experiments on typical edge platforms with popular SR models and resolution datasets of different scales, which verify the effectiveness and the versatility of our ESHP against other commonly-used baselines.

With the explosive growth in video streaming comes a rising demand for efficient and scalable video understanding. State-of-the-art video recognition approaches based on Convolutional Neural Network (CNN) have shown promising performance by adopting 2D or 3D CNN architectures. However, the large data volumes, high resource demands, and strict latency requirements have hindered the wide application of these solutions on resource-constrained Internet-of-Things (IoT) and edge devices. To address this issue, we propose a novel framework called TLEE that enables the input samples the abilities of both Temporal-wise and Layer-wise Early Exiting on 2D CNN backbones for efficient video recognition. TLEE consists of three types of modules: gating module, branch module, and feature reuse module. The gating module determines for an input video from which frame of this video to exit the per-frame computation, while the branch module determines for an input frame from which layer of the CNN backbone to exit the per-layer computation. Besides, based on the accumulated features of frame sequences from exit branches, the feature reuse module generates effective video representations to enable more efficient predictions. Extensive experiments on benchmark datasets demonstrate that the proposed TLEE can significantly outperform the state-of-the-art approaches in terms of computational cost and inference latency, while maintaining competitive recognition accuracy. In addition, we verify the superiority of TLEE on the typical edge device NVIDIA Jetson Nano.

The data privacy and security of Internet of Things (IoT) applications are progressively crucial and cryptography is frequently used to ensure security. Cryptographic hardware implementation is commonly adopted for high throughput and low-computational resources. The crypto circuits have to be strictly tested to guarantee the correctness of data. Scan-based design-for-testability (DFT) widely employed in the chip industry improves the controllability and observability of circuits. However, it facilitates illegal users to steal the internal data for cracking cryptographic keys. Many researchers have recently suggested effective defense technologies against scan-based attacks, but each technology has its own negative aspects. In this article, we propose an effective DFT authorization framework (DAF) based on obfuscation mechanisms. The authentication module is embedded to verify users’ keys, and it can empower each chip distinctive key to minimize the loss of key divulgence. If the test authorization key is accurate, the typical scan operation can be carried out. Conversely, if the key is inaccurate, the inserted obfuscation module comes into play to scramble the scan data. Additionally, a random number generation circuit is used to increase the uncertainty of data obfuscation, effectively preventing attackers from inferring sensitive information. Simulation results show that this design has a low overhead, and theoretical analysis demonstrates that the design has high security with no impact on the testability of the chip.

Emerging crowdsensing paradigm enables a large number of sensing applications, where much attention is drawn to the fundamental problems for maximizing the system utility and improving the data quality. However, the existing works only consider one of the above issues. In fact, the truthful value of data lies in its veracity. The false data submitted by malicious workers brings no benefits and even causes losses to the system. Therefore, the utility calculations without considering the veracity of data in the previous incentive mechanisms are inaccurate. While current truth discovery (TD) methods cost a lot and cannot improve the quality of data at source. To overcome these problems, we design a truthful value discovery and reverse auction (TVD-RA) framework. First, a TD and trust inspection approach are proposed to find the truth and get the trust degree of workers. This combination can capture the truthful value of data and identify malicious workers. In addition, an incentive mechanism based on reverse auction is proposed. Discovered malicious workers are excluded when selecting task performers, thereby improving the quality of data. In addition, tasks are redundantly assigned in the initial period, creating opportunities for trust inspections. Although this entails losses, the influence of malicious workers can be ruled out. Therefore, we gain long-term utility at the expense of current benefits. Theoretical analysis demonstrates that the proposed incentive mechanism is incentive compatible and individual rational. We finally carry out extensive evaluations, where results demonstrate the superiority of our mechanism over the state-of-the-art approaches.

Federated learning (FL) emerges as a promising paradigm, relentlessly pursuing excellence in efficiency, privacy preservation, and security—the holy trinity that underpins the fundamental philosophy and practical implementation of FL. However, previous scholarly works have predominantly focused on the privacy protection and secure aggregation in the realm of FL. Undoubtedly, efficiency still plays a pivotal role in determining FL’s viability in real-world applications. To improve efficiency while ensuring privacy protection and secure aggregation, this work proposes a verifiable privacy-preserving federated learning framework tailored for practical applications. Firstly, a novel aggregation rule, constrained M maximum security aggregation, forces the server to securely aggregate the local gradients from M users without relying on any auxiliary servers, thereby considerably decreasing the communication overhead. Secondly, regardless of the dimension of the aggregated gradient being verified, our scheme performs a single verification per user per round. Through security analysis, our scheme could guarantee some given security requirements. Besides, extensive experiments show that under the proportion that the gradient dimension$(d)$to the number of users$(n)$is 1:2 and 2:1, ours is$\times 1.3$and$\times 1$faster for total runtime and is$\times 3$and$\times 7$lower for total communication cost compared with a state-of-the-art framework, respectively. Therefore, our FL framework is more suitable for practical application in real life.

As Industrial Internet of Things (IIoT) systems have superior distributed characteristics, federated learning (FL) has gradually become a mainstream distributed computing paradigm in recent years, promoting its intelligent development. A key factor of FL is the model accuracy, which relies on the truth of the participating industrial edge device (IED)’s parameter. However, some untrusted IEDs can contaminate models by fabricating false FL parameters, thereby deceiving rewards, known as model poison attack (MPA). In this article, a truth-discovery-based multidimensional bidding (TD-MDB) strategy is proposed to obtain truthful parameters at a low cost for FL. In the TD-MDB strategy, parameter server (PS) will randomly assign the lightweight trust evaluation model to test the trust of each IED according to the accuracy of IED’s calculation. Then, in order to resist MPA and inference attack, we use an improved clustering algorithm to divide the trusted IEDs into different clusters based on their different trust and stability. Finally, a device selection strategy is proposed to select cost-effective IEDs for model training based on Asynchronous Advantage Actor–Critic (A3C) algorithm. In the TD-MDB strategy, the IEDs that provide more resources, high trust, and low bid will be selected to train the model and maximize the real revenue of the system with high accuracy. We finally carry out extensive evaluations, where results demonstrate that the TD-MDB strategy can effectively provide an efficient offloading strategy, resist MPA and ensure that truth computing for FL, which is better than the state-of-the-art strategies in model accuracy, system revenue, and other performance indicators.

Mobile Crowd Sensing (MCS), as a promising sensing paradigm, significantly relies on wireless communication networks and widely distributed mobile workers to capture data from the surroundings. However, the positioning-dependent nature of most MCS tasks often requires workers to embed their positionings in reports, which may result in privacy leakage and a decline in their participation enthusiasm. Considering workers’ diverse perceptions of positioning privacy, in this paper we propose the Lightweight Positioning-Privacy Protection Scheme with Double-Layer Incentives (L3P-DLI) to meet their personalized privacy requirements in an efficient and low-cost way while stimulating their participation. To the best of our knowledge, this scheme is the first attempt to employ proxy forwarding to protect workers’ sensitive positionings while ensuring high-quality sensing results. Moreover, our double-layer incentivizing mechanism is elaborately designed to motivate workers to actively participate or serve as proxies. Specifically, the bidirectional auction between data collectors and proxies can safeguard the security of data collectors, and compensate for the potential privacy leakage cost of proxies helping to forward data. Additionally, the reverse auction mechanism enables the platform to reward recruited workers to compensate for their various costs. Extensive experiments conducted on real-world datasets validate that L3P-DLI effectively preserves workers’ positioning privacy while maximizing their income to encourage participation.

Human-centric communication metaverse relies on the convergent integration of multiple existing technologies such as 5G and beyond networks, virtual reality, augmented reality, and digital twins, and thus their security vulnerabilities and vulnerability to interference attacks may also be inherited by the metaverse. In particular, existing security policies may be inefficient for communication interference problems encountered in multi-device collaborative computing in a metaverse 5G environment and lack adaptability to metaverse applications. In this paper, we propose a novel intelligent game anti-interference collaborative computing model that accurately describes the interference relationships among source devices, cooperating devices, and interferers in metaverse collaborative computing. We model the offensive and defensive confrontation between multiple metaverse devices as a Stackelberg game, where the source device is the leader, the collaborative computing device acts as the sub-leader, adjusts its antijamming strategy according to the source device’s strategy to improve the source device’s communication anti-jamming performance, and the jammer acts as the follower. We design an intelligent Stackelberg Game-theoretic Policy-based Learning (SGPL) algorithm for jamming resistance in metaverses over 5G and Beyond Networks, where the leaders (co-computing devices) update their training parameters using the total derivatives of the objective function, while the followers (i.e., jammers) update their training parameters using an independent gradient dynamics strategy. Loops are eased and convergence is accelerated by introducing differential dynamics into the leader training network to reflect the interaction structure of the critic and actor network layers. Finally, numerical results demonstrate the effectiveness of the proposed SGPL algorithm in metaverse anti-jamming countermeasures. The proposed SGPL algorithm has the potential to be generalized to other metaverse applications with multi-user attack and defense characteristics.

Stock forecasting research, which aims to predict the future price movement of stocks, has been the focus of investors and scholars. This is important for practical applications related to human-centric computing and information sciences. Previous research has generally only considered market information other than the relationship between stocks, and it is challenging to learn a better representation of stock characteristics by considering the relationship between stocks. In the existing methods of combining market information with stock relationship modeling, most of them use predefined industry relationships to construct stock relationship diagrams, which inevitably ignores the potential interactions between stocks, especially the hidden relationships between stock groups. To this end, a new dual-graph attention model (MF-DAT) based on multisource information fusion is designed. Specifically, first, multiple features are fused by the LMF module, then the long-term and short-term state characteristics of stocks are learned through the first layer of the graph attention layer, and finally the node representation of the stock relationship network constructed by the mining stock cluster structure through community detection is updated. Our model takes into account both stock time-series information and potential relationships between stocks. Experiments on the S &P 500 and NASDAQ datasets show that our MF-DAT has better performance than the 8 SOTA methods that are now more popular.

The rapid development of cloud computing probably benefits many of us while the privacy risks brought by semi-honest cloud servers have aroused the attention of more and more people and legislatures. In the last two decades, plenty of works seek to outsource various specific tasks to servers while ensuring the security of private data. The tasks to be outsourced are countless; however, the computations involved are similar. In this article, we construct a series of novel protocols that support the secure computation of various functions on numbers (e.g., the basic elementary functions) and matrices (e.g., the calculation of eigenvectors and eigenvalues) on arbitrary$n\geq 2$servers. All protocols only require constant rounds of interactions and achieve low computation complexity. Moreover, the proposed$n$-party protocols ensure the security of private data even though$n-1$servers collude. The convolutional neural network models are utilized as the case studies to verify the protocols. The theoretical analysis and experimental results demonstrate the correctness, efficiency, and security of the proposed protocols.

As the number of smart cars grows rapidly, vehicular crowdsensing (VCS) is gradually becoming popular. In a VCS infrastructure, sensing devices and computing units hold on smart cars as well as cloud servers form an IoT-edge-cloud continuum to perform real-time sensing tasks. In order to encourage the smart cars to participate in the real-time VCS process, blockchain technology can be combined with VCS to provide an automated incentive for VCS data trading without relying on trusted third parties. However, directly using blockchain to enforce the VCS data trading process incurs expensive service fees and participants still can conduct various misbehavior. In this article, we propose a secure blockchain-based data trading system for VCS named BTT system to address the above issues. In particular, we first integrate the blockchain-based data trading process with a lightweight privacy-preserving truth discovery algorithm to ensure the accuracy of sensing data while preserving data privacy. We then propose a gas-aware optimization mechanism to minimize the gas consumption of the data trading process. Finally, we carefully design a distributed judgment mechanism to regulate all participants to behave correctly in the data trading process. To demonstrate the practicability of our design, we implement a prototype of the BTT system deployed on an Ethereum test network and conduct extensive simulations.

The emergency of intelligent transportation system provides new methods to monitor and alleviate traffic problems by employing many Internet of Things (IoT) devices, namely workers, to collect data. However, there are many malicious workers (or spammers) negatively impacting the system, so it is urgent to propose an effective true data evaluation method. In this paper, a UAV-assisted Intelligent True Data Evaluation (UITDE) method has been proposed to solve this problem effectively. UITDE has two main contributions. First, we abandon traditional algorithms that only use workers’ data to obtain estimated truths and compute their trusts directly. In contrast, we employ Unmanned Aerial Vehicles (UAVs) to collect data as ground truths to test and calculate workers’ trusts. Second, there are some unmalicious workers who collect inaccurate data due to their aged sensing equipment, extreme weather, or other objective reasons, so we propose a data rectification algorithm to rectify their data. The results show that our proposed method outperforms the compared methods, in terms of workers’ trust evaluation, utility, and accuracy.

Abstract A new trend in long-range biometrics, gait recognition, is finding application in a number of different fields including video surveillance. Recently, with the increase in robustness of the pose estimator and the presence of various unpredictable factors in realistic gait recognition, skeleton-based methods with higher robustness have emerged to better meet the challenging gait recognition needs. However, existing approaches primarily focus on extracting global skeletal features, neglecting the intricate motion information of local body parts and overlooking inter-limb relationships. Our solution to these challenges is the dynamic local fusion network (GaitDLF), a novel gait neural network for complex environments that includes a detail-aware stream in addition to the previous direct extraction of global skeleton features, which provides an enhanced representation of gait features. To extract discriminative local motion information, we introduce predefined body part assignments for each joint in the skeletal structure. By segmenting and mapping the overall skeleton based on these limb site divisions, limb-level motion features can be obtained. In addition, we will dynamically fuse the motion features from different limbs and enhance the motion feature representation of each limb by global context information and local context information of the limb-level motion features. The ability to extract gait features between individuals can be improved by aggregating local motion features from different body parts. Based on experiments on CASIA-B, Gait3D, and GREW, we show that our model extracts more comprehensive gait features than the state-of-the-art skeleton-based method, demonstrating that our method is better suited to detecting gait in complex environments in the wild than the appearance-based method.

Deep learning technology has grown significantly in new application scenarios such as smart cities and driverless vehicles, but its deployment needs to consume a lot of resources. It is usually difficult to execute inference task solely on resource-constrained Intelligent Internet-of-Things (IoT) devices to meet strictly service delay requirements. CNN-based inference task is usually offloaded to the edge server or cloud. However, it may lead to unstable performance and privacy leaks. To address the above challenges, this article aims to design a low latency distributed inference framework, EdgeCI, which assigns inference tasks to locally idle, connected, and resource-constrained IoT device cluster networks. EdgeCI exploits two key optimization knobs, including: (1) Auction-based Workload Assignment Scheme (AWAS), which achieves the workload balance by assigning each workload partition to the more matching IoT device; (2) Fused-Layer parallelization strategy based on non-recursive Dynamic Programming (DPFL), which is aimed at further minimizing the inference time. We have implemented EdgeCI based on PyTorch and evaluated its performance with VGG-16 and ResNet-34 image recognition models. The experimental results prove that our proposed AWAS and DPFL outperform the typical state-of-the-art solutions. When they are well combined, EdgeCI can improve inference speed by 34.72% to 43.52%. EdgeCI outperforms the state-of-the art approaches on our edge cluster.

In edge computing, Internet of Things devices with weak computing power offload tasks to nearby edge servers for execution, so the task completion time can be reduced and delay-sensitive tasks can be facilitated. However, if the task is offloaded to malicious edge servers, then the system will suffer losses. Therefore, it is significant to identify the trusted edge servers and offload tasks to trusted edge servers, which can improve the performance of edge computing. However, it is still challenging. In this article, a trust Active Detecting-based Task Offloading (ADTO) scheme is proposed to maximize revenue in edge computing. The main innovation points of our work are as follows: (a) The ADTO scheme innovatively proposes a method to actively get trust by trust detection. This method offloads microtasks to edge servers whose trust needs to be identified, and then quickly identifies the trust of edge servers according to the completion of tasks by edge servers. Based on the identification of the trust, tasks can be offloaded to trusted edge servers, to improve the success rate of tasks. (b) Although the trust of edge servers can be identified by our detection, it needs to pay a price. Therefore, to maximize system revenue, searching the most suitable number of trusted edge servers for various conditions is transformed into an optimization problem. Finally, theoretical and experimental analysis shows the effectiveness of the proposed strategy, which can effectively identify the trusted and untrusted edge servers. The task offloading strategy based on trust detection proposed in this article greatly improves the success rate of tasks, compared with the strategy without trust detection, the task success rate is increased by 40.27%, and there is a significant increase in revenue, which fully demonstrates the effectiveness of the strategy.

3D perception of depth and ego-motion is of vital importance in intelligent agent and Human Computer Interaction (HCI) tasks, such as robotics and autonomous driving. There are different kinds of sensors that can directly obtain 3D depth information. However, the commonly used Lidar sensor is expensive, and the effective range of RGB-D cameras is limited. In the field of computer vision, researchers have done a lot of work on 3D perception. While traditional geometric algorithms require a lot of manual features for depth estimation, Deep Learning methods have achieved great success in this field. In this work, we proposed a novel self-supervised method based on Vision Transformer (ViT) with Convolutional Neural Network (CNN) architecture, which is referred to as ViT-Depth . The image reconstruction losses computed by the estimated depth and motion between adjacent frames are treated as supervision signal to establish a self-supervised learning pipeline. This is an effective solution for tasks that need accurate and low-cost 3D perception, such as autonomous driving, robotic navigation, 3D reconstruction, and so on. Our method could leverage both the ability of CNN and Transformer to extract deep features and capture global contextual information. In addition, we propose a cross-frame loss that could constrain photometric error and scale consistency among multi-frames, which lead the training process to be more stable and improve the performance. Extensive experimental results on autonomous driving dataset demonstrate the proposed approach is competitive with the state-of-the-art depth and motion estimation methods.

As a new type of digital living space that blends virtual and reality, Metaverse combines many emerging technologies. It provides an immersive experience based on VR technology and stores and protects users’ digital content and digital assets through blockchain technology. However, different virtual environments are often highly heterogeneous in terms of underlying architecture and software implementation technology, which leads to many challenges in scalability and interoperability for blockchains serving the Metaverse. Cross-chain technology is an essential technology to realize the scalability and interoperability of blockchain. However, the current cross-chain technologies all have their own merits and demerits, and there is no cross-chain solution that can be fully applied to any scenario. To this end, in the blockchain-based Metaverse, this article proposes a cross-chain transaction scheme based on improved hash timelock, HCNCT. By combining the notary mechanism, this scheme uses a group of notaries to supervise and participate in cross-chain transactions, effectively solving the problem that malicious users create a large number of time-out transactions to block the transaction channel, which exists in the traditional hash timelock method. Besides, this article uses the verifiable secret sharing method in the notary group, which can effectively prevent the centralization problem of the notary mechanism. Moreover, this article discusses the process of key processing, cross-chain transaction and transaction verification of the scheme, and designs the user credibility evaluation mechanism, which can effectively reduce the occurrence of malicious default of users. Compared with existing solutions, our solution has the advantage of effectively addressing time-out transaction attacks and centralization issues while guaranteeing security. The experiments also verify the effectiveness of the proposed scheme.

The popularity of cyber-physical systems is fueling the rapid growth of location-based services. This poses the risk of location privacy disclosure. Effective privacy preservation is foremost for various mobile applications. Recently, geo-indistinguishability and expected inference error are proposed for limiting location leakages. In this article, we argue that personalization means regionalization for geo-indistinguishability, and we propose a regionalized location obfuscation mechanism called DPIVE with personalized utility sensitivities. This substantially corrects the differential and distortion privacy problem of the PIVE framework proposed by Yu et al. on NDSS 2017. We develop DPIVE with two phases. In Phase I, we determine disjoint sets by partitioning all possible positions such that different locations in the same set share the Protection Location Set (PLS). In Phase II, we construct a probability distribution matrix in which the rows corresponding to the same PLS have their own sensitivity of utility (PLS diameter). Moreover, by designing a QK-means algorithm for more search space in 2-D space, we improve DPIVE with a refined location partition and present fine-grained personalization, enabling each location to have its own privacy level endowed with a customized privacy budget. Experiments with two public datasets demonstrate that our mechanisms have the superior performance, typically on skewed locations.

Federated Learning (FL) is a distributed machine learning framework in parallel and distributed systems. However, the systems’ Non-Independent and Identically Distributed (Non-IID) data negatively affect the communication efficiency, since clients with different datasets may cause significant gaps to the local gradients in each communication round. In this article, we propose a Federated Vectorized Averaging (FedVeca) method to optimize the FL communication system on Non-IID data. Specifically, we set a novel objective for the global model which is related to the local gradients. The local gradient is defined as a bi-directional vector with step size and direction, where the step size is the number of local updates and the direction is divided into positive and negative according to our definition. In FedVeca, the direction is influenced by the step size, thus we average the bi-directional vectors to reduce the effect of different step sizes. Then, we theoretically analyze the relationship between the step sizes and the global objective, and obtain upper bounds on the step sizes per communication round. Based on the upper bounds, we design an algorithm for the server and the client to adaptively adjusts the step sizes that make the objective close to the optimum. Finally, we conduct experiments on different datasets, models and scenarios by building a prototype system, and the experimental results demonstrate the effectiveness and efficiency of the FedVeca method.

Mobile Crowd Sensing (MCS) has emerged as a novel paradigm in massive data collection, which leverages many individual mobile devices (called workers) to collect data. MCS platform utilizes the collected data to construct various services for service requesters, thus obtaining profit based on the data values contributed by workers. However, untrustworthy data would greatly reduce the data value, leading to a decline in platform profit, so it is crucial for the platform to recruit high-trust workers and collect truthful data, thereby providing high-quality service and obtaining high profit. To address this problem, we propose a Maximize Profit Scheme, called MPS, for MCS platforms, which consider that the data value declines as data trust decreases and discounts over time. MPS scheme is the first work that systematically addresses the impact of untruthful data on the platform profit, which is not well addressed in previous research. First, we utilize historical data of trusted workers as truthful data to identify the truth of data, which is a low-cost method. Then, a trust-discounting and time-discounting value model is proposed, which is more practical than previous methods. Based on the proposed value model, we propose a novel worker recruitment strategy combined with a trust-related and time-dependent reward threshold, which prioritizes workers with high trust and low latency, thereby promoting the data value of workers and maximizing the platform's profit. By comparing the MPS with existing schemes, the experimental results show that our MPS can achieve better performance in terms of total profit.

Mobile Crowd Sensing (MCS) is an emerging paradigm that constructs various services by recruiting massive workers in edge networks to sense data. However, ensuring Quality-of-Service (QoS) while preserving bilateral location privacy remains a critical challenge for effective service provisioning in the context of spatiotemporal crowdsourcing. Previous studies have achieved privacy preservation through location obfuscation, which has the problem of low task-worker matching rate, and data loss adversely affected QoS. To tackle this issue, we propose a Deep Neuro-Fuzzy based Bilateral Location Privacy-Preserving (DNF-BLPP) scheme to construct services in spatiotemporal crowdsourcing. In this article, we first present a novel obfuscation strategy that obfuscates the location of each task and worker respectively to${\rm{\lambda }}$locations with the highest correlation, ensuring privacy preservation while providing assurance for accurate data recovery. Then, we further introduce a deep neural-fuzzy approach to solve the worker selection problem under obfuscated locations. Based on that, a Non-negative Constraint Matrix Factorization algorithm is employed to accurately impute missing data based on time-space correlation. Theoretical analysis and extensive simulations show that the proposed scheme has a strong ability to protect location privacy, and is better than the state-of-the-art schemes in performance indicators such as task-worker matching rate, QoS and computational efficiency.

Truth and Privacy-preserving service are two key issues for data collection in Mobile Crowd Sensing (MCS). However, most of the existing data collection studies use CRH to calculate the truth value, which is difficult to guarantee the data accuracy. The proposed privacy-preserving service methods either neglect the truth-value computation or still adopt the CRH method. To solve the challenge, we propose a Reputation and Privacy-Preserving Service based Truth Data Collection (RPPS-TDC) scheme to achieve the privacy-preserving accurate data collection for MCS. RPPS-TDC scheme consists of two steps: worker trust calculation and reputation-based truth value calculation. Firstly, data requester performs the initial weights calculation on the received data using DBCRH method. Secondly, reputation center uses the initial weights to update the reputation, which is based on the trustworthy worker inference. Finally, the workers are reweighted according to their reputation. We select workers based on trust_MaxHeap, thus obtaining more accurate data. At the same time, we give workers payments based on their trust weights. All the above operations are completed under data encryption, ensuring that only the data requesters have access to the workers’ submitted data. The platform consists of mining nodes, and reputation center resides on the blockchain, thus achieving distributed computing which overcomes the shortcomings of the traditional MCS system. Extensive experiments demonstrate that RPPS-TDC is effective and outperforms previous strategies in two key performance metrics: data accuracy and compensation rationality allocation.

Semantic segmentation, which aims to accurately identify each pixel, is a meaningful and challenging task. Recently, we witness a strong tendency to improve model efficiency in low-computing applications. However, most real-time methods ignore hierarchical features and context information to improve efficiency, leading to a decrease in the accuracy of semantic segmentation. To this end, we propose a novel system named hierarchical semantic-aware network (HSNet) to refine multilevel context information. HSNet mainly has the following two core modules: 1) hierarchical feature refinement module (HFRM) and 2) cross-scale pyramid fusion module (CPFM). By aggregating hierarchical feature maps, the proposed HFRM learns multilevel feature representation to recover spatial details. Afterward, the dual attention mechanism is developed to refine features from both channel and spatial levels, thereby alleviating the multilevel semantic gap. Meanwhile, the CPFM, which fuses local and global context information in a cross-scale manner, is proposed to enrich semantic information to improve accuracy. Furthermore, HSNet is carefully designed to improve the efficiency of the model by reusing shallow features and reducing channel capacity. Extensive experiments show that our method is effective and superior in segmentation accuracy and inference speed compared with state-of-the-art methods.

In this article, a novel system based on the simultaneous iterative reconstructive technique (SIRT) and multiagent deep reinforcement learning is proposed for detection of anomalous geological structures in coal mines. The system employs the SIRT optimization inversion method to construct a computational model for channel wave signal imaging. Then, the back projection technique (BPT) was introduced to the system. By utilizing the BPT algorithm to provide initial values for the SIRT, the channel wave signals can be prescreened, improving the ability of the SIRT algorithm to suppress model noise and enhancing its resolution. Furthermore, we employ multiagent reinforcement learning method for image feature classification of anomalous geological structures. Moreover, we conduct two-dimensional and three-dimensional imaging of four types of changes and energy fluctuations. The results demonstrate a high degree of concordance between the computed channel wave results and the slowness of the measured channel wave signals. Experimental findings validate the exceptional computational accuracy of this novel system, with relative errors and coefficient of deviation both within 1%, surpassing traditional SIRT inversion methods, damped least-squares methods, conjugate gradient methods, and classical algebraic reconstruction methods. These discoveries demonstrate the feasibility and superiority of utilizing transmission tomography imaging technology for the detection of anomalous structures in coal seams, offering new perspectives for underground exploration in coal mines.

The Satellite-Terrestrial Integrated Networks (STINs) is considered as a reliable and agile next-generation communication network scheme, because it is flexible to deploy and robust to disasters (e.g., earthquakes, floods, and volcanic eruptions). The routing and resource allocation directly affect the power consumption of satellites in the STINs. However, most of existing research works study these two issues separately, and neglect the impact of path selection on the satellite power consumption. This paper investigates the joint optimization problem of routing, bandwidth allocation, user association, channel allocation, and power allocation of the STINs. We aim at minimizing satellites’ power consumption while satisfying user's Quality of Service (QoS). Because the power consumption of satellites for routing in the space segment is involved with the data rate through satellite gateways in the user segment, this optimization problem is a Multi-Objective Optimization (MOO) problem. To this end, we convert the MOO problem to a Single-Objective Optimization (SOO) problem by linear weighted method. Considering that the SOO problem is a Mixed Integer Nonlinear Programming (MINLP) problem, we decompose it into the resource allocation sub-problem in the user segment and the routing optimization sub-problem in the space segment. And we solve these two sub-problems iteratively. Specifically, we convert the first sub-problem into a convex optimization problem and obtain the current user association and channel allocation. Then we formulate the second sub-problem into a multi-commodity flow problem and solve it based on the obtained user association and channel allocation. The SOO problem is solved by iteratively optimizing these two sub-problems. Experimental results demonstrate that our proposed algorithm is able to get an approximate optimal solution quickly and effectively reduce the power consumption of satellites compared with benchmark algorithms in the STINs.

In this paper, we propose a dynamic object elimination algorithm that combines semantic and geometric constraints to address the problem of visual SLAM being easily affected by dynamic feature points in dynamic environments. This issue leads to the degradation of localisation accuracy and robustness. Firstly, we employ a lightweight YOLO-Tiny network to enhance both detection accuracy and system speed. Secondly, we integrate the YOLO-Tiny network into the ORB-SLAM3 system to extract semantic information from the images and initiate the elimination of dynamic feature points. Subsequently, we augment this approach by incorporating geometric constraints between neighbouring frames to further eliminate dynamic feature points. Then, the former is supplemented by combining the geometric constraints between neighbouring frames to further eliminate dynamic feature points. Experiments on the TUM dataset demonstrate that the algorithm in this paper can improve the Relative Pose Error (RPE) by up to 95.12% and the Absolute Trajectory Error (ATE) by up to 99.01% in high dynamic sequences compared to ORB-SLAM3. The effectiveness of dynamic feature point elimination is evident, leading to significantly improved localisation accuracy.

Postoperative delirium is a common and preventable complication after cardiovascular surgery and is associated with increased risk of morbidity and mortality. However, strategies for identifying at-risk patients are limited. In this prospective observational study, intraoperative electroencephalography data of 50 patients undergoing cardiovascular surgery were collected. Twenty-five patients of them experienced delirium after surgery and 25 patients did not. The partial directional coherence method was used to evaluate the effective connectivity within the default mode network (DMN) regions in four frequency bands. Statistically significant features were considered as input signals in the CatBoost classifier to predict postoperative delirium. Compared with patients without delirium, patients with postoperative delirium had enhancement of causal effects in the DMN area, especially in the delta band. The accuracy rate of distinguishing patients with postoperative delirium from patients without postoperative delirium could reach 89.1%. These findings might help to explain why information processing was disturbed in patients with delirium and predict postoperative delirium.

Abstract With the development of the drying rack system, users with limited time tend to use the fully functional drying rack system to realize various intelligent control functions. However, the existing control methods for drying rack system has some defects such as, low intelligence and system delay, which are unsuitable for most users. In this paper, an efficient intelligent control algorithm based on the back‐propagation (BP) neural network is proposed. In this system, STM32F103 is first utilized as the central controller and multiple sensors are used to collect environmental information. Then, the remote control, voice keywords, and buttons can be used to achieve intelligent control. Subsequently, a motor drive intelligence control algorithm based on the BP neural network (MCBP) is proposed to improve the accuracy of the intelligent control. Next, an Application (APP) that can display environmental data such as wind speed, temperature, and humidity is developed. The APP can realize various intelligent control functions such as lifting, panning, rotating, and harvesting. Finally, MCBP is compared with normal control and programmable logic controller control. The accuracy of the MCBP is higher than other two control methods. The final extensive experiments confirm the accuracy and efficiency of the proposed intelligent control algorithm.

Large number of Internet of Things (IoT) devices deployed on demand to monitor surroundings and offload computation-intensive tasks to edge servers, which favors low-latency IoT applications. However, some of them scatter in areas with limited or no available communication infrastructure (no-ACI), which rises a huge challenge for task offloading, due to when and which devices have tasks cannot be known in advance. In this article, a trust-based active notice task offloading (TANTO) scheme in the aerial computing system (ACS) assisted by the unmanned aerial vehicle (UAV) is proposed to provide trust and low-delay task offloading for resource-limited IoT devices in areas with no-ACI. The main innovations of TANTO in ACS are as follow: 1) a novel task offloading mechanism for IoT devices in networks with no-ACI is proposed, where devices broadcast task offloading notice, making it easy for UAV to know where tasks need to offload without aimless search, which can effectively reduce UAV’s flight distance and task completion delay at the same time; 2) a trust calculation and reasoning method is proposed to calculate network trust, and divide the network into grids with different trust values, which guides UAV through grids with higher trust to increase the task completion rate and reduce delay; and 3) an online UAV trajectory optimization algorithm is proposed to dynamically prioritize tasks with urgent deadline. A large number of experimental results show that TANTO has better performance than previous studies in terms of task completion rate, tasks’ average completion time, and UAV’s flight cost.

The development of Industrial Internet of Things (IIoT) and Industry 4.0 has completely changed the traditional manufacturing industry. Intelligent IIoT technology usually involves a large number of intensive computing tasks. Resource-constrained IIoT devices often cannot meet the real-time requirements of these tasks. As a promising paradigm, the mobile-edge computing (MEC) system migrates the computation intensive tasks from resource-constrained IIoT devices to nearby MEC servers, thereby obtaining lower delay and energy consumption. However, considering the varying channel conditions as well as the distinct delay requirements for various computing tasks, it is challenging to coordinate the computing task offloading among multiple users. In this article, we propose an autonomous partial offloading system for delay-sensitive computation tasks in multiuser IIoT MEC systems. Our goal is to provide offloading services with minimum delay for better Quality of Service (QoS). Enlighten by the recent advancement of reinforcement learning (RL), we propose two RL-based offloading strategies to automatically optimize the delay performance. Specifically, we first implement the$Q$-learning algorithm to provide a discrete partial offloading decision. Then, to further optimize the system performance with more flexible task offloading, the offloading decisions are given as continuous based on deep deterministic policy gradient (DDPG). The simulation results show that the$Q$-learning scheme reduces the delay by 23%, and the DDPG scheme reduces the delay by 30%.

In the industrial Internet of Things (IoT), large amounts of image data are collected, stored, and transmitted by low-power cooperative sensors and heterogeneous devices, such as monitoring pictures taken by surveillance cameras in intelligent transportation. Thus, the increasing image data would be outsourced and shared between different distributed servers. Recently, some secret image sharing with authentication (SISA) schemes were proposed, which can provide image sharing in the distributed IoT system. However, if the IoT data are tampered with by malicious attackers, the error cannot be corrected, which causes unreliability and data loss. Therefore, in order to solve the above issues, we propose a secure and reliable secret image-sharing system based on extended Hamming codes (RSIS) in IoT. In RSIS, a novel distributed IoT architecture-based secret sharing is proposed. A secret image is shared into a series of stego-images. These stego-images can be distributed among multiple edge servers in IoT, which is not easy to attract attentions of attackers. In RSIS, check bits generated by authentication bits and hamming code are embedded into the stego-images to identify the tampered places with a high probability. Theoretical analysis and experiments prove that the proposed scheme achieves a high authentication capability, and the bit errors of data can be accurately detected and corrected. Generally, the scheme is effective and practical.

Indoor gesture recognition technology is concerned with making the machine accurately recognize dynamic gestures within a certain range. Remarkably, most of this technology is based on passive recognition methods. This is quite striking because the high cost is a crucial factor in active recognition methods and ignoring this aspect can increase the reality gap. In this paper, we tend to use the fine-grained channel state information (CSI) in Wi-Fi to build a dynamic CNN-GRU-Attention (CGA) model to implement a gesture recognition system and thus alleviate this problem. Firstly, we study the influence of gestures on the amplitude and phase difference in CSI, and prove the feasibility of proposed method by analyzing the fluctuation of amplitude and phase difference under different conditions. Then, we use data processing methods such as phase correction and unwrapping with a new proposed adaptive gesture action truncation algorithm to extract the phase difference and remove redundant information, thus ensuring the validity of data. Finally, we propose to segment gesture fragment into 3-channel CSI images as input information of model. Extensive comparison experiments are conducted under the influence of different people, different indoor environments , and different sampling rates . The results show that the system has high accuracy.

Modern cloud computing applications have stringent low-latency and high-throughput requirements to meet the increasingly diverse demands from customers. In edge datacenters, the popular load balancing scheme based on packet spraying, i.e., random packet spraying (RPS), makes good use of multiple equal-cost paths to ensure high link utilization and efficient transmission. However, RPS performs poorly under the bursty traffic scenario due to packet loss or even timeout. Therefore, to reduce the tail latency caused by retransmission, we design a coding-based random packet spraying named CRPS. Specifically, the source host transmits forward error correction (FEC) encoded packets and dynamically adjusts the data redundancy based on the packet loss rate. Then the switch randomly sprays the encoded packets across all equal-cost multiple paths to implement parallel transmission. In this way, once enough encoded packets from any parallel paths arrive at the destination host, the original packets can be decoded immediately to address the adverse impact of packet loss. The NS-2 simulation results show that CRPS effectively reduces the probability of timeout and significantly improves the tail flow completion time (FCT) by up to 72% compared with the state-of-the-art multipath transmission schemes.

This article mainly studies the projective quasisynchronization for an array of nonlinear heterogeneous-coupled neural networks with mixed time-varying delays and a cluster-tree topology structure. For the sake of the mismatched parameters and the mutual influence among distinct clusters, the exponential and global quasisynchronization within a prescribed error bound instead of complete synchronization for the coupled neural networks with clustering trees is investigated. A kind of pinning impulsive controllers is designed, which will be imposed on the selected neural networks with some largest norms of error states at each impulsive instant in different clusters. By employing the concept of the average impulsive interval, the matrix measure method, and the Lyapunov stability theorem, sufficient conditions for the realization of the cluster projective quasisynchronization are derived. Meanwhile, in terms of the formula of variation of parameters and the comparison principle for the impulsive systems with mixed time-varying delays, the convergence rate and the synchronization error bound are precisely estimated. Furthermore, the synchronization error bound is efficiently optimized based on different functions of the impulsive effects. Finally, a numerical experiment is given to prove the results of theoretical analysis.

Although analyzing the brain's functional and structural network has revealed that numerous brain networks are necessary to collaborate during deception, the directionality of these functional networks is still unknown. This study investigated the effective connectivity of the brain networks during deception and uncovers the information-interaction patterns of lying neural oscillations. The electroencephalography (EEG) data of 40 lying persons and 40 honest persons were used to create the weight- directed functional brain networks (WDFBN). Specifically, the connecting edge weight was defined based on the normalized phase transfer entropy (dPTE) between each electrode pair, where the network nodes involved 30 electrode channels. Additionally, the signal connectivity matrices were constructed in four frequency bands: delta, theta, alpha, and beta and were subjected to a difference analysis of entropy values between the groups. Statistical analysis of the classification results revealed that all frequency bands correctly detect deception and innocence with an accuracy of 92.83%, 94.17%, 85.93%, and 92.25%, respectively. Therefore, dPTE can be considered a valuable feature for identifying lying. According to WDFBN analysis, deception has stronger information flow in the frontoparietal, frontotemporal and temporoparietal networks compare to honest people. Furthermore, the prefrontal cortex was also found to be activated in all frequency ranges. This study examined the critical pathways of brain information interaction during deception, providing new insights into the underlying neural mechanisms. Our analysis offers significant evidence for the development of brain networks that could potentially be used for lie detection.

6G networks provide faster communication for connected vehicles. These vehicles are connected to the Internet, forming the Internet of Vehicles (IoV). Due to the development of Intelligent Transportation Systems (ITS), more and more vehicles are deployed with data-intensive applications. These applications interact heavily with IoT devices at the edge of the network, which causes IoT devices to consume a lot of limited and valuable power. Task offloading can help overcome resource constraints of IoT devices by offloading task to edge server which has sufficient computational power in ITS. Unmanned Aerial Vehicles (UAV) is a promising solution by serving as Computing-Communications Edge Server (CCES) for resource-constrained IoT devices that there is no edge server nearby that can offload task. Due to the IoT devices' limited battery capacity and UAV energy budget, it is a challenging issue to reduce the energy for task offloading in UAV-enable edge network. In this paper, an UAV-enabled Computing-Communications Intelligent Offloading (UAV-CCIO) scheme is proposed to offload task energy-efficiently. First, some nodes with a large amount of data are selected as Task Gathering Nodes (TGNs), and TGNs collect all the tasks of the left nodes. In this way, the UAV can only fly the TGNs and so all the IoT devices' tasks can be offloaded. The distance needed for the UAV can be greatly reduced and energy is saved. On the other hand, tasks that route to TGNs have a relatively small amount of data, while nodes with a large amount of data have already been selected as TGNs without routing, thus saving energy. Second, an optimization strategy for collection tasks is proposed to reduce UAV's energy. The extensive experimental simulations indicate that the performance of UAV-CCIO scheme is better than the existing scheme.

Gait authentication as a technique that can continuously provide identity recognition on mobile devices for security has been investigated by academics in the community for decades. However, most of the existing work achieves insufficient generalization to complex real-world environments due to the complexity of the noisy real-world gait data. To address this limitation, we propose an intelligent Implicit Real-time Gait Authentication (IRGA) system based on Deep Neural Networks (DNNs) for enhancing the adaptability of gait authentication in practice. In the proposed system, the gait data (whether with complex interference signals) will first be processed sequentially by the imperceptible collection module and data preprocessing module for improving data quality. In order to illustrate and verify the suitability of our proposal, we provide analysis of the impact of individual gait changes on data feature distribution. Finally, a fusion neural network composed of a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) is designed to perform feature extraction and user authentication. We evaluate the proposed IRGA system in heterogeneous complex scenarios and present start-of-the-art comparisons on three datasets. Extensive experiments demonstrate that the IRGA system achieves improved performance simultaneously in several different metrics.

The advent of Deep Neural Networks (DNNs) has empowered numerous computer-vision applications. Due to the high computational intensity of DNN models, as well as the resource constrained nature of Industrial Internet-of-Things (IIoT) devices, it is generally very challenging to deploy and execute DNNs efficiently in the industrial scenarios. Substantial research has focused on model compression or edge-cloud offloading, which trades off accuracy for efficiency or depends on high-quality infrastructure support, respectively. In this article, we present EdgeDI, a framework for executing DNN inference in a partitioned, distributed manner on a cluster of IIoT devices. To improve the inference performance, EdgeDI exploits two key optimization knobs, including: (1) Model compression based on deep architecture design, which transforms the target DNN model into a compact one that reduces the resource requirements for IIoT devices without sacrificing accuracy; (2) Distributed inference based on adaptive workload partitioning, which achieves high parallelism by adaptively balancing the workload distribution among IIoT devices under heterogeneous resource conditions. We have implemented EdgeDI based on PyTorch, and evaluated its performance with the NEU-CLS defect classification task and two typical DNN models (i.e., VGG and ResNet) on a cluster of heterogeneous Raspberry Pi devices. The results indicate that the proposed two optimization approaches significantly outperform the existing solutions in their specific domains. When they are well combined, EdgeDI can provide scalable DNN inference speedups that are very close to or even much higher than the theoretical speedup bounds, while still maintaining the desired accuracy.

With the development of the Internet of Things (IoT) , a large-scale, heterogeneous, and dynamic distributed network has been formed among IoT devices. There is an extreme need to establish a trust mechanism between devices, and blockchain can provide a zero-trust security framework for IoT. However, the efficiency of the blockchain is far from meeting the application requirements of the IoT, which has become the biggest resistance to the application of the blockchain in the IoT. Therefore, this paper combines sharding to build an effective Blockchain-based IoT data storage scheme (S-BDS) . Sharding can solve the problem of blockchain capacity and scalability. While the blockchain provides data immutability and traceability for the IoT, it also brings huge demands for data credibility verification. The communication delay in the IoT system seriously affects the security of the system, while the Merkle proof of traditional blockchain occupies a lot of communication resources. This paper constructs Insertable Vector Commitment (IVC) in the bilinear group and replaces the Merkle tree with IVC to store IoT data in the blockchain. The construct has small-sized proof. It also has the ability to record the number of updates, which can prevent replay-attacks. Experiments show that each block processes 1,000 transactions, the proof size of a single data piece is 30% of the original scheme, and proofs from different shards can be aggregated. IVC can effectively reduce communication congestion and improve the stability and security of the IoT system.

With the rapid proliferation of urbanization, massive data in social networks are collected and aggregated in real time, making it possible for criminals to use images as a cover to spread secret information on the Internet. How to determine whether these images contain secret information is a huge challenge for multimedia computing security. The steganalysis method based on deep learning can effectively judge whether the pictures transmitted on the Internet in urban scenes contain secret information, which is of great significance to safeguarding national and social security. Image steganalysis based on deep learning has powerful learning ability and classification ability, and its detection accuracy of steganography images has surpassed that of traditional steganalysis based on manual feature extraction. In recent years, it has become a hot topic of the information hiding technology. However, the detection accuracy of existing deep learning based steganalysis methods still needs to be improved, especially when detecting arbitrary-size and multi-source images, their detection efficientness is easily affected by cover mismatch. In this manuscript, we propose a steganalysis method based on Inverse Residuals structured Siamese network (abbreviated as SiaIRNet method, Sia mese- I nverted- R esiduals- Net work Based method). The SiaIRNet method uses a siamese convolutional neural network (CNN) to obtain the residual features of subgraphs, including three stages of preprocessing, feature extraction, and classification. Firstly, a preprocessing layer with high-pass filters combined with depth-wise separable convolution is designed to more accurately capture the correlation of residuals between feature channels, which can help capture rich and effective residual features. Then, a feature extraction layer based on the Inverse Residuals structure is proposed, which improves the ability of the model to obtain residual features by expanding channels and reusing features. Finally, a fully connected layer is used to classify the cover image and the stego image features. Utilizing three general datasets, BossBase-1.01, BOWS2, and ALASKA#2, as cover images, a large number of experiments are conducted comparing with the state-of-the-art steganalysis methods. The experimental results show that compared with the classical SID method and the latest SiaStegNet method, the detection accuracy of the proposed method for 15 arbitrary-size images is improved by 15.96% and 5.86% on average, respectively, which verifies the higher detection accuracy and better adaptability of the proposed method to multi-source and arbitrary-size images in urban scenes.

By sharing resources between edge servers and end users, edge-end cooperation is one important way to support various applications in Internet of Things, which have critical resource requirements on computing, storage, or bandwidth. How to price these resources and how to evaluate the service quality of edge servers are two key issues to support trusted resource allocation for blockchain-enabled Internet of Things. In this article, we provide a trusted resource allocation mechanism based on smart contracts, in which a group-buying pricing mechanism (GBPM) and a reputation evaluation mechanism (REM) are proposed to effectively address the problems existing in resources pricing and service quality evaluation of edge servers. In the trusted resource allocation mechanism, end users can choose a purchase mode from four pricing schemes in terms of actual demands on delay and price, and smart contracts can match end users with high-reputation edge servers automatically. Moreover, end users can submit reputation evaluations to smart contracts based on the behaviors of edge servers. Simulation results show the GBPM can provide differentiated prices and optimize the utility of end users accordingly, while the REM is more sensitive to edge servers with irregular behaviors and quickly reduces their reputations so that the success rate of transactions is improved.

Recent developments in collection, computation and communication have expanded the way of data reporting in intelligent Internet of Things (IoT). However, diversity and complexity of data sources also impose new trust challenge in data collection process since untrust reporters tend to report false or even malicious data, which highlights the need to develop a novel methodology to solve such challenge. Thus, based on this domain, inspired by deterrence theory, this article proposes a blockchain-based trust system with assistant of drones to deter malicious data reporting in intelligent IoT. Specifically, to deter malicious data reporting, based on the blockchain technology, the data sensed by fully trusted drones is public published on blockchain showing participants the data standards, named as malicious deterrence scheme. This scheme provides a barrier for malicious reporters to arbitrarily publish false data to blockchain, since the false data can be easily detected while they cannot deny. Second, to further reduce malicious data reporting, a strict penalty mechanism is proposed to punish malicious reporters who have reported false data to blockchain to reduce the malicious data reporting in the following task through punishment. Third, note that the sensing of data standard generates additional costs, therefore, a drone flight route scheme based on a simper deep reinforcement learning with multihead attention mechanism (MA-DRL) is designed to reduce the flight distance for drones. Finally, extensive experiments demonstrate efficiency of our proposed system in terms of reducing malicious data reporting in advance as well as reducing drone flight distance.

Contemporarily, the fast development of computing, communication, and storage technology has revolutionized the way that various data-based applications reach massive data from underlying sensor networks. However, such a process also raises two challenging but critical issues: 1) trustworthy and 2) privacy issue for data collectors. Therefore, this article proposes a novel system, which is designed over the distributed mobile-edge network to sufficiently exploit advantages of blockchain and differential privacy (DP) to collect trustworthy data and protect privacy for data collectors. First, to improve trustworthiness of data collections, a new consensus mechanism is proposed for blockchain-based data collection structure, which comprehensively incorporates trustworthy, collection contribution, and throughput together to prefer data collectors for the next block. Second, with the assistance of fully trusted devices, a verifiable trustworthy evaluation strategy is designed to accurately compute the trustworthiness for data collectors. Third, we enforce DP on the data stored in a global blockchain maintained by the cloud server to protect privacy for data collectors without influencing data availability. Finally, both theoretical analyses and experimental results prove that the proposed system comprehensively improves performance of data collections in distributed network without adding any additional cost for the cloud server, compared to other schemes.

Recently, rapid deployment on the fifth-generation (5G) networks has brought great opportunities for enabling data-intensive applications and brings an extending expectation on the developments of 6G. A basic requirement to develop 6G networks is to reach data with low latency, low cost, and high coverage in smart Internet of Things (IoT). Therefore, this article proposes a novel machine learning-based approach to collect data from multiple sensor devices by cooperation between vehicle and unmanned aerial vehicle (UAV) in IoT. First, a genetic algorithm is utilized to select vehicular collectors to collect massive data from sensor devices, which aims to maximize coverage ratio and to minimize employment cost. Second, we design a novel deep reinforcement learning (DRL)-based route policy to plan collection routes of UAVs with constrain energy, which simplifies the network model, accelerates training speeds, and realizes dynamic planning of flight paths. The optimal collection route of a UAV is a series of outputs based on the proposed DRL-based route policy. Finally, our extensive experiments demonstrate that the proposed scheme can comprehensively improve the coverage ratio of massive data collections and reduce collection costs in smart IoT for the future 6G networks.

With the rapid development of the Internet of Things (IoT), it becomes convenient to use mobile devices to remotely monitor the physiological signals (e.g., Arrhythmia diseases) of patients with chronic diseases [e.g., cardiovascular diseases (CVDs)]. High classification accuracy of interpatient electrocardiograms is extremely important for diagnosing Arrhythmia. The Supraventricular ectopic beat (S) is especially difficult to be classified. It is often misclassified as Normal (N) or Ventricular ectopic beat (V). Class imbalance is another common and important problem in electronic health (eHealth), as abnormal samples (i.e., samples of specific diseases) are usually far less than normal samples. To solve these problems, we propose a sustainable deep learning-based heart beat classification system, called BeatClass. It contains three main components: two stacked bidirectional long short-term memory networks (Bi-LSTMs), called Rist and Morst, and a generative adversarial network (GAN), called MorphGAN. Rist first classifies the heartbeats into five common Arrhythmia classes. The heartbeats classified as S and V by Rist are further classified by Morst to improve the classification accuracy. MorphGAN is used to augment the morphological and contextual knowledge of heartbeats in infrequent classes. In the experiment, BeatClass is compared with several state-of-the-art works for interpatient arrhythmia classification. The$F1$-scores of classifying N, S, and V heartbeats are 0.6%, 16.0%, and 1.8% higher than the best baseline method. The experimental result demonstrates that taking multiple classification models to improve classification results step-by-step may significantly improve the classification performance. We also evaluate the classification sustainability of BeatClass. Based on different physical signal data sets, a trained BeatClass can be updated to classify heartbeats with different sampling rates. Finally, an engineering application indicates that BeatClass can promote the sustainable development of IoT-based eHealth.

Embedded devices (e.g., mobile phones, smart watches, etc.) store a large amount of sensitive information. However, Android-based devices may leak a lot of user information if unsafe data deletion. Therefo re, research on secure data deletion for embedded devices has become a practical and urgent issue. In this article, we study the logic structure, operation characteristics, and data management mechanisms of flash memory. Then, we propose a novel method Vacuum that uses a user-space file system and can provide fine-grained file deletion guarantees. Our approach encrypts files on an insecure medium with a unique key that can later be discarded to cryptographically render the data irrecoverable. Additionally, we use TrustZone as a secure key vault, and a garbage collection mechanism is introduced to purge the memory. Finally, we carried out experiments on the Android system, and the results showed that the solution is efficient and can meet the needs of real applications.

Nowadays, with the explosive growth of sensor-based devices connected to Internet of Things (IoT), massive amount of data are generated every day with potential tremendous value. We argue that the value of those data can be extracted through monetize data platform in IoT-Edge-Cloud ecosystems for many parts of the business. In such monetize data platform, the data can be computed and transformed into services in IoT-Edge-Cloud ecosystems and provide data-as-a-service (DAAS) for applications. The key to implement such a monetize data platform is to evenly distribute DAAS computing tasks to network devices to maximize the benefits of the system. So, in this article, we study the task type-based computation offloading algorithm (TTCO) to implement such platform. We use the “IoT-Edge-Cloud” three-layer multihop model, which is closer to the complex scene in monetize data platform. We divide tasks into data-intensive tasks and CPU-intensive tasks, and then combine the cost model of computation offloading with task type to make data-intensive tasks prefer local computing and CPU-intensive tasks prefer offload computing, thereby reducing the monetize data platform transmission volume and improving the overall quality of computation offloading. We then use a hierarchical game model combined with fictitious play to solve the Nash equilibrium (NE) of the system and obtain the mixed strategies of the devices. Finally, we propose a TTL-constrained flood strategy transmission mechanism to make the algorithm apply to practice. The experimental results prove that our algorithm has a large performance gain in various scenarios, which can be severed as a monetize data platform for IoT-Edge-Cloud ecosystems.

By migrating tasks from the end devices to the edge or cloud, cooperative computing in the Internet of Things can support time-sensitive, high-dimensional, and complex applications while utilizing existing resources, such as the network bandwidth, computing resources, and storage capacity. How to design the multiresource allocation system efficiently is a significant research problem. In this article, we design a multiresource allocation system for cooperative computing in the Internet of Things based on deep reinforcement learning by redefining latency calculation models for communication, computation, and caching with the consideration of practical interference factors, such as the Gaussian noise and data loss. The proposed system uses actor–critic as the base model for rapidly approximating the optimal policy by updating parameters of the actor and critic in respective gradient directions. The balance control parameter is introduced to fit the desired learning rate and actual learning rate. At the same time, we use the method of double experience pool to limit the exploration direction of the optimal policy, which reduces the time complexity and space complexity of the problem solution and improves the adaptability and reliability of the scheme. Experiments have demonstrated that multiresource allocation algorithm based on deep reinforcement learning (DRL-MRA) performs well in terms of the average service latency under resource-constrained conditions, and the improvement is significant with the increase of network size.

Fog-assisted Internet of Things (IoT) can outsource the massive data of resource-constraint IoT devices to cloud and fog nodes (FNs). Meanwhile, it enables convenient and low time-delay data-sharing services, which relies heavily on high security of data confidentiality and fine-grained access control. Many efforts have been focused on this urgent requirement by leveraging ciphertext-policy attribute-based encryption (CP-ABE). However, when deployed in fog-assisted IoT systems for secure data sharing, it remains a challenging problem of how to preserve attribute privacy of access policy, and trace-then-revoke traitors (i.e., malicious users intending to leak decryption keys for illegal profits) efficiently and securely in such a large scale and decentralized environment with resource-constraint user devices, especially in consideration of misbehaving cloud and FNs. Therefore, in this article, we propose a revocable and privacy-preserving decentralized data-sharing framework (RPDDSF) by designing a large universe and multiauthority CP-ABE scheme with fully hidden access policy for secure data sharing in IoT systems to achieve user attribute privacy preserving with unbounded attribute universe and key escrow resistance suitable for large scale and decentralized environment. Based on this, with RPDDSF, anyone can efficiently expose the traitors and punish them by forward/backward secure revocation. Besides, RPDDSF is able to guarantee data integrity for both data owners (DOs) and users to resist misbehaving cloud and FNs, alongwith low computation overhead for resource-constraint devices. Finally, RPDDSF is proven to be secure with detailed security proofs, and its high efficiency and feasibility are demonstrated by extensive performance evaluations.

With the rapid growth of the coronavirus disease of 2019 (COVID-19) cases, massive amounts of relevant data are being trained on machine learning models for countering communicable infectious diseases. Federated Learning (FL) is a paradigm of distributed machine learning to deal with the individual COVID-19 data, and enable the protection of data privacy. However, FL has low efficiency in Edge-Based wireless communication systems with system heterogeneity. In this paper, we propose an “Asynchronous-Adaptive FL” (AAFL) scheme. Specifically, we allow that medical devices with different performances have a heterogeneous number of local SGD iterations in each communication round, called asynchronous iteration strategy which is balanced under adaptive control. We theoretically analyze the convergence of the AAFL scheme under a given time budget and obtain a mathematical relationship between the heterogeneous number of local SGD iterations and the optimal model parameters. Based on the mathematical relationship, we design an algorithm for parameter server and work nodes to adaptively control the heterogeneous number of local SGD iterations. Subsequently, we build a prototype heterogeneous system and conduct experiments on various scenarios for analyzing the general properties of our algorithm, and then apply our algorithm to public COVID-19 databases. The experimental results and application performance demonstrate the effectiveness and efficiency of our AAFL scheme.

Future heterogeneous services and applications require the provisioning of unprecedented massive user access, heterogeneous data traffic, high bandwidth efficiency, and low latency services in next generation multiple access. In response to the requests from these services and applications, a large number of workers with scattered computing power need to be managed uniformly and scheduled in an efficient manner to perform various tasks. Therefore, task allocation has become a crucial issue to determining whether next generation multiple access can support future heterogeneous services and applications. In this paper, we propose a novel Two-stage Multi-task Allocation method based on Discrete Particle Swarm Optimization (TMA-DPSO). TMA-DPSO is easy to implement and has good search efficiency, which is suitable for large-scale task allocation in next generation networks. Under TMA-DPSO, we redefine the particles in discrete coding form, iteratively update the position and velocity based on the individual optimal particles and the global optimal particle, and finally obtain a corrected optimal solution. Unlike previous methods that only focused on the first-stage task allocation, we make full use of workers’ remaining time to perform second-stage redundant task allocation, which can not only increase workers’ income, but also potentially improve fault tolerance and security. As far as we know, this is the first attempt to utilize the remaining time after first-stage allocation. Finally, we evaluate TMA-DPSO extensively using the synthetic and real-life datasets. The results demonstrate that whether in a compactly or uniformly distributed scene, TMA-DPSO outperforms three benchmark methods by increasing 2.15%-42.24% platform revenue and 6.1%-46.63% workers income.

With the rise of the processing power of networked agents in the last decade, second-order methods for machine learning have received increasing attention. To solve the distributed optimization problems over multiagent systems, Newton's method has the benefits of fast convergence and high estimation accuracy. In this article, we propose a reinforced network Newton method with K -order control flexibility (RNN-K) in a distributed manner by integrating the consensus strategy and the latest knowledge across the network into local descent direction. The key component of our method is to make the best of intermediate results from the local neighborhood to learn global knowledge, not just for the consensus effect like most existing works, including the gradient descent and Newton methods as well as their refinements. Such a reinforcement enables revitalizing the traditional iterative consensus strategy to accelerate the descent of the Newton direction. The biggest difficulty to design the approximated Newton descent in distributed settings is addressed by using a special Taylor expansion that follows the matrix splitting technique. Based on the truncation on the Taylor series, our method also presents a tradeoff effect between estimation accuracy and computation/communication cost, which provides the control flexibility as a practical consideration. We derive theoretically the sufficient conditions for the convergence of the proposed RNN-K method of at least a linear rate. The simulation results illustrate the performance effectiveness by being applied to three types of distributed optimization problems that arise frequently in machine-learning scenarios.

Proximity testing technologies have been of increasing importance in vehicularad-hocnetworks (VANETs), especially in location-based services. However, there exist several known challenges in most existing proximity testing methods. For instance, during proximity testing, how to protect the location privacy of users, guarantee the fairness trait of both communication parties, and reduce computational costs is challenging. In this article, we present an efficient privacy-preserving proximity testing scheme using private set intersection (PSI) and differential privacy. In our design, a Chebyshev-based PSI is constructed to achieve location privacy with low energy consumption during the proximity testing process. Furthermore, geo-indistinguishability is employed in our scheme to generate virtual points as inputs set of PSI, which further protects the location privacy from exposure and provides resistance to collusion attacks. Fairness requirement is alsosatisfied in our scheme. The performance evaluation shows that the proposed scheme achieves good efficiency and is suitable for VANETs.

The rapid development of the Industrial Internet of Things (IIoT) enables IIoT devices to offload their computation-intensive tasks to nearby edges via wireless base stations and thus relieve their resource constraints. To better guarantee quality-of-service, it has become necessary to cooperate multiple edges instead of letting them work alone. However, the existing solutions commonly use a centralized decision-making manner and cannot effectively achieve good load balancing among massive edges that are widely distributed in IIoT environments. This results in long decision-making time and high communication costs. To address this important problem, in this article, we propose a reinforcement learning (RL)-empowered feedback control method for cooperative load balancing (RF-CLB). First, by integrating RL and machine learning (ML) algorithms, each edge independently schedules tasks and performs load balancing between adjacent edges based on the local information. Next, through feedback control and multiedge cooperation, the objective multiedge load-balancing plan for IIoT can be found. Simulation results demonstrate that the RF-CLB chooses the adjustment operations of load balancing with 96.3% correctness. Moreover, the RF-CLB achieves the near-optimal performance, which outperforms the classic ML-based and rule-based methods by 6–9% and 10–12%, respectively.

Due to the strong analytical ability of big data, deep learning has been widely applied to model on the collected data in industrial Internet of Things (IoT). However, for privacy issues, traditional data-gathering centralized learning is not applicable to industrial scenarios sensitive to training sets, such as face recognition and medical systems. Recently, federated learning has received widespread attention, since it trains a model by only sharing gradients without accessing training sets. But existing research works reveal that the shared gradient still retains the sensitive information of the training set. Even worse, a malicious aggregation server may return forged aggregated gradients. In this article, we propose the VFL, a verifiable federated learning with privacy-preserving for big data in industrial IoT. Specifically, we use Lagrange interpolation to elaborately set interpolation points for verifying the correctness of the aggregated gradients. Compared with existing schemes, the verification overhead of VFL remains constant regardless of the number of participants. Moreover, we employ the blinding technology to protect the privacy of the privacy gradients. If no more than$\boldsymbol{n}$-2 of$\boldsymbol{n}$participants collude with the aggregation server, VFL could guarantee the encrypted gradients of other participants not being inverted. Experimental evaluations corroborate the practical performance of the presented VFL with high accuracy and efficiency.

The increasing volume of user and household data and number of smart meters compound the challenge of ensuring efficiency and privacy protection of electricity trading in existing smart grids. Therefore, to minimize the cost and latency in electricity trading, in this article, we design a new architecture for smart meters that can support transactions (using a blockchain-based wallet) and initiate transmission switch instructions (using smart contacts). Specifically, our approach comprises a decentralized peer-to-peer electricity trading scheme to enable automated electricity transmission (using smart contract instead of some centralized entity), and our blockchain-based anonymous authentication scheme to facilitate fast and privacy-aware roaming, in order to achieve privacy protection. We demonstrate that our proposed scheme is secure under the universally composable framework.

A large number of devices with communication and sensing capabilities are connected to the 5G network and facilitate distributed industrial Internet of Things (IIoT) applications. Those devices can update their program code by using software-defined technologies and upgrade functions without hardware replacement, thus, greatly facilitating the development of IIoT applications. Disseminating code safely to these devices is a pivotal issue. However, the existing methods rarely consider whether the code received by devices is integrated. Therefore, an unmanned aerial vehicle (UAV) assisted trustworthy code dissemination (UTCD) framework in 5G-enabled intelligent IIoT systems is proposed to select credible mobile vehicles (MVs) to disseminate code with opportunistic routing style. In the UTCD framework, a verifiable trust evaluation scheme is proposed to identify the trust of the MVs by sending the UAV to collect the code wait to be verified (CWV) directly from the selected devices (bedrock devices) as evidence. This scheme is a fundamental change from the previous passive, indirect and unverifiable trust evaluation schemes. In UTCD, a bedrock devices selection scheme is presented by selecting as few bedrock devices as possible to collect as much the CWV as possible with the minimum cost. After that, based on the CWV, which is collected from bedrock devices, a complete trust evaluation scheme is proposed to obtain the trust of MVs. Furthermore, a UAV trajectory optimization algorithm is proposed to obtain as much CWV as possible within the limited conditions of UAV. Finally, comprehensive experiments conduct on a real-life dataset demonstrate that the proposed scheme outperforms the existing schemes in terms of the efficiency and security of code dissemination.

Recommendation accuracy is a fundamental problem in the quality of the recommendation system. In this article, we propose an efficient deep matrix factorization (EDMF) with review feature learning for the industrial recommender system. Two characteristics in user’s review are revealed. First, interactivity between the user and the item, which can also be considered as the former’s scoring behavior on the latter, is exploited in a review. Second, the review is only a partial description of the user’s preferences for the item, which is revealed as the sparsity property. Specifically, in the first characteristic, EDMF extracts the interactive features of onefold review by convolutional neural networks with word-attention mechanism. Subsequently,${L}_{0}$norm is leveraged to constrain the review considering that the review information is a sparse feature, which is the second characteristic. Furthermore, the loss function is constructed by maximuma posterioriestimation theory, where the interactivity and sparsity property are converted as two prior probability functions. Finally, the alternative minimization algorithm is introduced to optimize the loss functions. Experimental results on several datasets demonstrate that the proposed methods, which show good industrial conversion application prospects, outperform the state-of-the-art methods in terms of effectiveness and efficiency.

Large number of Internet of Things (IoT) applications with intelligent sensing devices (ISDs) are combined with cloud computing to collect and process data more efficiently. However, ISDs are vulnerable to various attacks. Compromised devices may maliciously provide unreliable data to the cloud, causing damage to IoT applications. Therefore, it is a critical issue to design an effective mechanism to ensure the security of data collection in cloud computing. In this article, a cloud-assisted reliable trust computing (CRTC) scheme is proposed to identify the trust of ISDs at low cost, providing high-quality data for IoT applications. The CRTC scheme mainly includes the following parts: First, a reliable approach of obtaining the real data of ISDs at a low cost is proposed to identify the trust of ISDs. In the proposed method, ISDs fits the forwarding packets information to form inspection information (II) with a small amount of data and then sends II to inspection nodes, effectively reducing the cost of routing II. Second, an effective method of trust computing is given to evaluate the trustworthiness of ISDs based on the data and II collected by unmanned aerial vehicles (UAV). Then, the aggregators are selected from high-trust ISDs to ensure secure data collection. Third, to obtain more reliable trust at lower cost, a trajectory optimization algorithm for UAV is proposed to collect as much II as possible and reduce the moving distance. Theoretical analysis and experimental results show that the proposed CRTC scheme is superior to previous strategies in terms of the success rate of data collection, the speed of identifying the trust of ISDs, and the UAV's trajectory distance.

At present, solid-state fermentation (SSF) is mainly controlled by artificial experience, and the product quality and yield are not stable. Therefore, predicting the quality and yield of SSF is of great significance for improving the utility of SSF. In this article, we propose a deep learning utility prediction (DLUP) scheme for the SSF in the Industrial Internet of Things, including parameters collection and utility prediction of the SSF process. Furthermore, we propose a novel edge-rewritable Petri net to model the parameters collection and utility prediction of the SSF process and further verify their soundness. More importantly, DLUP combines the generating ability of least squares generative adversarial network with the predicting ability of fully connected neural network to realize the utility prediction (usually use the alcohol concentration) of SSF. Experiments show that the proposed method predicts the alcohol concentration more accurately than the other joint prediction methods. In addition, the method in our article provides evidences for setting the ratio of raw materials and proper temperature through numerical analysis.

With the rapid development of wireless communication, traditional cloud computing cannot fully support low-latency services, especially in sensor networks. Mobile edge computing (MEC) can improve the quality of experience of end users and save the energy consumption of mobile end devices by providing computing resources and storage space. However, it may cause discontinuity of services if these mobile end devices roam around different MEC servers’ areas. To solve the aforementioned problem, in this article, we propose an effective edge-intelligent service placement algorithm (EISPA), which transforms the service placement problem into finding a globally optimal solution via nature-inspired particle swarm optimization (PSO). Moreover, we use a shrinkage factor and combine it with the simulated annealing (SA) algorithm to adjust the position of particles in our algorithm, which aims to avoid falling into an optimal local solution to a certain extent. Performance analysis results show that the EISPA is approaching the optimal enumeration collaborative computation offloading algorithm, and system cost under energy constraints is 83.6%, 20.4%, and 20.3% lower than that in Only Local, Finding the Nearest Edge, and the genetic SA-based PSO algorithms, respectively, which proves that the EISPA has better performance.

With the development of the Augmented and Virtual Reality (AR/VR) technologies, massive biometric data are collected by different organizations. These data have great significance but also worsen the privacy risks. Electro-CardioGram (ECG)-based Identity Recognition (EIR) is a popular Biometric technology. An ECG record is an internal Biology feature of a person and has time continuity. Thus, compared with traditional Biometric methods like face recognition, EIR may be less vulnerable to attack. We propose an Autoencoder-based EIR system, called Personalized AutoEncoder (PerAE). PerAE maintains a small autoencoder model (called Attention-MemAE) for each registered user of a system. The Attention-MemAE enhances the autoencoder by using a memory module and two attention mechanisms. A user's Attention-MemAE classifies the hearbeats of other users as anomalies. An Attention-MemAE can be updated when the distribution of the user's ECG data is changed. By using personalized autoencoder, PerAE can improve the time efficiency and reduce the memory overhead. It improves the adaptability, scalability, and maintainability of EIR systems. Experiment results show that to train an Attention-MemAE with 90 % identification accuracy for a user, we can just take five minutes to collect the user's ECG data (around 500 heartbeat samples).

With the increasing demand for intelligent transportation systems, short-term traffic flow prediction has become an important research direction. The memory unit of a Long Short-Term Memory (LSTM) neural network can store data characteristics over a certain period of time, hence the suitability of this network for time series processing. This paper uses an improved Gate Recurrent Unit (GRU) neural network to study the time series of traffic parameter flows. The LSTM short-term traffic flow prediction based on the flow series is first investigated, and then the GRU model is introduced. The GRU can be regarded as a simplified LSTM. After extracting the spatial and temporal characteristics of the flow matrix, an improved GRU with a bidirectional positive and negative feedback called the Bi-GRU prediction model is used to complete the short-term traffic flow prediction and study its characteristics. The Rectified Adaptive (RAdam) model is adopted to improve the shortcomings of the common optimizer. The cosine learning rate attenuation is also used for the model to avoid converging to the local optimal solution and for the appropriate convergence speed to be controlled. Furthermore, the scientific and reliable model learning rate is set together with the adaptive learning rate in RAdam. In this manner, the accuracy of network prediction can be further improved. Finally, an experiment of the Bi-GRU model is conducted. The comprehensive Bi-GRU prediction results demonstrate the effectiveness of the proposed method.

Vehicular Edge Computing (VEC) is a promising paradigm that leverages the vehicles to offload computation tasks to the nearby VEC server with the aim of supporting the low latency vehicular application scenarios. Incentivizing VEC servers to participate in computation offloading activities and make full use of computation resources is of great importance to the success of intelligent transportation services. In this paper, we formulate the competitive interactions between the VEC servers and vehicles as a two-stage Stackelberg game with the VEC servers as the leader players and the vehicles as the followers. After obtaining the full information of vehicles, the VEC server calculates the unit price of computation resource. Given the unit prices announced by VEC server, the vehicles determine the amount of computation resource to purchase from VEC server. In the scenario that vehicles do not want to share their computation demands, a deep reinforcement learning based resource management scheme is proposed to maximize the profits of vehicles and VEC server. The extensive experimental results have demonstrated the effectiveness of our proposed resource management scheme based on Stackelberg game and deep reinforcement learning.

Knowledge graphs are large graph-structured knowledge bases with incomplete or partial information. Numerous studies have focused on knowledge graph embedding to identify the embedded representation of entities and relations, thereby predicting missing relations between entities. Previous embedding models primarily regard (subject entity, relation, and object entity) triplet as translational distance or semantic matching in vector space. However, these models only learn a few expressive features and hard to handle complex relations, i.e., 1-to-N, N-to-1, and N-to-N, in knowledge graphs. To overcome these issues, we introduce a multi-scale dynamic convolutional network (M-DCN) model for knowledge graph embedding. This model features topnotch performance and an ability to generate richer and more expressive feature embeddings than its counterparts. The subject entity and relation embeddings in M-DCN are composed in an alternating pattern in the input layer, which helps extract additional feature interactions and increase the expressiveness. Multi-scale filters are generated in the convolution layer to learn different characteristics among input embeddings. Specifically, the weights of these filters are dynamically related to each relation to model complex relations. The performance of M-DCN on the five benchmark datasets is tested via experiments. Results show that the model can effectively handle complex relations and achieve state-of-the-art link prediction results on most evaluation metrics.

Knowledge graph (KG) embedding aims to study the embedding representation to retain the inherent structure of KGs. Graph neural networks (GNNs), as an effective graph representation technique, have shown impressive performance in learning graph embedding. However, KGs have an intrinsic property of heterogeneity, which contains various types of entities and relations. How to address complex graph data and aggregate multiple types of semantic information simultaneously is a critical issue. In this article, a novel heterogeneous GNNs framework based on attention mechanism is proposed. Specifically, the neighbor features of an entity are first aggregated under each relation-path. Then the importance of different relation-paths is learned through the relation features. Finally, each relation-path-based features with the learned weight values are aggregated to generate the embedding representation. Thus, the proposed method not only aggregates entity features from different semantic aspects but also allocates appropriate weights to them. This method can capture various types of semantic information and selectively aggregate informative features. The experiment results on three real-world KGs demonstrate superior performance when compared with several state-of-the-art methods.

With the integration of Artificial Intelligence (AI) and Internet of Things (IoT), the Federated Edge Learning (FEL), a promising computing framework is developing. However, there are still unsolved issues on communication efficiency and data security due to the huge models and unreliable transmission links. To address these issues, this paper proposes a novel federated edge learning system, called LightFed, where the edge nodes upload only vital partial local models, and successfully achieve lightweight communication and model aggregation. First, a novel model aggregation method Model Splitting and Splicing (MSS) and a Selective Parameter Transmission (SPT) scheme are proposed. By detecting the updating gradients of local parameters and filtering significant parameters, selective rotated transmission and efficient aggregation of local models are achieved. Second, a Training Filling Model (TFM) is proposed to infer the total data distribution of edge nodes, and train a filling model to mitigate the unbalanced training data without violating the data privacy of individual users. Moreover, a blockchain-powered confusion transmission mechanism is proposed for defending the attacks from external adversaries and protecting the model information. Finally, extensive experimental results demonstrate that our LightFed significantly outperforms the existing FEL systems in terms of communication efficiency and privacy security.

In recent years, Industrial Internet of Things (IIoT) has gradually attracted the attention of the industry owing to its accurate time synchronization, communication accuracy, and high adaptability. As an important data structure, graphs are widely used in IIoT applications, where entities and their relationships can be expressed in the form of graphs. With the widespread adoption of IIoT and cloud computing, an increasing number of individuals or organizations are outsourcing their IIoT graph data to cloud servers to enjoy the unlimited storage space and fast computing service. To protect the privacy of graph data, graphs are usually encrypted before being outsourced. In this article, we propose a search pattern privacy system for approximate shortest distance query of encrypted graphs in IIoT. To realize search pattern privacy, we adopt two noncolluded cloud servers to accomplish different tasks. We leverage the first server to store the encrypted data and perform query operations, and use the second one to rerandomize the contents and shuffle the locations of the queried records. Before queries, we generate the trapdoors by using different random numbers. After queries, we ask the second server to rerandomize the contents of the records that the first server touched. In addition, we shuffle the physical locations of original records by inserting some fake records. In this way, all contents and physical locations of the touched records change, so that the first server cannot distinguish whether two queries are the same or not. To enhance the efficiency on the user side, we further improve this system by moving some heavy workloads from the user to the cloud. The security analysis and the performance evaluation show that our work is secure and efficient.

Influenza poses a significant risk to public health, as evidenced by the 2009 H1N1 pandemic which caused up to 203 000 deaths worldwide. Predicting the spatiotemporal information of disease in the incubation period is crucial because the prime aim of it is to provide guidance on preparing a response and avoid presumably adverse impact caused by a pandemic. This article designs and analyzes a prediction system about influenza-like illness (ILI) from the latent temporal and spatial information. In this system 1) Gaussian function model and multivariate polynomial regression are employed to investigate the temporal and spatial distribution of ILI data; 2) the phase space reconstructed by delay-coordinate embedding is used to explore the dynamical evolution behavior of the 1-D ILI series; and 3) a dynamical radial basis function neural network (DRBFNN) method which is the kernel of the system, is proposed to predict the ILI values based on the correlations between the observations space and reconstructed phase space. The performance analysis of our system shows that the regression equations coupling with spatial distribution information can be used to supplement the missing data, and the proposed DRBFNN method can predict the trends of ILI for the following one year. Furthermore, the prediction system in this article applies a model-free control schemes, i.e., there are no restriction equations between the multivariable inputs and outputs. This prediction system is expected to be used in predicting the output signals, even the chaotic output signals, in meteorology, industry, medicine, economy, and other fields. An example of predicting the Standard & Poors 500 index is given to introduce the application of our proposed system. The trend of open prices of the following eight trading days is well predicted.

Cryptography chips are often used in some applications, such as smart grids and Internet of Things (IoT) to ensure their security. Cryptographic chips must be strictly tested to guarantee the correctness of the encryption and decryption. Scan-based design-for-testability (DFT) provides high test quality. However, it can also be misused to steal the cipher key of cryptographic chips by hackers. In this article, we present a new scan design methodology that can resist scan-based side-channel attacks by the dynamical obfuscation of scan input data and scan output data. The scan test is managed by a test password, which consists of load password and scan password. When the chip enters into the test mode, it is required to apply the test password via some external input ports. Once the correct load password is delivered, the scan password can be loaded into a special shift register. If the scan password is also correct, the chip testing can proceed normally. In case the load password or the scan password is wrong, the data in scan chains cannot be propagated correctly. Specifically, some elusory bits are sneaked into scan chains dynamically. The advantage of the proposed method is that it has no negative impact on design performance and test flow when powerfully protecting cryptographic chips. The area penalty is also acceptably low compared with other schemes.

For distributed estimation arising in the nonlinear least squares (NLLSs) problems over adaptive networks, where every node has the abilities of data processing and learning, only the incomplete local data are exploited by the traditional noncooperative method, thereby resulting in the degradation on estimation performance. In this article, a cooperative diffusion strategy is proposed by using a Gauss–Newton (GN) method in order to fully utilize the diversity of temporal–spatial data on local updates. The proposed algorithm includes two steps, i.e., aggregate then update (ATU), where the aggregating step collects in real time the global information instead of local information due to the diffusion strategy, and the updating step implements the local GN iteration. The resulting ATU diffusion algorithm is a distributed and cooperative system without any increase on communication cost, as compared with the noncooperative version. Based on the detailed convergence analysis for ATU, which is fundamental to the promotion of this algorithm, the sufficient conditions for convergence are derived and the evidences of faster convergence than the noncooperative version are provided. The simulation results confirm the obtained theoretical derivations by applying the ATU algorithm to an NLLS-based target localization problem and show the cooperation gains in many aspects, such as the convergence rate, steady-state accuracy, and robustness to noisy range, step size, node, and link failures.

Internet of Things (IoT) realizes the interconnection of heterogeneous devices by the technology of wireless and mobile communication. The data of target regions are collected by widely distributed sensing devices and transmitted to the processing center for aggregation and analysis as the basis of IoT. The quality of IoT services usually depends on the accuracy and integrity of data. However, due to the adverse environment or device defects, the collected data will be anomalous. Therefore, the effective method of anomaly detection is the crucial issue for guaranteeing service quality. Deep learning is one of the most concerned technology in recent years which realizes automatic feature extraction from raw data. In this article, the integrated model of the convolutional neural network (CNN) and recurrent autoencoder is proposed for anomaly detection. Simple combination of CNN and autoencoder cannot improve classification performance, especially, for time series. Therefore, we utilize the two-stage sliding window in data preprocessing to learn better representations. Based on the characteristics of the Yahoo Webscope S5 dataset, raw time series with anomalous points are extended to fixed-length sequences with normal or anomaly label via the first-stage sliding window. Then, each sequence is transformed into continuous time-dependent subsequences by another smaller sliding window. The preprocessing of the two-stage sliding window can be considered as low-level temporal feature extraction, and we empirically prove that the preprocessing of the two-stage sliding window will be useful for high-level feature extraction in the integrated model. After data preprocessing, spatial and temporal features are extracted in CNN and recurrent autoencoder for the classification in fully connected networks. Empiric results show that the proposed model has better performances on multiple classification metrics and achieves preferable effect on anomaly detection.

The establishment of communication link in Social Internet of Things is not only determined by personal interests, but also by neighbors’ influences. The influence of each neighbor is often independent and different. However, existing communication link prediction methods have not considered the semantic influence of each neighbor separately. Furthermore, the influence of each neighbor may work on multiple semantic levels, which is also not well considered. We propose a deep learning model to embed the multi-level semantic influence of each neighbor separately to predict communication links in SIoT, named as Intelligent Data Semantics (IDS) system. The IDS system captures the semantics of each neighbor’s influence on both local-level and global-level. The multi-level semantic influence integrated with topology information is then used to predict communication links in SIoT. Extensive experiments on four real social networks demonstrate that IDS outperforms the state-of-the-art models.

Location-based recommendation has become a significant method to help people locate fascinating and appealing points of interest (POIs) with the rapid popularity of smart mobile devices and the prevalence of location-based social networks (LBSN). However, the sparsity of the user-POI matrix and the cold-start issue have generated serious challenges, resulting in a substantial decrease in collaborative filtering methods’ recommendation results. In reality, location-based recommendation demands spatiotemporal context awareness. In order to overcome these challenges, we develop an embedding model based on the heterogeneous graph attention network. Geographic influence, social relation and historical check-in influence are captured in a unified way by constructing a user-POI heterogeneous graph. Subsequently, we use the LSTM-based model to learn the category weight of the next POI to select. We are developing a score function to recommend the next POI for users by integrating category weights, user preferences and time impact. We conduct experiments on existing large-scale datasets to evaluate the performance of our model. The results demonstrate our proposal is superior to other rivals. Additionally, our method has been significantly improved compared with other competitive approaches in terms of recommending cold-start POI.

Unsupervised image-to-image translation is to learn a mapping function from one image domain to another with unpaired samples, which is an important task of computer vision. However, current unsupervised image-to-image translation methods only perform well on certain datasets. To handle the limitation, this paper proposes a novel framework termed as CSAGAN which contains a new discriminator structure, a novel attention module, and a new normalized function. The discriminator is an attention-guided feature pyramid discriminator which makes use of low-level and high-level features to determine an image’s realness. The new attention module integrating channel attention and spatial attention can guide generators focus on the most discriminative regions of feature maps to generate high-quality translated images. Moreover, our attention module embedded into generators requires less computation compared with other self-attention methods. In addition, the new normalized function helps generators limberly control the variation of shape, color, and texture through learning parameters. Experimental results indicate that our approach performs better than the current state-of-the-art methods.

Summary The Codelet Model is a fine‐grained event‐driven hybrid parallel model inspired by dataflow, whose computing performance depends on the scheduling policy. An approximate optimal codelet scheduling policy based on the features of the task graphs is important to accelerate the performance of dataflow computer system. Therefore, we have proposed an adaptive genetic scheduling policy (GSP) for codelet by improving a “pure” genetic algorithm (IPGA) for given tasks with complex dependencies. It is verified that the genetic scheduling policy is effective according to bunches of experimental results.

In the field of evolutionary algorithms, Pareto-based algorithms are less effective when more than three objectives are encountered, due to the lack of sufficient selection pressure. In this paper, a Pareto-based many-objective evolutionary algorithm with reference direction and coordinated selection strategy, abbreviated as SPEACSS, is proposed. The algorithm inherits the fitness calculation strategy of the strength Pareto evolutionary algorithm, while it applied an efficient reference direction-based density estimator and a novel selection strategy. Moreover, mating selection and environmental selection are complementary and coordinated in the evolutionary process and have better performance than be used alone. In the criteria of mating selection, a method is given to improve the effectiveness of the parent combination. Experimental results on benchmark functions show that the proposed algorithm is superior to several state-of-the-art designs, and demonstrate the effectiveness of the improved algorithm in balancing diversity and convergence.

With billions of sensing devices are deployed in smart cities to monitor regions of interests and collect large sensing data, the Internet-of-Things (IoT) applications are being widely used in various fields and empower the intelligent smart cities. Due to the smart decision made by IoT applications depends on the reliability of data collection, it is pivotal to collect data from the trust sensing devices. However, how to identify the credibility of sensor nodes to ensure the credibility of data collection is a challenge issue. In this article, an active and traceable trust-based data collection (ATTDC) scheme is proposed to collect trust data in Internet of Thing. The main contribution of this article are as follows: 1) an active trust framework is proposed to quickly obtain the trustworthiness of sensor nodes by using unmanned aerial vehicles (UAVs) with a piggybacking method; 2) in order to accurately obtain the trust degree of the sensor nodes, a traceable trust method of obtaining is proposed in which nodes in the network send data packets by digital signature, Tracing suspicious nodes according to data routing paths to obtain active trust, therefore, the acquisition cost of the network can be effectively reduced; and 3) In order to reduce the acquisition cost of UAV, an ant colony algorithm-based flight path algorithm is designed to reduce the flight path of UAV, and obtain the credibility evaluation of as many nodes as possible. The experimental results show that the ATTDC scheme proposed in this article can identify the trust of the sensing nodes faster and more accurately, ensuring the credibility of data collection.

As an important application of the Internet of Things (IoT) devices, sentiment analysis has been paid more attention with the rapid development of artificial intelligence. As a widely used method in artificial intelligence applications, traditional deep learning methods need massive data for training. However, due to the limitations of hardware, IoT devices have deficiencies in processing big data. In the case of insufficient sample size, how to carry out a machine learning method for IoT devices has become a common concern of the industry. In order to perform sentiment analysis on text with few data samples from the IoT devices, we propose FSLM, which is an intelligent few-shot learning model based on Siamese networks. The FSLM model consists of two self-attention models with the same parameters, which are divided into two parts. First, for two input texts, a self-attention model is used to extract sentiment features, and then the Mahalanobis distance is adopted to measure the similarity between two feature vectors to determine whether they belong to the same category. The FSLM is tested on the Amazon Review Sentiment Classification (ARSC) data set. The extensive experimental results on this data set demonstrate that the FSLM model has better accuracy and robustness for text sentiment analysis than other main existing models with a small number of samples.

Electric vehicle charging is becoming more commonplace, but a number of challenges remain. For example, the wireless communications between vehicle users and aggregators can be subject to exploitation and hence, several authentication schemes have been designed to support varying levels of privacy protection. However, there are a number of limitations observed in existing authentication schemes, and examples include lack of anonymity and not considering charging peak in their design (and consequently, not meeting low energy consumption requirement in smart grid environments). More recently, there have been attempts to utilize Chevyshev chaotic map in the design of authentication mechanism, with the aims of reducing computational costs yet achieving high security. However, the security requirements of Chebyshev polynomials pose new challenges to the construction of Chebyshev chaotic maps-based authentication schemes. To solve these limitations, we propose an efficient Chebyshev polynomials algorithm by adopting a square matrix-based binary exponentiation algorithm to provide secure and efficient Chebyshev polynomial computation. We further construct an energy-efficient authentication and key negotiation scheme for the smart grid environments based on the proposed algorithm. Compared with five other competing schemes, our proposed authentication scheme achieves reduced computational and communication costs. In addition, the ProVerif tool is used to analyze the security of our proposed authentication scheme. The results show that the proposed scheme outperforms these five other schemes in terms of computation and communication overheads while achieving privacy preserving.

UAV-assisted wireless communications facilitate the applications of Internet of Things (IoT), which employ billions of devices to sense and collect data with an on-demand style. However, there are numerous malicious Mobile Data Collectors (MDCs) mixing into the network, stealing or tampering with data, which greatly damages IoT applications. So, it is urgent to build a ubiquitous trust communication system. In this paper, a UAV-assisted Ubiquitous Trust Evaluation (UUTE) framework is proposed, which combines the UAV-assisted global trust evaluation and the historical interaction based local trust evaluation. We first propose a global trust evaluation model for data collection platforms. It can accurately eliminate malicious MDCs and create a clean data collection environment, by dispatching UAVs to collect baseline data to validate the data submitted by MDCs. After that, a local trust evaluation model is proposed to help select credible MDCs for collaborative data collection. By letting UAVs distribute the data verification hash codes to MDCs, the MDCs can verify whether the exchanged data from the interacted MDCs is reliable. Extensive experiments conduct on a real-life dataset demonstrate that our UUTE system outperforms the existing trust evaluation systems in terms of accuracy and cost.

Motivated by the prospects of 5G communications and industrial Internet of Things (IoT), recent years have seen the rise of a new computing paradigm, edge computing, which shifts data analytics to network edges that are at the proximity of big data sources. Although deep neural networks (DNNs) have been extensively used in many platforms and scenarios, they are usually both compute and memory intensive, thus, difficult to be deployed on resource-limited edge devices and in performance-demanding edge applications. Hence, there is an urgent need for techniques that enable DNN models to fit into edge devices, while ensuring acceptable execution costs and inference accuracy. This article proposes an on-demand DNN model inference system for industrial edge devices, called knowledge distillation and early exit on edge (EdgeKE). It focuses on the following two design knobs: first, DNN compression based on knowledge distillation, which trains the compact edge models under the supervision of large complex models for improving accuracy and speed; second, DNN acceleration based on early exit, which provides flexible choices for satisfying distinct latency or accuracy requirements from edge applications. By extensive evaluations on the CIFAR100 dataset and across three state-of-art edge devices, experimental results demonstrate that EdgeKE significantly outperforms the baseline models in terms of inference latency and memory footprint, while maintaining competitive classification accuracy. Furthermore, EdgeKE is verified to be efficiently adaptive to the application requirements on the inference performance. The accuracy loss is within 4.84% under various latency constraints, and the speedup ratio is up to 3.30× under various accuracy requirements.

The gap between the peak-hour Internet and the average level is increasing, which inevitably creates a type of temporary cellular weak coverage when there is a surge in data traffic demand, where any cell-edge device will have a low spectrum efficiency (SE). In this article, we propose a novel incentive architecture based on the dynamic radio frequency charging technology to improve the SE and use the Stackelberg game theory to formulate the problem. In such a model, a small base station (SBS) acts as the leader to offer a desired partition of the resource block obtained by a cell-edge device, while some small energy providers (SEPs) and small virtual access points (SVAPs) that are selected from user equipment act as the followers to make their decisions, respectively, to compete for the free part of such a resource block. Following the potential game rules, all the SEPs compete for a specific free resource part allocated by the SBS, and then, all the SVAPs compete for another nonoverlapping part allocated by the SBS on the basis of the results of the SEPs' potential game. Although our incentive architecture formally has three game stages, it is essentially a two-level Stackelberg game, which is analyzed by using a backward induction method. The theoretical analysis proves the convergence of the above-mentioned game models, and the simulation results demonstrate that the proposed incentive architecture can improve the SE for each cell-edge device.

Intelligent anomaly detection for key performance indicators (KPIs) is important for keeping services reliable in industrial-based cyber-physical systems (CPS). However, it is common in practice for various KPI sampling strategies to be utilized. We experimentally verify that anomaly detection is highly sensitive to irregular sampling, and accordingly go on to investigate low-cost anomaly detection for large-scale irregular KPIs. Irregular KPIs can be classified into four types: equal interval and unequal quantity (EIUQ) KPIs, unequal interval (UI) KPIs, unequal interval with equal duration (UIED) KPIs, and segmented irregular KPIs. In this article, we propose an anomaly detection framework based on these irregular types. Moreover, to handle the various lengths and phase shifts among EIUQ KPIs, we propose a normalized version of unequal cross-correlation, which slides the KPIs to enable finding the most similar position. To avoid high computational costs, we analyze the low-rank feature of KPIs data and propose a matrix factorization-based alignment algorithm for UIED KPIs; this algorithm treats UIED KPIs as an incomplete matrix and recovers the KPIs to align them before performing anomaly detection. Extensive simulations using three public datasets and two real-world datasets demonstrate that our algorithm can achieve a larger F1-score than Minkowski distance and less time than dynamic time warping distance.

Industrial cyber-physical systems are smart systems, which amalgamate the physical processes with computational capabilities to seamlessly capture, monitor and control the entities and scenarios in industrial environments. Among them, event-based industrial cyber-physical systems (EICPSs), such as Meetup and Plancast, have gained rapid developments. EICPSs provide event recommendation service for groups, which alleviates the information overload problem. However, existing group recommendation models in EICPSs focus on how to aggregate the preferences of group members, failing to model the complex and deep influence of contexts on groups. In this article, we propose an attention-based context-aware group event recommendation model (ACGER) in EICPSs. ACGER models the deep, nonlinear influence of contexts on users, groups, and events through multilayer neural networks. Especially, a novel attention mechanism is designed to enable the influence weights of contexts on users/groups change dynamically with the events concerned. Considering that groups may have completely different behavior patterns from group members, we acquire the preference of a group from two perspectives: indirect preference and direct preference. To obtain the indirect preference, we propose a method of aggregating preferences based on attention mechanism. Compared with existing predefined strategies, this method can flexibly adapt the strategy according to the events concerned by the group. To obtain the direct preference, we employ neural networks to learn it from group-event interactions. Furthermore, to make full use of rich user-event interactions in EICPSs, we integrate the context-aware individual recommendation task into ACGER, which enhances the accuracy of learning of user embeddings and event embeddings. Extensive experiments on three real datasets from Meetup and Douban event show that our model ACGER significantly outperforms the state-of-the-art models.

The communication in a 6G-enabled network in a box (NIB) needs to meet the characteristics of fast, convenient, and safe. Secret sharing scheme has become a hot topic nowadays due to unconditional security and simple decryption. At the same time, a quick response (QR) code as a popular carrier has been widely applied in various industrial applications because of the data payload and convenience. Thus, the combination of secret sharing and QR code provides a solution by satisfying the requirements of 6G-enabled NIB. In this article, we design a QR code secret sharing scheme with authentication to protect private data and prevent cheater. In this scheme, a secret image is first divided into a series of shadows based on a polynomial, and authentication bits are generated based on the generated shadows. Then shadows and the authentication bits are embedded into the cover QR codes according to the error correction redundancy and the homomorphism of the Reed-Solomon code in the QR code. In addition, the secret can be restored with the qualified shares and the authentication bits could verify the authenticity of the embedded shadows. Compared with existing schemes, the proposed scheme not only guarantees a high capacity but also embeds more authentication bits to improve the authentication ability. In addition, experimental results have demonstrated that the proposed scheme is both robust and secure.

Due to the fast transmission speed and severe health damage, COVID-19 has attracted global attention. Early diagnosis and isolation are effective and imperative strategies for epidemic prevention and control. Most diagnostic methods for the COVID-19 is based on nucleic acid testing (NAT), which is expensive and time-consuming. To build an efficient and valid alternative of NAT, this article investigates the feasibility of employing computed tomography images of lungs as the diagnostic signals. Unlike normal lungs, parts of the lungs infected with the COVID-19 developed lesions, ground-glass opacity, and bronchiectasis became apparent. Through a public dataset, in this article, we propose an advanced residual learning diagnosis detection (RLDD) scheme for the COVID-19 technique, which is designed to distinguish positive COVID-19 cases from heterogeneous lung images. Besides the advantage of high diagnosis effectiveness, the designed residual-based COVID-19 detection network can efficiently extract the lung features through small COVID-19 samples, which removes the pretraining requirement on other medical datasets. In the test set, we achieve an accuracy of 91.33%, a precision of 91.30%, and a recall of 90%. For the batch of 150 samples, the assessment time is only 4.7 s. Therefore, RLDD can be integrated into the application programming interface and embedded into the medical instrument to improve the detection efficiency of COVID-19.

Sample entropy (SampEn) technologies have been widely applied in diagnosing the faults of industrial systems. However, there are two disadvantages of these technologies. First, all of these technologies measure the distance of two vectors solely based on the maximum distance between the corresponding elements in the two vectors, which is not able to fully reflect the distance of the two vectors. Second, these methodologies measure the similarity of two vectors with either zero or one, which may cause sudden changes in entropy values. Therefore, we proposed a Euclidean distance based multiscale fuzzy entropy (EDM-Fuzzy), which measures the similarity of two vectors with continuous values from zero to one based on the Euclidean distance of the two vectors. The results from the synthetic and real signals demonstrated that EDM-Fuzzy has higher accuracy in measuring the complexity of signals. As a result, EDM-Fuzzy obtains a higher accuracy in detecting the bearing faults than the state-of-the-art entropy technologies.

Automatic recommendation has become an increasingly relevant problem to industries, which allows users to discover new items that match their tastes and enables the system to target items to the right users. In this article, we have proposed a deep learning based fully Bayesian treatment recommendation framework, DVMF, which has high-quality performance and ability to integrate any kinds of side information handily and efficiently. In DVMF, the variational inference technique and the reparameterization tricks are introduced to make DVMF possible to be optimized by the stochastic gradient-based methods, in addition, two novel deep neural networks have been constructed to infer the hyper-parameters of the distributions of latent factors from the knowledge of user and item, which are represented as low-dimensional real-valued vectors retaining primary features. Experimental results on five public databases indicate that the proposed method performs better than the state-of-the-art recommendation algorithms on prediction accuracy in terms of quantitative assessments.

Large-scale data clustering is an essential key for big data problem. However, no current existing approach is “optimal” for big data due to high complexity, which remains it a great challenge. In this article, a simple but fast approximate DBSCAN, namely, KNN-BLOCK DBSCAN, is proposed based on two findings: 1) the problem of identifying whether a point is a core point or not is, in fact, a kNN problem and 2) a point has a similar density distribution to its neighbors, and neighbor points are highly possible to be the same type (core point, border point, or noise). KNN-BLOCK DBSCAN uses a fast approximate kNN algorithm, namely, FLANN, to detect core-blocks (CBs), noncore-blocks, and noise-blocks within which all points have the same type, then a fast algorithm for merging CBs and assigning noncore points to proper clusters is also invented to speedup the clustering process. The experimental results show that KNN-BLOCK DBSCAN is an effective approximate DBSCAN algorithm with high accuracy, and outperforms other current variants of DBSCAN, including ρ-approximate DBSCAN and AnyDBC.

Semi-supervised clustering algorithms have several limitations: 1) the computation complexity of them is very high, because calculating the similarity distances of pairs of examples is time-consuming; 2) traditional semi-supervised clustering methods have not considered how to make full use of must-link and cannot-link constraints. In the clustering, the contribution of a few pairwise constraints to the clustering performance is very limited, and some may negatively affect the outcome; and 3) these methods are not effective to handle high dimensional data, especially for time series data. Up to now, few work touched semi-supervised clustering on time series data. To efficiently cluster large-scale time series data, we first tackle contract time series clustering to produce the most accurate clustering results under a contracted time. We propose a semi-supervised time series clustering framework (STSC), which integrates a fast similarity measure and a constraint propagation approach. Based on the proposed framework, two valid semi-supervised clustering algorithms including fssK-means and fssDBSCAN are designed. Experiments on 11 datasets show that our proposed method is efficient and effective for clustering large-scale time series data.

The selection of cloud resources is important to users which influences their utility directly. To improve the users’ utility of purchased resources, according to the historical bidding information, we present data-driven cloud resource procurement (CRP) auctions which can help the resource buyer make an optimized selection for cloud resources. First, we design two procurement auction mechanisms with personalized reserve prices for CRP: Lazy-CRP and Eager-CRP, in which the reserve prices are set by the broker before the auction based on the broker’s knowledge about costs of cloud providers. Then, given the assumption that each cloud provider is myopic, we propose the optimal learning algorithms of personalized reserve prices for the Lazy-CRP approach, and 2-approximation learning algorithm for the Eager-CRP approach. Both of two algorithms can be performed even if the cost distribution functions of cloud providers are nonidentical. By comparing our mechanisms with Vickery–Clark–Groves CRP (VCG-CRP) in simulation, the results show that our proposed mechanism is very suitable for occasions where the number of providers is very small. According to the results of the simulation, the total utilities obtained by the data-driven procurement schemes are significantly higher than VCG-CRP when the number of cloud providers is less than four. When there are only two providers, the increased total utility of our mechanism can be up to 80% of that obtained by VCG-CRP.

The issue of impulse noise detection and reduction is a critical problem for image processing application systems. In order to detect impulse noises in corrupted images, a statistic named local consensus index (LCI) is proposed for quantitatively evaluating how noise free a pixel is, and then an impulse noise detection scheme based on LCI is introduced. First, the similarity between arbitrary two pixels in an image is quantified based on both their geometric distance and intensity difference, and the LCI of arbitrary pixel is calculated by summing all the similarity values of pixels in its neighborhood. As a new statistic, the value of LCI indicates the local consensus of the concerned pixel regarding its neighbors and could also tell whether a pixel is noise free or impulsive. Therefore, LCI can be directly used as an efficient indicator of impulse noise. Furthermore, to improve the performance of impulse noise detection, different strategies are applied to the pixels at flat regions and the ones with complex textures, since distributions of LCI value within those regions are totally different. As for impulse noise filtering, a hybrid graph Laplacian regularization (HGLR) method is introduced to restore the intensities of those pixels degraded by impulse noise. We conduct extensive experiments to verify the effectiveness of our impulsive noise detection and reduction method, and the results show that the proposed method outperforms the state-of-the-art techniques in terms of impulse detection and noise removal.

In this paper, we consider the nonlinear least squares (NLLSs) problems in adaptive networks, where a collection of nodes with adaptation ability by learning are required to estimate a global vector parameter by minimizing the specified convex cost function. Although the global Gauss-Newton (GN) method is an excellent candidate for solving such problems, many challenges need to be addressed for practical realization due to its natures of centralization and noncooperation. Without specialized design for routing, we motivate and propose new diffusion GN methods with cooperative strategy among local neighborhoods. The good performances of diffusion cooperation schemes have been proved in different literatures, such as distribution, robustness, and easy implementation. The proposed cooperative diffusion strategies are named as aggregation-then-update (ATU) and update-then-aggregation (UTA), which reach fully information diffusion across network and consist of two steps in a reversible way including aggregation of local estimates and local GN update. Although all implementations are local, the cooperation between nodes is network wide. Based on the steady-state equilibria theory in the nonlinear discrete dynamical system, the convergence analysis of proposed algorithms is provided. The results show that the global convergence can be achieved when the sufficient conditions are satisfied. We also provide performance comparisons and analysis together with simulation to confirm the applicability and effectiveness of proposed diffusion GN algorithms.

Until now, the pseudonym ID and one-time hash chain as the key technologies solve anonymous and forward secrecy properties, respectively, in lightweight authentication protocols for wireless sensor networks (WSNs) environment. However, both techniques have to face the limitation of asynchronization attack, which will cause the pseudonym ID and one-time hash chain value between participants to be loss of synchronization when the transmitted message is blocked. Most recently, a serial of lightweight anonymous authentication scheme with forward secrecy (LAASFS) have been built to resolve this problem. Unfortunately, they failed to handle this question satisfactorily. Besides, most of them are still vulnerable to smart card loss attack (SCLA). In this article, to address these issues and make more effective, we combine pseudonym ID, one-time hash chain and tag techniques to construct an LAASFS for WSNs environment. The proposed LAASFS can be adaptive resilience of asynchronization attacks. In addition, it is also able to resist a variety of known attacks like SCLA and wrong password login attack. Formal analyses are taken by BAN logic and ProVerif tool to demonstrate the security of properties of our LAASFS. Compared with several previous related schemes, the proposed LAASFS possesses obvious advantages in computation and communication costs.

Once defined, the access control policies and regulations would never be changed in a running and state transition process. However, it will give attackers the possibility of discovering vulnerabilities in the system, and the control systems lack the ability of dynamic perception of security state and risk, causing the systems to be exposed to risks. In this article, a dynamic Bell-LaPadula (BLP) model is proposed. The conditional random field (CRF) is introduced into the BLP model to optimize the rules. First, the model formalizes the security attributes, states of system, transition rules, and constraint models on the basis of the state transition of CRFs. After the historical system access logs are processed as the original dataset, a feature selection method is proposed to extract the requests and current states as feature vectors. Second, this article presents a rules training algorithm based on L-BFGS to implement the study and training of datasets, and then marks the logs in the test set through Viterbi algorithm automatically. On the base of these, a rule generation algorithm is proposed to dynamically adjust the access control rules based on the current security status and events of the system. Third, the security of CRFs-BLP is proved by theoretical analysis. Finally, the validity and accuracy of the model are verified by estimating the value of the precision, recall, and F1-score. As the system threats are shown to be decreased obviously from these experiments, this dynamic model can decrease the vulnerabilities and risk effectively.

The blockchain‐based management system has been regarded as a novel way to improve the efficiency and safety of Vehicular Ad Hoc Networks (VANETs). A blockchain‐based scheme’s performance depends on blockchain nodes’ computing power composed from the road‐side unit (RSU). However, the throughput of blockchain‐based application in VANETs is limited by the network bandwidth. A single blockchain cannot record large‐scale VANETs’ data. In this paper, we design an atomic cross‐chain swap‐based management system (ACSMS) to boost the scalability of blockchain‐based application in VANETs. The blockchain‐based public‐key encryption with keyword search is further introduced to protect user privacy. The analysis shows that ACSMS achieves cross‐chain swap without loss of CAV security privacy. The simulation results show that our method can realize multiple blockchain‐based applications in VANETs.

In the practical application, sensor data collection is an essential means for the system to perceive the intrinsic features of data. The anomaly detection of data points can improve data quality and explore the potential information of data. The anomaly detection can be classified as two basic types, that is, classification and clustering. Those methods usually depend on the spatial correlation of data and have high computation complexity, so they are not suitable for the smart home and another mini‐Internet of Things (IoT) environment. To overcome these problems, we propose a novel method for anomaly detection. In this paper, we first define the temporal and spatial feature of data flows; then, a time series denoising autoencoder (TSDA) is proposed to extract the discriminative high‐dimensional characteristics to represent the data points. Moreover, a probability statistics‐based anomaly detection model (PADM) was proposed for identifying the abnormal data. Extensive experimental results demonstrated that our method has fewer parameters and is easy to adjust and optimize. More importantly, our approach has higher precision and recall rate than the gradient boosted decision tree and XGBoot.

On a daily basis people waste so many items, and what better way to help them from wasting but to create an app that tracks their groceries, creates less waste in the world and saves money at the same time in mobile networks. People waste food on average 1 pound per person each day they live. The application GAPP will cut down costs and waste in everyday life. People also misplace or lose track of items daily. Location of items is a big deal and knowing where they are at all times on a device would help.

The Spatio temporal scale varying characteristics of aridity and thus the dominance of this method within the present year is notable due to global climate change during this investigation, we proposed to work out the trend of the Aridity index using non-parametric methods like Mann-Kendall and Sen's slope approaches for testing. In Contrast, the Aridity index is computed by using Jensen formula to formulate, which is worldwide accepted. At the same time, ETo can be calculated using the well-known scheme by the Food and Agriculture Organization (FAO) through Penman-Monteith FAO-56 (PM). We used 10-year data (2000-2009) of 12 climate locations positioned in Khyber Pakhtunkhwa (KP) Province, Pakistan. We acknowledged an increasing drift within the Aridity index rate had been observed mostly in spring, summer, and winter, with a downward tendency through fall. Furthermore, the statistical investigation demonstrations an increasing drifts in AI during spring and winter. In conclusion, we presented an efficient procedure which may, therefore, benefit by including in irrigation, water resource management, and meteorological modeling for future enlargement.

As an important positioning source of indoor positioning technology, Wi‐Fi signals have attracted the attention of researchers for a long time. Fingerprint positioning can solve the problems caused by non‐line‐of‐sight propagation and multipath effects. To improve the accuracy of Wi‐Fi indoor positioning, this study proposes an indoor positioning algorithm based on fine‐grained channel state information (CSI) and convolutional neural network (CNN). CSI is a kind of observable measurement that better describes the nature of Wi‐Fi signal propagation than received signal strength indication. This method uses the subcarrier amplitude and phase difference information extracted from CSI data to establish fingerprints. The clustering method is used to analyse the number of clusters of fingerprint data, and the fingerprint database is divided into two sub‐databases according to the threshold. CNNs with the same network structure are used to train the two kinds of fingerprint sub‐databases. In the positioning stage, the sub‐database to which the data to be measured belongs is determined according to the calibration algorithm, and the corresponding CNN model is used to estimate the position. Experiments are performed in a typical indoor environment. Compared with existing fingerprint‐based positioning methods, this method has higher positioning accuracy.

As the industrial sector is becoming ever more flexible in order to improve productivity, legacy interfaces for industrial applications must evolve to enhance efficiency and must adapt to achieve higher elasticity and reliability in harsh manufacturing environments. The localization of machines, sensors and workers inside the industrial premises is one of such interfaces used by many applications. Current localization-based systems are unable to deal with highly variable conditions, meaning that the solutions working well in stationary systems suffer from considerable difficulties in harsh environments, such as factories. As a result, the precision of localization techniques is not satisfactory in most industrial applications. This paper fills in the existing gap between static approaches and dynamic indoor positioning systems, by presenting a solution adapting the system to highly changeable conditions. The proposed solution makes use of a Machine Learning-based feedback loop that learns the variability of the environment. This feedback makes continuous fingerprint calibration feasible even in the presence of different machines and Industrial Internet of Things sensors that introduce variations to the electromagnetic environment. This paper also presents a comprehensive indoor positioning system solution that reduces complexity of hardware, meaning that a multi-standard-transceiver infrastructure may be adopted with reduced capital and operational expenditures. We have developed the system from scratch and have conducted an extensive range of testbed experiments showing that the multi-technology transceiver feature is capable of increasing positioning accuracy, as well as of introducing permanent fingerprints calibration at harsh industrial premises.

Access control is an essential feature of industrial software systems security mechanisms. Role-based access control (RBAC), which is likely the most popular access-control technique, specifies “user roles” and associates each role with “permissions” to access distinct system functionalities. These role-permissions assignment rules, as well as the types of system users and system functionalities, evolve over time. In this paper, we describe a methodology for analyzing and understanding the RBAC-configuration evolution, its relation to the overall evolutionary lifecycle of industrial systems, and its impact on security vulnerabilities from which the system may suffer. Our methodology considers two different sources of information regarding the RBAC-configuration evolution: 1) the role-permissions matrices of subsequent system versions; and 2) the corresponding concept lattices, implied by these matrices. By examining the evolution of these two system properties, developers can easily notice which versions involve more and more complex RBAC-configuration changes that may indicate higher security risks. We demonstrate our methodology using a study of four popular real-world systems: 1) MediaWiki; 2) Moodle; 3) Joomla; and 4) WordPress. Our findings show that the proposed metrics have strong, positive linear correlations with the security vulnerabilities' properties.

Vision systems are the core information collection module in outdoor industrial systems such as factory inspection robots. However, haze greatly reduces working efficiency. Existing dehazing methods have two problems-first, they are not specifically designed for the industrial systems; second, these methods include several assumptions in their design processes and imaging models, leading to unsatisfactory results. In this article, an approach for single image dehazing is proposed to improve the efficiency of outdoor vision-based systems. First, a novel haze imaging model is proposed based on the dichromatic atmospheric scattering model. It considers the effects of multiple scattering and involves fewer assumptions. Then a data-driven technique called sparse representation is used to solve this model. Considering a haze image, a distorted and blurred version of a fine image, every patch is presented using dedicatedly prepared over-complete dictionaries and is traced back to a haze-free image. Quantitative and qualitative comparisons on a number of real-world haze images demonstrate that the proposed approach not only is more stable but also leads to better dehazing results.

With the enormous growth of the content and users of social network, user portrait based on social network have been widely used in industrial applications such as artificial intelligence, recommendation systems, etc. The issue has been extensively studied, and most of them have focused on user attribute mining and behavior prediction. However, there is little research on the intrinsic relationship between big data-based interests and skills and occupational adaptability. To clarify this issue, in this article, we first collect a large amount of user data from LinkedIn. Then, we filter high-frequency interests and skills, and explore relevant characteristics of interests and skills through relevant analytical methods, found that there are many association rules. Next, the users are grouped according to the characteristics of occupational adaptability, and the relationship between the association rules of interests and skills and user adaptability is studied. Finally, designed an occupational adaptive classifier based on association rules. This article reveals the connections and dependencies between human interests and professional skills, as well as the impact of these association rules on user career adaptability, also discusses the prospects of these results in industrial applications. We hope that our research results will provide a solid theoretical foundation for other areas of research and industrial applications, such as career adaptability judgment, interest training, career development, career recommendation, etc.

In the era of Industrial Internet of Things (IIoT), huge amounts of data are generated, and companies are highly motivated to store the data on cloud servers for cost saving and efficient application. However, the IIoT data are always of great value. The direct outsourcing of such data can leak the important information of the companies and cause great business losses. A straightforward solution is to encrypt the data by using standard encryption methods before outsourcing. Nevertheless, this will make data utilization quite inconvenient. This paper focuses on the secure process of image data on cloud servers. Images are stored on cloud servers in encrypted form, and the local binary pattern feature can be directly extracted from the encrypted images for applications. The security analysis and experimental results demonstrate the security and effectiveness of our scheme.

In the era of data deluge, data analysis has become a key task for many industrial applications, e.g., master data management, and data integration. In particular, similarity join is an important primitive operator to support data analysis, which is to find similar pairs based on similarity functions and thresholds. In this article, we first propose a new similarity join operation called the dynamic skyline join without having to specify any similarity function or similarity threshold, which measures the similarity through multicriteria optimization. The dynamic skyline join operator makes the similarity join more flexible to support different criteria in multidimensional space. However, it is nontrivial to achieve dynamic skyline joins as both join operations and dynamic skyline queries are computationally complex in the increasing volume of real-world data. Therefore, we further propose Grid-SkyJoin, a framework to enable efficient parallel dynamic skyline joins on a shared-nothing cluster. Specifically, we use a grid partitioning to facilitate the data filtering and grouping strategies to provide load balancing and reduce the number of replicas. We also propose a multilevel filtering scheme to prune away a large fraction of unpromising points that do not fit into join results without actual join operations. Extensive experiments using benchmark datasets demonstrate that our filtering scheme can greatly reduce the number of data points to be joined, and our approach is about two times faster compared with the straightforward method in average.

Decision-making is a key component of industrial control system (ICS) security. However, due to the stability and real-time requirements of ICSs, current decision-making approaches typically designed for IT systems are not entirely suitable for ICSs. In this paper, with consideration of the characteristics of ICSs, a risk-based multistep dynamic decision-making approach for protecting ICSs is proposed. Following this proposal, multiple models, including multilayer Bayesian network, process model, and attack-defense strategy model are built first. On this basis, a state controller is designed to ensure the safe degradation/upgradation of ICSs. Finally, a game theory-based optimal defense strategy generation approach is presented. To verify the effectiveness of the proposed approach, a simulation on a simplified chemical reactor control system is conducted in MATLAB. The simulation results clearly demonstrate that the proposed dynamic decision-making approach has the ability to generate the optimal defense strategy to minimize system loss.

In recent years, fingerprint authentication systems have been extensively deployed in various applications, including attendance systems, authentications on smartphones, mobile payment authorizations, as well as various safety certifications. However, similar to the other biometric identification technologies, fingerprint recognition is vulnerable to artificial replicas made from cheap materials, such as silicon, gelatin, etc. Thus, it is especially necessary to distinguish whether a given fingerprint is a live or a spoof one prior to such authentication. In order to solve the problems above, a novel local descriptor named Weber local binary descriptor for fingerprint liveness detection (FLD) has been proposed in this paper. The method consists of two components: the local binary differential excitation component that extracts intensity-variance features and the local binary gradient orientation component that extracts orientation features. The co-occurrence probability of the two components is calculated to construct a discriminative feature vector, which is fed into support vector machine (SVM) classifiers. The effectiveness of the proposed method is intuitively analyzed on the image samples and numerically demonstrated by Mahalanobis distance. Experiments are performed on two public databases from FLD competitions from 2011 and 2013. The results have proved that the proposed method obtains the best detection accuracy among the existing image local descriptors in FLD.

Aiming at the shortcomings of some existing interval-valued intuitionistic fuzzy entropy, this paper proposes an interval-valued intuitionistic fuzzy entropy, which contains not only the interval distance between membership and nonmembership but also the average hesitancy information. On this basis, the impact evaluation of microblog users is studied. Through the multilevel decomposition of user influence, the coverage of microblog, user interaction, and user activity are constructed as the first level indicators, and nine secondary indicators are selected as the comprehensive evaluation index system of microblog influence. Considering the highly dynamic and unstructured characteristics of social network data, the idea of interval-valued intuitionistic fuzzy is introduced to transform the evaluation of social network users’ influence into interval-valued intuitionistic fuzzy multiattribute group decision-making problem. Finally, the model is applied to dynamic evaluation of Sina Weibo users’ influence to verify the effectiveness of the model.

The objective of the study is to develop a framework for automatic breast cancer detection with merging four imaging modes. Attempts were made for tumor classification and segmentation; using a multi-parametric Magnetic Resonance Imaging (MRI) method on breast tumors. MRI data of the breast were obtained from 67 subjects with a 1.5T-MRI scanner. Four imaging modes: were T1 weighted, T2 weighted, Diffusion Weighted and eTHRIVE sequences, and dynamic-contrast-enhanced(DCE)-MRI parameters are acquired. The proposed four-mode linkage backbone in tumor classification, which overcomes the limitations of single-modality image detection and simulates actual diagnosis processes by clinicians, achieves the accuracy of 0.942. The proposed automatic segmentation approach is performed by a refined U-Net architecture, and the result improved segmentation performance significantly. The combination of four-mode linkage classification backbone and improved segmentation network for breast cancer detection forms a computer-aided detection (CAD) system that corresponds to the actual clinical diagnosis work.

In wireless sensor networks, the node reliability can affect the reliability of data transmission. To analyze the node reliability, this paper defines both betweenness centrality and dependency degree for a node to reflect its global reliability and local reliability, respectively. Based on the above definition, a global and local reliability-based routing (GLRR) protocol is proposed to guarantee the reliability of data transmission between a source and destination node in network. In GLRR, at first, some nodes, usually with greater betweenness centrality among their neighbors in a limited range, will be selected as the backtracking node. Moreover, all backtracking nodes then construct their backtracking paths from themselves to the source node separately, and each node on a backtracking path should calculate the dependency degree on its previous hop node. All the betweenness centrality and dependency degree will be forwarded to the source node along the backtracking paths, and be combined to design the routing metric, based on which, the source node can calculate an optimal backtracking path to forward packets to the corresponding backtracking node, meanwhile, this backtracking node will act as a new source node to launch another routing process until the packets be forwarded to the destination node. At last, the simulation results demonstrate that the proposed protocol is superior to the classical algorithms in terms of the network reliability and the network efficiency.

Compared to traditional distributed computing environments such as grids, cloud computing provides a more cost-effective way to deploy scientific workflows. Each task of a scientific workflow requires several large datasets that are located in different datacenters, resulting in serious data transmission delays. Edge computing reduces the data transmission delays and supports the fixed storing manner for scientific workflow private datasets, but there is a bottleneck in its storage capacity. It is a challenge to combine the advantages of both edge computing and cloud computing to rationalize the data placement of scientific workflow, and optimize the data transmission time across different datacenters. In this study, a self-adaptive discrete particle swarm optimization algorithm with genetic algorithm operators (GA-DPSO) was proposed to optimize the data transmission time when placing data for a scientific workflow. This approach considered the characteristics of data placement combining edge computing and cloud computing. In addition, it considered the factors impacting transmission delay, such as the bandwidth between datacenters, the number of edge datacenters, and the storage capacity of edge datacenters. The crossover and mutation operators of the genetic algorithm were adopted to avoid the premature convergence of traditional particle swarm optimization algorithm, which enhanced the diversity of population evolution and effectively reduced the data transmission time. The experimental results show that the data placement strategy based on GA-DPSO can effectively reduce the data transmission time during workflow execution combining edge computing and cloud computing.

In this paper, we propose an approach to estimate and quantify the degree of extraversion for crowd motion based on individual trajectories. Extraversion is a typical personality that is often observed in human behaviors. We present a composite motion descriptor, which integrates the basic motion information and social metrics, to describe the extraversion of each individual in a crowd. In order to train a universal scoring function that can measure the degrees of extraversion, we incorporate the active learning technique with the relative attribute approach based on the social grouping behavior in crowd motions. In addition, we demonstrate the performance of the proposed method by measuring the degree of extraversion for real individual trajectories in a crowd and analyzing crowd scenes from a real-world dataset.

In a noisy manufacturing environment, range-based target localization for wireless sensor networks (WSNs) experiences various variations in range measurements, thus causing the unsatisfactory results. Starting from an empirical observation on the existing various industrial noise distribution, in this paper, a diffusion Gauss–Newton (GN) algorithm with cooperation strategy is proposed for solving target localization non linear-least-squares problem in a WSN. The proposed algorithm has an equalization effect on the unbalance noise distribution over the network by aggregating the global estimates into local GN update via diffusion strategy. When facing a hostile industrial environment where the ambient noise is heterogeneous or has the sudden changes across partial nodes, the significant performance degradation is produced by diffusion GN. To solve the problem, we propose further an improved version of diffusion GN, which is adaptive to sudden changes on noisy range measurements. Instead of using a static combiner, the new algorithm leverages the evolutionary game theory to assign a time-varying weight for the estimate from each neighboring node based on the individual range error. Consequently, the good estimates from the neighbors with high SNR have a larger weight in the combiner than the bad estimates caused by the low SNR or high noise. We also propose a simple but effective energy-accuracy tradeoff scheme by using a sigmoidal utility function. Some simulation examples show that the standard diffusion GN is effective in stationary noise and its improved version provides the adaptation to changing noisy environment. The effectiveness of energy–accuracy tradeoff is also validated.

Automatic recommendation has become an increasingly relevant problem to industries, which allows users to discover new items that match their tastes and enables the system to target items to the right users. In this paper, we propose a deep learning (DL) based collaborative filtering framework, namely, deep matrix factorization (DMF), which can integrate any kind of side information effectively and handily. In DMF, two feature transforming functions are built to directly generate latent factors of users and items from various input information. As for the implicit feedback that is commonly used as input of recommendation algorithms, implicit feedback embedding (IFE) is proposed. IFE converts the high-dimensional and sparse implicit feedback information into a low-dimensional real-valued vector retaining primary features. Using IFE could reduce the scale of model parameters conspicuously and increase model training efficiency. Experimental results on five public databases indicate that the proposed method performs better than the state-of-the-art DL-based recommendation algorithms on both accuracy and training efficiency in terms of quantitative assessments.

Security is a pivotal issue for the development of Cyber Physical Systems (CPS). The trusted computing of CPS includes the complete protection mechanisms, such as hardware, firmware, and software, the combination of which is responsible for enforcing a system security policy. A Trust Detection-based Secured Routing (TDSR) scheme is proposed to establish security routes from source nodes to the data center under malicious environment to ensure network security. In the TDSR scheme, sensor nodes in the routing path send detection routing to identify relay nodes’ trust. And then, data packets are routed through trustworthy nodes to sink securely. In the TDSR scheme, the detection routing is executed in those nodes that have abundant energy; thus, the network lifetime cannot be affected. Performance evaluation through simulation is carried out for success of routing ratio, compromised node detection ratio, and detection routing overhead. The experiment results show that the performance can be improved in the TDSR scheme compared to previous schemes.

Millions of dedicated sensors are deployed in smart cities to enhance quality of urban living. Communication technologies are critical for connecting these sensors and transmitting events to sink. In control systems of mobile wireless sensor networks (MWSNs), mobile nodes are constantly moving to detect events, while static nodes constitute the communication infrastructure for information transmission. Therefore, how to communicate with sink quickly and effectively is an important research issue for control systems of MWSNs. In this paper, a communication scheme named first relay node selection based on fast response and multihop relay transmission with variable duty cycle (FRAVD) is proposed. The scheme can effectively reduce the network delay by combining first relay node selection with node duty cycles setting. In FRAVD scheme, first, for the first relay node selection, we propose a strategy based on fast response, that is, select the first relay node from adjacent nodes in the communication range within the shortest response time, and guarantee that the remaining energy and the distance from sink of the node are better than the average. Then for multihop data transmission of static nodes, variable duty cycle is introduced novelty, which utilizes the residual energy to improve the duty cycle of nodes in far-sink area, because nodes adopt a sleep-wake asynchronous mode, increasing the duty cycle can significantly improve network performance in terms of delays and transmission reliability. Our comprehensive performance analysis has demonstrated that compared with the communication scheme with fixed duty cycle, the FRAVD scheme reduces the network delay by 24.17%, improves the probability of finding first relay node by 17.68%, while also ensuring the network lifetime is not less than the previous researches, and is a relatively efficient low-latency communication scheme.

Pervasive sensing devices make an unprecedented increase in data sensing, collection, and processing in edge network and they form the edge content server system. The edge content server system combines the content-centric network (CCN) to form a huge content propagation system which makes it challenging to achieve efficient content propagation. Different from IP-based, host-oriented Internet architecture, the CCN systems focus on the information that is contained in network and directly accessible, providing more secure and flexible Internet services. This emerging network architecture supports a number of novel applications, such as common interests sharing, mobile data offloading, and information dissemination without Internet access. In this paper, an analytical framework is proposed to address the problem of content propagation among users with the same interests in leveraging location-based social networks, where the check-in patterns of users are recorded. Particularly, we propose a content propagation effectiveness quantitative model that considers the distance between users, users' interests, and contact rates to formulate the propagation effect and latency. We also apply our framework to two real-world datasets for the evaluation of its effectiveness. Compared with previous studies, our simulated annealing-based algorithm can greatly improve the effects by as much as 25.4%-65.6%, and the contents can be disseminated faster by about 24.6%-57.8%.

Cyber physical systems (CPS) is a tightly coupled integration and interaction between computational and physical components. In many cases, information collection in CPS is provided through a group of distributed sensors and all of them change continuously with time. Thus the sensor information is usually in the form of time series. One particularly interesting application in time series analysis is use of complex networks to represent and study behaviors of system. Complex networks has been playing an important role for analyzing complex systems as it helps understanding the topology structure of systems with different interacting units. In this paper, we proposed a reliable method for constructing complex networks from multivariate time series (MTSs) in the cases of single and multisensor based on information geometry theory, which allows the information in the time series to be extracted by analyzing the associated complex network. We first estimate covariance matrices and then a geodesic-based distance between the covariance matrices is introduced. Consequently, the network can be constructed on a Riemannian manifold where the nodes and edges correspond to the covariance matrix and the geodesic-based distance, respectively. The proposed method provides us with a nonlinear relationship and intrinsic geometry viewpoint to understand the MTSs and also an alternative approach to fuse, model, represent, and visualize the multisensor data in CPS. A number of experimental studies and numerical examples are presented to demonstrate the generality and the effectiveness of our approach with both synthetic and real datasets.

Inspired by security applications in the industrial Internet of things, this paper focuses on the usage of impulsive neural network (NN) synchronization technique for intelligent image protection against illegal swiping and abuse. A class of nonlinear interconnected NNs with transmission delay and random impulse effect is first formulated and analyzed in this paper. In order to make network protocols more flexible, a randomized broadcast impulsive coupling scheme is integrated into the protocol design. Impulsive synchronization criteria are then derived for the chaotic NNs in the presence of nonlinear protocol and random broadcast impulse, with the impulse effect discussed. Illustrative examples are provided to verify the developed impulsive synchronization results and to show its potential application in image encryption and decryption.

With the evolution of information, communications, and technologies, modern industrial control systems (ICSs) face more and more cybersecurity issues. This leads to increasingly severe risks in critical infrastructure and assets. Therefore, risk analysis becomes a significant yet not well investigated topic for prevention of cyberattack risks in ICSs. To tackle this problem, a dynamic impact assessment approach is presented in this paper for risk analysis in ICSs. The approach predicts the trend of impact of cybersecurity dynamically from full recognition of asset knowledge. More specifically, an asset is abstracted with properties of construction, function, performance, location, and business. From the function and performance properties of the asset, object-oriented asset models incorporating with the mechanism of common cyberattacks are established at both component and system levels. Characterizing the evolution of behaviors for single asset and system, the models are used to analyze the impact propagation of cyberattacks. Then, from various possible impact consequences, the overall impact is quantified based on the location and business properties of the asset. A special application of the approach is to rank critical system parameters and prioritize key assets according to impact assessment. The effectiveness of the presented approach is demonstrated through simulation studies for a chemical control system.

With the increasing deployment of data network technologies in industrial control systems (ICSs), cybersecurity becomes a challenging problem in ICSs. Dynamic cybersecurity risk assessment plays a vital role in ICS cybersecurity protection. However, it is difficult to build a risk propagation model for ICSs due to the lack of sufficient historical data. In this paper, a fuzzy probability Bayesian network (FPBN) approach is presented for dynamic risk assessment. First, an FPBN is established for analysis and prediction of the propagation of cybersecurity risks. To overcome the difficulty of limited historical data, the crisp probabilities used in standard Bayesian networks are replaced in our approach by fuzzy probabilities. Then, an approximate dynamic inference algorithm is developed for dynamic assessment of ICS cybersecurity risk. It is embedded with a noise evidence filter in order to reduce the impact from noise evidence caused by system faults. Experiments are conducted on a simplified chemical reactor control system to demonstrate the effectiveness of the presented approach.

Density-based clustering is one of the most popular paradigms of existing clustering approaches, most approaches of this kind, such as DBSCAN, recognize clusters of data characterized by a fixed scanning radius. However, some flaws are caused by the fixed scanning radius, e.g., the determination of a proper scanning radius is nontrivial. In order to solve these problems, we revise DBSCAN, Meanshift, DPeak, etc. based on two new features, i.e., effective radius and density heat (DHeat). Generally, we name these revised clustering algorithms as DHeat. The underlying idea is based on two assumptions: 1) the existence of clusters is raised by the nonuniformity of data distribution, and the density of one data point within its r-neighborhood is proportional to the volume of the neighborhood provided the density distribution is uniform and 2) each cluster can be divided into different density layers, such as edges, shallow inner, deep inner, etc.; the deeper inner of a point locates, the higher density of that point. The experiments conducted on various test cases show that the advantage of DHeat lies in its good performance and the self-adapting scanning radius.

The recent emergence of mobile cloud computing has enabled mobile users to offload computing tasks from mobile devices to nearby cloudlets, so as to reduce energy consumption and improve application performance. In this paper, we consider the problem of maximizing the profit of the cloudlets' managing platform that receives computing requests from mobile users and fulfils these requests by leveraging computing service of participating cloudlets. However, it is very challenging to maximize the operating profit for such a managing platform, due to unpredictable arrival of user requests, dynamic participation of mobile cloudlets, and complexity in computing resource allocations. Based on the Lyapunov optimization technique combined with the technique of weight perturbation, we introduce a new stochastic control algorithm that makes online decisions on computing request admission and dispatching, computing service purchasing, and computing resource allocation. Different from traditional techniques, this algorithm does not require any statistical knowledge of relevant system dynamics, and is efficient for implementation in practice. Theoretical analysis and simulation results have demonstrated both the profit optimality and the system stability achieved by the proposed control algorithm.

The recent advances of compressive sensing (CS) have witnessed a great potential of efficient compressive data gathering (CDG) in wireless sensor network systems (WSNSs). However, most existing work on CDG mainly focuses on multihop relaying strategies to improve the performance of data gathering. In this paper, we propose a mobile CDG scheme including a random walk-based algorithm and a kernel-based method for sparsifying sensory data from irregular deployments. The proposed scheme allows a mobile collector to harvest data by sequentially visiting a number of nodes along a random path. More importantly, toward building the gap between CS and machine learning theories, we explore a theoretical foundation for understanding the feasibility of the proposed scheme. We prove that the CS matrices, constructed from the proposed random walk algorithm combined with a kernel-based sparsity basis, satisfy the restricted isometry property. Particularly, we also show that m = O(klog(n/k)) measurements collected by a mobile collector are sufficient to recover a k-sparse signal and t = O(klog(n/k)) steps are required to collect these measurements in a network with n nodes. Finally, we also present extensive numerical results to validate the effectiveness of the proposed scheme by evaluating the performance in terms of energy consumption and the impact of packet losses. The numerical results demonstrate that the proposed scheme is able to not only significantly reduce communication cost but also combat unreliable wireless links under various packet losses compared to the stateof-the-art schemes, which provides an efficient alternative to data relaying approaches for CDG in WSNS.

The ultradensification deploying for cellular networks is a direct and effective method for the improvement of network capacity. However, the benefit is achieved at the cost of network infrastructure investment and operating overheads, especially when there is big gap between peak‐hour Internet traffic and average one. Therefore, we put forward the concept of virtual cellular coverage area, where wireless terminals with high‐end configuration are motivated to enhance cellular coverage quality by both providing RF energy compensation and rewarding free traffic access to Internet. This problem is formulated as the Stackelberg game based on three‐party circular decision, where a Macro BS (MBS) acts as the leader to offer a charging power to Energy Transferring Relays (ETRs), and the ETRs and their associating Virtual Access Points (VAPs) act as the followers to make their decisions, respectively. According to the feedback from the followers, the leader may readjust its strategy. The circular decision is repeated until the powers converge. Also, the better response algorithm for each game player is proposed to iteratively achieve the Stackelberg‐Nash Equilibrium (SNE). Theoretical analysis proves the convergence of the proposed game scheme, and simulation results demonstrate its effectiveness.

This study takes an intuitive step to develop the user‐convenient smart vehicle parking system (SVPS), a smart system able to manage the massive crowd of vehicles during parking searching and do the better jobs of parking reservation and management, with the shorter‐path processing tactics. For that, this study inclusively employed the mapping strategy, where the system parking points are prevalent, to assist the users to get the parking information fast and conveniently. This study is comprised of the several parking points systematically spread over the several locations and traceable over the available graphical map, and the overall information is easily accessible using smart devices. For parking information, a smart web application which is another important module of this study is designed, with which the SVPS system’s registered users are able to access all the services provided for smart vehicle parking searching and reservation in efficient and reliable ways. An integrated network approach, RFID (radio frequency identification) and wireless sensors network (WSN), called RF‐WSN, is employed to retrieve the real‐time information from the installed and configured sensor devices in RFID‐WSN network.

The data transmission process in Wireless Sensor Networks (WSNs) often experiences errors and packet losses due to the environmental interference. In order to address this problem, we propose a Compressive Sensing data gathering algorithm based on Packet Loss Matching (CS‐PLM). It is proven that, under tree routing, the packet loss on communication links would severely undermine the data reconstruction accuracy in Compressive Sensing (CS) based data gathering process. It is further pointed out that the packet loss in CS based data gathering exhibits the correlation effect. Meanwhile, we design a sparse observation matrix based on packet loss matching and verify that the designed matrix satisfies the Restricted Isometry Property (RIP) with a probability arbitrarily close to 1. Therefore, reliable transmission of the compressed data can be guaranteed by adopting the multipath backup routing among CS nodes. It is shown in the simulation results that, with a 60% packet loss ratio of the link, the CS‐PLM algorithm can still ensure the effective reconstruction of the data gathered by the CS algorithm and the relative reconstruction error is lower than 5%. Therefore, it is verified that the proposed algorithm could effectively alleviate the sensitivity to packet losses for the CS based data gathering algorithm on unreliable links.

Mobile applications are being increasingly deployed on a massive scale in various mobile sensor grid database systems. With limited resources from the mobile devices, how to process the huge number of queries from mobile users with distributed sensor grid databases becomes a critical problem for such mobile systems. While the fundamental semantic cache technique has been investigated for query optimization in sensor grid database systems, the problem is still difficult due to the fact that more realistic multidimensional constraints have not been considered in existing methods. To solve the problem, a new semantic cache scheme is presented in this paper for location-dependent data queries in distributed sensor grid database systems. It considers multidimensional constraints or factors in a unified cost model architecture, determines the parameters of the cost model in the scheme by using the concept of Nash equilibrium from game theory, and makes semantic cache decisions from the established cost model. The scenarios of three factors of semantic, time, and locations are investigated as special cases, which improve existing methods. Experiments are conducted to demonstrate the semantic cache scheme presented in this paper for distributed sensor grid database systems.

With various emerging mobile devices, the visual content have be to resized into different sizes or aspect ratios for good viewing experiences. In this paper, we propose a new multioperator retargeting algorithm by using four retargeting operators of seam carving, cropping, warping, and scaling iteratively. To determine which retargeting operator should be used at each iteration, we adopt structural similarity (SSIM) to evaluate the similarity between the original and retargeted images. The retargeting operator sequence is constructed based on the four types of retargeting operators by an optimization process. Since the sizes of original and retargeted images are different, scale-invariant feature transform flow is used for dense correspondence between the original and retargeted images for similarity evaluation. Additionally, visual saliency is used to weight SSIM results based on the characteristics of the human visual system. Experimental results on a public image retargeting database have shown the promising performance of the proposed multioperator retargeting algorithm.

Visual sensor networks (VSNs) are highly vulnerable to attacks due to their open deployment in possibly unattended environments. To improve the network security of VSNs, an intrusion detection system (IDS) is an effective countermeasure. However, as visual sensors can produce big and dynamic video data, it is a tough task to rapidly and effectively detect attacks in VSNs. Moreover, attack samples in VSNs are generally too rare for IDSs to fully understand the behaviors of attacks. Facing these difficulties, in this paper, we propose an efficient intrusion detection approach for VSNs, which is based on traffic pattern learning. In the proposed approach, a traffic model is developed to describe the dynamic characteristics of network traffic in VSNs. Based on this model, the optimal feature set for traffic pattern learning can be extracted. Then a hierarchical self-organizing map (HSOM) is employed to learn traffic patterns and detect intrusions. Furthermore, an active learning strategy is devised to accelerate the training process of the HSOM and better learn the patterns of attacks. Experimental results show that the proposed approach has high detection accuracy and good real-time performance.

Summary With the continued development of hardware and software, graphics processing unit (GPU) has been used in general purpose computational fields, while accelerating applications for CPUs. To achieve high computing performance, a GPU typically includes hundreds of computing units. The high density of computing resource on‐chip incurs high power consumption as well as engendering high performance. The power consumption problem has become one of the most important problems for the development of GPUs. Focusing on a CPU‐GPU heterogeneous parallel system, this research proposed an architecture‐level GPU energy model, with the aim of reducing system energy demand and improving system efficiency. Taking the influences of memory and temperature on GPU energy demand into account, a dynamic energy model based on division of computation and memory and a static energy model based on real‐time temperature perception were established. Validation, through the evaluation and comparative analysis of nine typical GPU programmes, demonstrated that these models could reduce chip energy demand under the performance constraint conditions imposed. Copyright © 2014 John Wiley & Sons, Ltd.

The rapid development of the latest distributed computing paradigm, i.e., cloud computing, generates a highly fragmented cloud market composed of numerous cloud providers and offers tremendous parallel computing ability to handle big data problems. One of the biggest challenges in multiclouds is efficient workflow scheduling. Although the workflow scheduling problem has been studied extensively, there are still very few primal works tailored for multicloud environments. Moreover, the existing research works either fail to satisfy the quality of service (QoS) requirements, or do not consider some fundamental features of cloud computing such as heterogeneity and elasticity of computing resources. In this paper, a scheduling algorithm, which is called multiclouds partial critical paths with pretreatment (MCPCPP), for big data workflows in multiclouds is presented. This algorithm incorporates the concept of partial critical paths, and aims to minimize the execution cost of workflow while satisfying the defined deadline constraint. Our approach takes into consideration the essential characteristics of multiclouds such as the charge per time interval, various instance types from different cloud providers, as well as homogeneous intrabandwidth vs. heterogeneous interbandwidth. Various types of workflows are used for evaluation purpose and our experimental results show that the MCPCPP is promising.

Intrusion response is a critical part of security protection. Compared with IT systems, industrial automation systems (IASs) have greater timeliness and availability demands. Real-time security policy enforcement of intrusion response is a challenge facing intrusion response for IASs. Inappropriate enforcement of the security policy can influence normal operation of the control system, and the loss caused by this security policy may even exceed that caused by cyberattacks. However, existing research about intrusion response focuses on security policy decisions and ignores security policy execution. This paper proposes a general, real-time control approach based on table-driven scheduling of intrusion response in IASs to address the problem of security policy execution. Security policy consists of a security service group, with each type of security service supported by a realization task set. Realization tasks from several task sets can be combined to form a response task set. In the proposed approach, first, a response task set is generated by a nondominated sorting genetic algorithm (GA) II with joint consideration of security performance and cost. Then, the system is reconfigured through an integrated scheduling scheme where system tasks and response tasks are mapped and scheduled together based on a GA. Furthermore, results from both numerical simulations and a real-application simulation show that the proposed method can implement the security policy in time with little effect on the system.

Currently, an increasing number of information/communication technologies are adopted into the industrial control systems (ICSs). While these IT technologies offer high flexibility, interoperability, and convenient administration of ICSs, they also introduce cybersecurity risks. Dynamic cybersecurity risk assessment is a key foundational component of security protection. However, due to the characteristics of ICSs, the risk assessment for IT systems is not completely applicable for ICSs. In this paper, through the consideration of the characteristics of ICSs, a targeted multilevel Bayesian network containing attack, function, and incident models is proposed. Following this proposal, a novel multimodel-based hazardous incident prediction approach is designed. On this basis, a dynamic cybersecurity risk assessment approach, which has the ability to assess the risk caused by unknown attacks, is also devised. Furthermore, to improve the accuracy of the risk assessment, which may be reduced by the redundant accumulation of overlaps amongst different consequences, a unified consequence quantification method is presented. Finally, to verify the effectiveness of the proposed approach, a simulation of a simplified chemical reactor control system is conducted in MATLAB. The simulation results can clearly demonstrate that the proposed approach has the ability to dynamically calculate the cybersecurity risk of ICSs in a timely manner. Additionally, the result of a different comparative simulation shows that our approach has the ability to assess the risk caused by unknown attacks.

Single-image blind deconvolution is a challenging illposed inverse problem which requires regularization techniques to stabilize the restoration process. Its purpose is to recover an underlying blur kernel and a latent image from only one blurred image. In most imaging situations, the blur kernel is not only spatially sparse, but also piecewise smooth with the support of a continuous curve. Thus this paper proposes a hybrid regularized method to robustly estimate the blur kernel by incorporating both L1-norm of kernel intensity and squared L2- norm of intensity derivative. Once the blur kernel is estimated, a total generalized variation based image restoration model is developed to guarantee robust non-blind image deconvolution. All optimization problems related to blur kernel estimation and non-blind deconvolution in this paper will be efficiently solved using fast numerical algorithms. Numerous experiments have been conducted to compare our proposed method with some state-of-the-art blind deconvolution methods on both synthetic and real-world datasets. The experimental results have illustrated the effectiveness of our proposed method in terms of quantitative and qualitative image quality evaluations.

Total variation (TV) is well capable of preserving edges and smoothing flat regions, however, often suffers from staircase artifacts in regions with gradual intensity variations. The established second-order TV can overcome this drawback but may lead to blurred edges and boundaries in restored images. In current literature, their nonconvex extensions have been proven to be effective for further enhancing image quality. This paper proposes an edge-preserving image restoration model by using both nonconvex first- and second-order TV regularizers, with a box constraint. The nonconvex hybrid regularizer is able to significantly suppress the staircase artifacts while preserving the valuable edge information. The addition of the box constraint provides a visible positive effect on image restoration, especially when there are many pixels with values lying on the predefined dynamic range boundaries. In what follows, to guarantee solution efficiency and stability, we develop an iteratively reweighted algorithm based on alternating direction method of multipliers (ADMM) to solve the proposed constrained nonconvex hybrid variational model. Numerous experimental results have demonstrated the superior performance of our proposed method in terms of quantitative and qualitative image quality evaluations.

In this paper, we propose an efficient anonymous data reporting protocol for participatory sensing, which provide strong privacy protection, data accuracy, and generality. The protocol consists of two stages: 1) slot reservation and 2) message submission. In the slot reservation stage, a group of N participants cooperate to assign each member a message slot in a vector which is essentially a message submission schedule, in such a manner that each participant's slot is oblivious to other members and the application server. In the message submission stage, each participant transmits an encoded data to the application server based on the slot information known only to herself, in such a way that the application server cannot link a data to a specific participant. With such a data reporting protocol, the link between the data and the participants is broken, and as a result, participant's privacy is protected. We conduct theoretical analysis of the correctness and anonymity of our protocol, as well as experiments to demonstrate the efficiency in small-scale applications with periodic data sampling.

Web-based systems have enjoyed tremendous growth in both theory and applications. They are highly visible and influential realizations of user-oriented technology supporting numerous human pursuits realized across the e-service. In this paper, we focus on web-based e-service systems for the permanent magnet synchronous motor (PMSM) remote control. These systems can provide web services for updating factors and the fuzzy law of Takagi-Sugeno fuzzy, when the PMSM devices are required. This paper designs the controller of the PMSM with uncertain inertia and friction factors working under load noise in Web-based e-service systems. This controller is based on rotor field-oriented control (RFOC) structures, internal model control (IMC), and improved direct adaptive fuzzy (IDAF). In order to enhance the transient quality for the case of uncertain inertia and friction factors, we use the IDAF algorithm for the outer loop (speed loop). The IDAF is designed based on the direct adaptive fuzzy algorithm combined with the G-Fuzzy system for adjusting online updating adaption factors. The essence of IDAF is a self-learning and self-adaption system with enhanced adaptive ability through the G-Fuzzy system. For the inner loop (current loop), an improved IMC (IIMC) structure is proposed to reduce the effect of load noise. The IIMC combines the tradition IMC and a speed feedback loop to enhance the antiload noise ability of the system. The difference between our control structure and the traditional control structure is that the system could automatically realize antiload noise in the inner loop before adjusting the speed in the outer loop. This will create really high performances for PMSM control systems. We also demonstrate the effect of this control algorithm on PMSM-RFOC system control. The extensive simulation results demonstrate that the current response satisfies the condition of ability and settling time. Especially, the antiload noise ability and transient quality of the system are controlled independently. Thus, it is a solid foundation upon which to develop a high-quality PMSM electric drive in the e-service.

The dynamical degradation of digital chaotic systems (DCSs) often has serious negative influences on some digital chaos-based systems and then becomes one of the bottleneck problems stopping chaos from some applications. In this paper, we first restrict the Devaney’s chaos definition to finite state sets to describe the chaotic motion of digital systems. Then, we propose a novel control method for DCSs based on the differential mean value theorem and state feedback technology. Simulation results show the effectiveness, robustness, and superiority of the proposed method. Finally, we construct a new pseudorandom number generator (PRNG) and evaluate its randomness via NIST SP800-22 and TestU01 test suites. Statistical test results show that the proposed PRNG has high reliability of randomness, thus it can be used for cryptography and other potential applications.

In wireless networks, it is still a challenge to allocate the limited resources among users to meet their specific quality of service (QoS) requirements, especially when the users have different traffic types, i.e., the hard QoS traffic, the best effort traffic, and the soft QoS traffic. In this paper, we develop the utility-based resource allocation algorithms in the following three tasks: 1) resource allocation among the hard QoS traffic and the soft QoS traffic; 2) resource allocation among the best effort traffic and the soft QoS traffic; and 3) finally resource allocation among the hard QoS traffic, the best effort traffic, and the soft QoS traffic, by solving the network utility maximization problem using the Karush-Kuhn-Tucker condition. We develop a number of critical theorems to give the conditions that find the optimal solutions for the above three cases in a unified framework. These theorems then act as design guidelines for the three algorithms. The proposed algorithms take into account the traffic type, the total available resources and the users' channel qualities. We evaluate the time complexity of the proposed algorithms, which comes out to be polynomial, and study the network performance by numerical examples. Numerical results demonstrate the bandwidth allocations, the fairness index and the total maximum utility under different channel qualities and resource situations.

Unlike traditional epidemic virus spreading, behavior diffusion in social networks is conducted by rational users, who would make strategic choices instead of being randomly infected with some probability. Specifically, individuals always try to maximize their utilities through rationally selecting specific behaviors (adopting a new product, or spreading a rumor, etc.). However, utility obtained by an individual, naturally contains uncertainty, and it may stem from two sources: users' imperfect and incomplete knowledge about others, and the inherently stochastic property in human behavior. Thus, it is imperative to model and analyze the diffusion pattern under the resulting uncertainty in social networks, which, however, has not yet been deeply examined by the existing works. This paper deeply explores the pattern of gossip diffusion in social networks when uncertainty exists in users' decision making. In detail, the innovative results provided in this paper are: first, inspired by random utility theory, we formulate the diffusion model based on mixed logit model that allows for user's uncertainty in determining whether to adopt a specific strategy; second, the formal analysis framework characterizing the diffusion process is derived through the approximation method of mean field theory; finally, we explore the extensive applicability of our proposed analysis framework through modeling rumor diffusion in social networks as a coordination game. Our findings are, for various structural characteristics, small uncertainty can significantly speed up the diffusion of gossip; furthermore, social networks with scale-free property can facilitate the gossip diffusion in the easiest way, but, the range of uncertainty factor that can maximize gossip diffusion is the smallest. The obtained results perfectly comply with the philosophical saying about rumor diffusion in real social life: easy come, easy go.

Transient faults are a dominant kind of threat to system safety in networked control systems (NCSs) due to their high occurrence rate and wide variety. However, they are hardly detected accurately in NCSs because of their unpredictable nature and short duration. Hence, fault propagation analysis (FPA) has become a bottleneck issue for fault-tolerant control in NCSs, which is used to analyze the fault effects and identify the approximate zone where transient fault occurred. In this paper, an innovative ontology-based FPA approach (ontologyFPA) is proposed to analyze transient fault propagation effects in NCSs. From the view of object-centered ontology, function, behavior, and structure models are built to reflect system abstraction hierarchies, and fault propagation effects and traces are identified from behaviors to functions through the mapping relationships of abstraction models. From the view of system-centered ontology, information-based workflows are employed to represent system independence in which fault propagation is investigated by excavating different effect traces among serial tasks in control loops. To illustrate the processes of propagation analysis, the application of ontologyFPA in a steam generator water level control system is presented. Finally, based on a unified simulation platform described by the architecture analysis and design language (AADL), two types of faults are injected to inspect the fault propagation processes between abstraction hierarchies, while another type is injected to investigate the processes in workflows. The results demonstrate that the proposed approach is effective in terms of identifying transient fault propagation effects and traces.

Industrial process automation is undergoing an increased use of information communication technologies due to high flexibility interoperability and easy administration. But it also induces new security risks to existing and future systems. Intrusion detection is a key technology for security protection. However, traditional intrusion detection systems for the IT domain are not entirely suitable for industrial process automation. In this paper, multiple models are constructed by comprehensively analyzing the multidomain knowledge of field control layers in industrial process automation, with consideration of two aspects: physics and information. And then, a novel multimodel-based anomaly intrusion detection system with embedded intelligence and resilient coordination for the field control system in industrial process automation is designed. In the system, an anomaly detection based on multimodel is proposed, and the corresponding intelligent detection algorithms are designed. Furthermore, to overcome the disadvantages of anomaly detection, a classifier based on an intelligent hidden Markov model, is designed to differentiate the actual attacks from faults. Finally, based on a combination simulation platform using optimized performance network engineering tool, the detection accuracy and the real-time performance of the proposed intrusion detection system are analyzed in detail. Experimental results clearly demonstrate that the proposed system has good performance in terms of high precision and good real-time capability.

Emerging non-volatile memory technologies, especially flash-based solid state drives (SSDs), have increasingly been adopted in the storage stack. They provide numerous advantages over traditional mechanically rotating hard disk drives (HDDs) and have a tendency to replace HDDs. Due to the long existence of HDDs as primary building blocks for storage systems, however, much of the system software has been specially designed for HDD and may not be optimal for non-volatile memory media. Therefore, in order to realistically leverage its superior raw performance to the maximum, the existing upper layer software has to be re-evaluated or re-designed. To this end, in this paper, we propose PASS, an optimized I/O scheduler at the Linux block layer to accommodate the changing trend of underlying storage devices toward flash-based SSDs. PASS takes the rich internal parallelism in SSDs into account when dispatching requests to the device driver in order to achieve high performance. Specifically, it partitions the logical storage space into fixed-size regions (preferably the component package sizes) as scheduling units. These scheduling units are serviced in a round-robin manner and for every chance that the chosen dispatching unit issues only a batch of either read or write requests to suppress the excessive mutual interference. Additionally, the requests are sorted according to their visiting addresses while waiting in the dispatching queues to exploit high sequential performance of SSD. The experimental results with a variety of workloads have shown that PASS outperforms the four Linux off-the-shelf I/O schedulers by a degree of 3% up to 41%, while at the same time it improves the lifetime significantly, due to reducing the internal write amplification.

Advancements in cloud computing enable the easy deployment of numerous services. However, the analysis of cloud service access platforms from a client perspective shows that maintaining and managing clients remain a challenge for end users. In this paper, we present the design, implementation, and evaluation of an asymmetric virtual machine monitor (AVMM), which is an asymmetric partitioning-based bare-metal approach that achieves near-native performance while supporting a new out-of-operating system mechanism for value-added services. To achieve these goals, AVMM divides underlying platforms into two asymmetric partitions: a user partition and a service partition. The user partition runs a commodity user OS, which is assigned to most of the underlying resources, maintaining end-user experience. The service partition runs a specialized OS, which consumes only the needed resources for its tasks and provides enhanced features to the user OS. AVMM considerably reduces virtualization overhead through two approaches: 1) Peripheral devices, such as graphics equipment, are assigned to be monopolized by a single user OS. 2) Efficient resource management mechanisms are leveraged to alleviate complicated resource sharing in existing virtualization technologies. We implement a prototype that supports Windows and Linux systems. Experimental results show that AVMM is a feasible and efficient approach to client virtualization.

Packet schedulers are the key to guarantee quality of service in computer networks. The rate-controlled EDF schedulers embrace the good characteristics of output smoothness. But the methods used in the existing EDF schedulers to calculate eligible time are based on a real or virtual global system time, which is too complex to be implemented in high-speed networks. The paper presents a specific and practical scheme to implement a timeslot based EDF scheduler which named TiRSS (Rate-controlled Smooth Scheduling Based on Timeslot-iteration). In order to avoid the dependence of the global system time, TiRSS apply a new feasible state tracking mechanism which update the state of queues along with the scheduling timeslot iteratively. Theory analysis proved the state tracking mechanism can accurately track the process of packet transmitting in the ideal scheduling model. Through this counting mechanism combined with EDF scheduling principle, TiRSS realizes a practical implementation to EDF scheduler based on timeslot-iteration. The sufficient theoretical analysis and simulation results show that TiRSS is reasonable and effective.

Application Layer Multicast (ALM) systems can easily be deployed compared with IP multicast because they do not require any modification to the current Internet infrastructure. Topology-aware ALM systems make multicast forwarding path as far as possible to match the underlying physical paths. It can reduce redundant data packets and forwarding delay. So the source path topology map construction is more important in topology-aware ALM systems. We find that only the routers directly connecting to terminal nodes (usually as gateway) and their connection information are required to be added to the source path topology map. This kind of topology map is named as gateway-level map. In this paper, we first present a delay coarse-grained matching method to generate the gateway-level topology map. After that, the paper discusses some details such as how to get the topology information and how to build the map. Finally, performance analysis and simulation experiments demonstrate the conclusion that the method not only simplifies the information required by topology construction, but also accelerates the speed of accessing topological information. The time of application layer multicast nodes joining the multicast tree is reduced. This method also provides users the higher quality of service.

Fingerprint authentication for content protection in the human-machine systems, cybernetics, and computational intelligence is very popular. Because of the complex input contexts, low-quality input fingerprint images always exist with cracks and scars, dry skin, or poor ridges and valley contrast ridges. Usually, fingerprint images are enhanced by one stage in either the spatial or the frequency domain. However, the enhanced performances are not satisfactory because of the complicated ridge structures that are affected by unusual input contexts. In this paper, we propose a novel and effective two-stage enhancement scheme in both the spatial domain and the frequency domain by learning from the underlying images. To remedy the ridge areas and enhance the contrast of the local ridges, we first enhance the fingerprint image in the spatial domain with a spatial ridge-compensation filter by learning from the images. With the help of the first step, the second-stage filter, i.e., a frequency bandpass filter that is separable in the radial- and angular-frequency domains, is employed. It is noted that the parameters of the bandpass filters are learnt from both the original image and the first-stage enhanced image instead of acquiring from the original image solely. It enhances the fingerprint image significantly because of the fast and sharp attenuation of the filter in both the radial and the angular-frequency domains. Experimental results show that our proposed algorithm is able to handle various input image contexts and achieves better results compared with some state-of-the-art algorithms over public databases, and to improve the performances of fingerprint-authentication systems.

To avoid failure and achieve higher availability, replication scheme is now widely used in distributed Cloud storage systems [25]. However, most of them only statically replicate data on some randomly chosen nodes for a fixed number of times and it is obviously not enough for more reasonable resource allocation. Moreover, query load for Web application is highly irregular. It throws us into a dilemma to always maintain maximum number of replicas in case of explosive query load outburst or save resources with fewer replicas at the expense of performance. In this paper, we present a Resilient, Fault-tolerant and High-efficient global replication algorithm (RFH) for distributed Cloud storage systems. RFHis especially efficient facing 'flash crowd' problem. Each data partition is represented by a virtual node. Each virtual node itself decides whether to replicate, migrate or suicide by weighing up the pros and cons. It is based on the evaluation of traffic load of all nodes, and selects among physical nodes with the most traffic (traffic hub) to replicate or migrate on. After that, it takes into account blocking probability to achieve quicker response and better load balance performance. Extensive simulations have been conducted and the results have demonstrated that the proposed scheme RFH outperforms the main existing algorithms the request-oriented algorithms[16] [5], the owner-oriented algorithms [7] [11] [12] [13] and the random algorithms [4] [21] [22] in terms of high replica utilization rate, high query efficiency and reasonable path length at a low cost while maintaining high availability.

Cloud computing is an increasingly important solution for providing services deployed in dynamically scalable cloud networks. Services in the cloud computing networks may be virtualized with specific servers which host abstracted details. Some of the servers are active and available, while others are busy or heavy loaded, and the remaining are offline for various reasons. Users would expect the right and available servers to complete their application requirements. Therefore, in order to provide an effective control scheme with parameter guidance for cloud resource services, failure detection is essential to meet users' service expectations. It can resolve possible performance bottlenecks in providing the virtual service for the cloud computing networks. Most existing Failure Detector (FD) schemes do not automatically adjust their detection service parameters for the dynamic network conditions, thus they couldn't be used for actual application. This paper explores FD properties with relation to the actual and automatic fault-tolerant cloud computing networks, and find a general non-manual analysis method to self-tune the corresponding parameters to satisfy user requirements. Based on this general automatic method, we propose specific and dynamic Self-tuning Failure Detector, called SFD, as a major breakthrough in the existing schemes. We carry out actual and extensive experiments to compare the quality of service performance between the SFD and several other existing FDs. Our experimental results demonstrate that our scheme can automatically adjust SFD control parameters to obtain corresponding services and satisfy user requirements, while maintaining good performance. Such an SFD can be extensively applied to industrial and commercial usage, and it can also significantly benefit the cloud computing networks.

Community structure is an important characteristic in real complex networks, meaning the networks are divided naturally into modules or communities, groups of vertices with relatively dense connections within groups but sparser connections between them. Fuzzy c-means clustering have been proposed to detect the community structure in networks for the past few years, and gained some fruitful results. In this paper, we present a new method to find the community structure in complex networks with self-adapted fuzzy c-means clustering, which can find the sum of the community structure voluntarily and overcome the deficiencies of original Fuzzy c-means clustering algorithms. The simulation results on the real-world networks and on synthetic benchmarks verify that the algorithm is more complete and accurate than the other mainstream FCM algorithms.

SUMMARY The auto‐design of electronic circuits for the next generation Information Technology (IT) computing environments is currently one of the most extensively studied issues in the field of evolvable hardware (EHW) architectures. It aims to improve the reliability and fault‐tolerance of hardware systems using embedded techniques. As the scalability of logic circuits becomes larger and more complex nowadays, its auto‐design is more and more difficult. In order to improve the efficiency and the capability of digital circuit auto‐design, in this paper, a multi‐objective simulated annealing (MSA)‐based increasable evolution approach is proposed in an embedded system. First, an extended matrix encoding method is used to indicate the potential performance of a circuit. Therefore, the risk of deleting a circuit with a good developing potential during evolution can be reduced. Second, we consider each output of a digital circuit as an objective, and MSA is designed for digital logic circuits with gradual evolution scheme. In the process of evolution, each objective is evolved in parallel with adaptive mechanism of neighborhood and a performance evaluation. Finally, a framework of online evolution with macro‐blocks is employed to implement MSA on a field‐programmable gate array efficiently and securely. In our experiments, six arithmetic circuits are designed to assess the performance of MSA with gate‐level and function‐level approaches comparing to other algorithms. The comparison results show that our method is very efficient in the auto‐design of EHW. Copyright © 2010 John Wiley & Sons, Ltd.

SUMMARY On the basis of CSC (Cluster Supply Chain) networks with two four‐echelon single supply chains, chain‐to‐chain inventory transshipment between two retailers is taken into account in the context of the two single chains cooperation. The uncertain robust switched control system model is built. Meanwhile, the methodology of robust optimization is introduced to minimize the bullwhip effect, and the solution of optimal decision serials is explored utilizing H‐infinity switched control algorithm. The online decision‐making simulation framework structure is developed to demonstrate the switching procedure. The simulation example proves that chain‐to‐chain inventory transshipment in CSC networks and robust switch control method can effectively weaken the bullwhip effect, reduce stock, dwindle fluctuation of order, and improve response to cluster market. Copyright © 2011 John Wiley & Sons, Ltd.

The authors are particularly aware that green cloud computing (GCC) is a broad range and a hot field. The distinction between ‘consumer of’ and ‘provider of’ cloud-based energy resources may very important in creating a world-wide ecosystem of GCC. A user simply submits its service request to the cloud service provider with the connection of Internet or wired/wireless networks. The result of the requested service is delivered back to the user in time, whereas the information storage and process, interoperating protocols, service composition, communications and distributed computing, are all smoothly interactive by the networks. In this study, this is a survey on GCC schemes based on networks. The concept and history of Green computing were introduced first, and then focus on the challenge and requirement of cloud computing. Cloud computing needs to become green, which means provisioning cloud service while considering energy consumption under a set of energy consumption criteria and it is called GCC. Furthermore, the recent work done in GCC based on networks, including microprocessors, task scheduling algorithms, virtualisation technology, cooling systems, networks and disk storage were introduced. After that, the works on GCC from their research group was presented in Georgia State University. Finally, the conclusion and some future works were given.

In networked control systems (NCS), the performance degradation introduced by the heterogeneous and dynamic environment has intensified the need for reconfigurable protocol stacks (RPS). In this paper, an IEC61499-based method is proposed for the model-driven development of RPS. The method is enabled by defining a novel RPS function block (FB), which unifies the communication behavior and interface of nodes in NCS. Beyond existing communication FBs in IEC61499, the parameter reconfiguration of routing and scheduling table in RPS FB is highlighted as the core of communication layer function to adapt environment and system variations. Furthermore, the method allows for the code reconfiguration on Java algorithms in RPS FB under different application requirements. Through porting the Java virtual machine on different platforms, the code reconfiguration is implemented by reloading the .class file for a specified protocol FB. A case study on the embedded platform, such as DSP/BIOS and ARM/Linux, is conducted to demonstrate the effectiveness and feasibility of the proposed reconfiguration method for maintaining stable and predictable behavior in NCS.

Abstract In this paper we focus on the mobile GIS management system based on multi‐agents, and wider range of client devices with J2ME‐MIDP kernel are supported in it. A kit (Map Database Manager) is developed and described in detail. The kit can convert GIS data of commercial formats such as ArcGIS and Mapinfo into Geospatial data in GML, and provide Geospatial data for handheld devices freely without commercial plug‐in software. A peer to peer multi‐agents mechanism on mobile GIS services is the main subject of this paper. According to user's requirement and known buffer area radius on map, agent can define the factors to calculate the optimal path from one node to target node in a map, which considers not only the length of the road but also traffic flow, road cost, and other factors. We use the integrated cost to re‐define the road path weights, and select the optimal path among the road based on the minimum weight. The actual experimental results shown that in the quantified traffic flow and road condition, from node to other target nodes, we can get an optimal path on map, which satisfies the condition mostly. The results can greatly assist the searching of the optimal path in GIS services. Copyright © 2010 John Wiley & Sons, Ltd.

Topology control is one of the most fundamental problems in wireless sensor networks (WSNs). It is of great importance for prolonging network lifetime, reducing radio interference and energy consumption, increasing the efficiency of media access control (MAC) protocols and routing protocols, among other things. In this paper, the problem is transformed into a model of multi-criteria degree constrained minimum spanning tree (mcd-MST), and a non-dominated discrete particle swarm optimization (NDPSO) is designed to deal with this problem. To obtain a better approximation of true Pareto front, the phenotype sharing function of the objective space is applied in the definition of fitness function in NDPSO. Furthermore, a topology control scheme based on NDPSO is proposed in this paper. Simulation results show that the topology derived under the proposed scheme has low total power consumption, high robust structure and low contention among nodes, and it also can effectively prolong the lifetime of network.

Wireless sensor network (WSN) is a typical distributed storage system, and network coding technology is developed to enhance the data persistence of WSN. However, the traditional distributed coding strategy may cause serious "cliff effect" in the decoding process, that is to say, few source data can be recovered before sufficient encoded packets are received. Moreover, nodes may fail due to the lack of energy or the influence of the switching of external environment, such as a disaster scenario. Such failures may concentrate in a small region or distribute in the whole deployment area which can severely reduced the decoding efficiency of the persistent data in WSN. In this paper, we propose the PLTCDS (prioritized LT codes based distributed storage) algorithm to improve the data decoding efficiency when the data persistence is assured. The main idea of PLTCDS is that the predefined node broadcasts a beacon to stimulate the nodes to form the network with degree distribution priority. To ensure the effectiveness of storage nodes, PLTCDS introduces a class of cumulative counter scheme to avoid empty storage. Also we discuss about the idea of another type of PLTCDS. Experimental results show that PLTCDS algorithm can enhance the network data collection performance and reduce the influence of "cliff effect".

There is heavy computation involved in Global / Regional Assimilation and Prediction System (GRAPES) which is a typical non-linear discrete system of the Earth's atmosphere developed for numerical weather prediction. Researchers have recently paid a lot of attention to the parallel acceleration of the GRAPES model by low-cost, low-power, high-performance Graphics Processor Units (GPUs). We implement in this paper the acceleration for the GRAPES model in GPUs, which is however not as efficient as supposed. On this basis, we further propose strategies to optimize the performance, including the ease of data transmission time, reducing the amount of device memory loaded and used to store various objects, and avoiding the branches of thread control flows. One can find from the experiments that, parallel acceleration with GPUs helps to improve the performance of GRAPES in terms of measures like accuracy and timeliness.

In NCS (networked control systems), the control performance depends on not only the control algorithm but also the communication protocol stack. This paper presents a framework for the protocol reconfiguration in NCSs. Task queuing, network transmission policy and communication link control are emphasized as three key protocol functionalities related to the system performance. Based on it, data and event exchange models for layers of protocol stack are developed according to the standard IEC61499, which allows for the dynamic reconfiguration management of distributed resources as well as provides a unified real time communication service for control tasks. Finally, experiments are conducted to illustrate the application and efficiency of the proposed reconfigurable protocol stack.

IP prefix hijacking is one of the top threats in the cloud computing Internets. Based on cryptography, many schemes for preventing prefix hijacks have been proposed. Securing binding between IP prefix and its owner underlies these schemes. We believe that a scheme for securing this binding should try to satisfy these seven critical requirements: no key escrow, no other secure channel, defending against Malicious Key Issuer (MKI) in the phase of prefix announcement, defending against MKI in the phase of key issuing, no certificate, in-band delegation attestation, and in-band public key witness. In this paper, we propose a new scheme, Origin Authentication based on Self-Certified public keys (SC-OA), using self-certified public keys to authenticate origin autonomous systems. To the best of our knowledge, it is the first work for securing prefix ownership using self-certified public keys to achieve an efficient and secure scheme that satisfies all seven requirements. The analyses show that SC-OA can defend against regular prefix, sub prefix, unassigned prefix, interception-based, and MKI hijacking, and improve performance in many aspects. It will be pushed ahead to practical deployment for preventing prefix hijacks.

Data aggregation has been emerged as a basic approach in wireless sensor networks (WSNs) in order to reduce the number of transmissions of sensor nodes. Since multi-source data obtained from different nodes represent redundancy or complement property, as an effective tool to deal with the conflicts, the use of game theory for WSNs is provided. The authors propose a common aggregation model, which is independent of the specific application environments, based on the evolutionary game theory called evolutionary game-based data aggregation model (EGDAM) in WSNs. EGDAM made up of formal definition, functional model and general process is defined to map the competition and cooperation in aggregation procedure into games, and well-avoid perfect rationality. The authors then put the theoretic model into application. Guided by our model, an evolutionary game-based adaptive weighting algorithm named EGWDA is provided for the pixel-level data aggregation with homogeneous sensors. Reasonable weights distribution of sensors can be achieved during the aggregation in WSNs. The experiments on both the self-constructed data and the one from reference made satisfied performances.

As WiMAX achieves increasing deployment, the large overhead in its uplink scheduling when providing real-time services has become a major challenge. In this study, the authors present effective, low-overhead scheduling algorithms for WiMAX uplink scheduling. The authors adaptively predict users' load and select a small set of active users to be served. This addresses the major source of overhead in WiMAX uplink scheduling: the Markovian arrival process information elements (IEs) and media access control layer (MAC) service data unit (SDUs) subheader overheads grow with the number of active users. The authors introduce additional novel techniques, including piggybacking, to reduce MAC overhead. The authors implement their algorithms and conduct extensive evaluations. The results show that their algorithms not only provide quality-of-service guarantees, but also substantially reduce the scheduling overhead compared with existing schemes.

Autonomic communications aim at easing the burden of managing complex and dynamic networks, and designing adaptive, self-turning and self-stabilizing networks to provide much needed flexibility and functional scalability. With the ever-increasing number of multicast applications made recently, considerable efforts have been focused on the design of adaptive flow control schemes for autonomic multicast services. The main difficulties in designing an adaptive flow controller for autonomic multicast service are caused by heterogeneous multicast receivers, especially those with large propagation delays, since the feedback arriving at the source is somewhat outdated and can be harmful to the control operations. To tackle the preceding problem, this article describes a novel, adaptive, and autonomic multicast scheme, the so-called Proportional, Integrative, Derivative plus Neural Network (PIDNN) predictive technique, which consists of two components: the Proportional Integrative plus Derivative (PID) controller and the Back Propagation BP Neural Network (BPNN). In this integrated scheme, the PID controllers are located at the next upstream main branch nodes of the multicast receivers, and have explicit rate algorithms to regulate the receiving rates of the receivers; while the BPNN is located at the multicast source, and predicts the available bandwidth of those longer delay receivers to compute the expected rates of the longer delay receivers. The ultimate sending rate of the multicast source is the maximum of the aforesaid receiving rates that can be accommodated by its participating branches. This network-assisted property is different from the existing control schemes, in that the PIDNN controller can release the irresponsiveness of a multicast flow caused by those long propagation delays from the receivers. By using BPNN, this active scheme makes the control more responsive to the receivers with longer propagation delay. Thus the rate adaptation can be performed in a timely manner, for the sender to respond to network congestion quickly. We analyze the theoretical aspects of the proposed algorithm, show how the control mechanism can be used to design a controller to support multirate multicast transmission based on feedback of explicit rates, and verify this matching using simulations. Simulation results demonstrate that the proposed PIDNN controller avoids overflow of multicast traffic, and performs better than the existing scheme PNN [Tan et al. 2005] and the multicast schemes based on control theory. Moreover, it also performs well in the sense that it achieves high link utilization, quick response, good scalability, high unitary throughput, intra-session fairness and inter-session fairness.

Abstract Wireless monitoring and control technique is vital to improve the performance of wireless networks, e.g., helping improve the efficiency of scheduling. In this paper, we focus on two centralized algorithms based on IEEE 802.16 standard to adopt effective monitor and control mechanism to do the scheduling and channel assignment in WiMAX mesh networks. The base station (BS) monitors the subscriber stations (SSs) and gathers their requests, and then controls the SSs under the direction of the centralized algorithm. The IEEE 802.16 standard proposes two kinds of scheduling algorithms in WiMAX mesh networks, centralized and distributed algorithms. The centralized solution is more convenient to monitor and control a WiMAX mesh network since the central node is the unique monitor and controller. This paper mainly introduces two schemes working in a centralized mode. They are working in a tree search active link selection (TSALS) fashion and a link search active link selection (LSALS) fashion. In these schemes, each node is equipped with a third generation (3G) mesh router, which owns two relay transceivers and can be tuned among multiple channels. The major goal of our solution is to reduce the length of scheduling under an interference‐aware restriction. The feasibility and efficiency of our proposal are testified through extensive simulations. The result justifies that our algorithms provide much higher performance than the previous ones. Copyright © 2009 John Wiley & Sons, Ltd.

Abstract Next generation communications (NGC) represents advanced communication environments featuring objects that are focused on users. It is crucial to protect information and digital forensics for NGC. In this paper, we aim to address network security for NGC, including IEEE standard 802.16 that are associated with NGC utilization. The IEEE standard 802.16 is a wireless air interface technology in Wireless Metropolitan Access Network (WMAN). Now in the new era of wireless networking, it is the solution for ‘last mile’ problem. Point‐to‐MultiPoint (PMP) network architecture network mode and mesh network mode are two optional networking modes proposed in this standard. However, the security problems in its original protocol may be becoming the most serious obstacle in its marketable producing process. In this paper, explanation and analysis of the security mechanism are based on PMP and mesh networking modes, respectively. This paper focuses on authentication & authorization, which is the most important part but not explicitly defined in original security design of IEEE standard 802.16. Two improved schemes focused on authentication & authorization based on the two modes are proposed to strengthen 802.16 WMAN security, respectively. The relevant analysis and simulation results prove that our schemes are effective. Copyright © 2009 John Wiley & Sons, Ltd.

Abstract The rapid development of wireless technology plays extremely important roles in monitoring, control, and collaboration related applications. This paper aims at state‐of‐the‐art self‐stabilizing flocking control of wireless mobile robots with memory corruption. Flocking of a group of wireless mobile robots has gained a lot of attention due to its wide applications in recent years. While there are few works addressed on fault tolerance issue of flocking, especially for transient failure of robots. However, in the practical applications of robots, for the weak robots, some of their parts, like sensor, moving actuator, or memory etc., are prone to be crashed due to the complex environment. So, it is interesting to explore such kind of component crash or corruption (partly crash), especially for transient failure. The transient failure means the failure is temporary and after a while the robots will not be influenced by outside environment. Specially, in this paper, we mainly focus on how one kind of transient failure—memory corruption affects robot flocking, especially for the existed fault tolerant flocking. Copyright © 2009 John Wiley & Sons, Ltd.

Spatial Audio Coding (SAC) is an emerging technology with a distinguishing feature of delivering good even excellent audio quality at monotonic or stereo bitrate of conventional perceptual transform coders. By a systematic exploitation of spatial hearing, Binaural Cue Coding illustrates the power and potentials of SAC in the future for intelligent multimedia services. MPEG Surround, receiving cumulative efforts from industry and academy, strives to build a SAC system with great versatility and high quality. The initial test results of MPEG Surround show its performance advantage over conventional state-of-the-art coders in a wide range of coding configurations.

This paper propose a method for sentence ordering in multi-doucument summarization task, which combine support vector machine (SVM) and Grey Model(GM). Firstly, the method train the SVM with sentences of source documents and predict sentences sequence of summary as primary dataset. Secondly, using Grey Model to process the primary dataset, and achieve the final sequence of summary sentences. Experiments on 100 summaries showed this method provide a much higher precision than probabilistic model in sentence ordering task.

The Industrial Ethernet is promising for the implementation of a Cloud Computing based control system. However, numerous standard organizations and vendors have developed various Industrial Ethernets to satisfy the real-time requirements of field devices. This paper presents a real-time reconfigurable protocol stack to cope with this challenge, by introducing the architecture with a core of dynamic routing and autonomic local scheduling. It is based on the deterministic and stochastic Petri-Nets (DSPN) method to illustrate the performance of producer/consumer based application model, CSMA/CD based node accessing activities, and TDMA based resource allocation for real time and non-real time traffic. Furthermore, the predicted time distribution for evaluating the stability of a control system can be obtained from the proposed DSPN model. It is shown that the DSPN modeling yields good verification analysis and performance prediction results through a real experimentation.

Considering a large part of node failures in a storage clusters cannot actually destroy data in disks and even some failed nodes can soon recover, a policy that deferring a reconstruction until recover during a certain time after a node failure can lessen unnecessary data rebuilding process is absolutely possible and favorable, but it also undoubtedly introduces a certain risk of data loss.

Considering sensor nodes deployed densely and uniformly a mobile sink moving through the sensing field queries a specific area of interest for monitoring information. The Query packet, injected by the mobile sink, is routed to the specific area and the corresponding Response packet is expected to return via multi-hop communication. In this paper, we analyze such a network model to address the problem of efficient data collection for mobile wireless monitoring applications. We first propose a meeting position-aware routing (MPAR) protocol for routing the Response packet efficiently and then propose an efficient query-based data collection scheme (QBDCS) for mobile wireless monitoring applications based on the MPAR. In order to minimize the energy consumption and packet delivery latency, the QBDCS chooses the optimal query time of injecting the Query packet and tailors the routing mechanism for sensor nodes forwarding packets. Simulation study has verified the analysis and demonstrated that the QBDCS can significantly reduce the energy consumption and end to end delivery latency.

A next generation network (NGN) exploits advanced multiple broadband, quality of service (QoS)-enabled service technologies. The NGN is aimed at users and service-related functions, including web services. Recently, web service composition (WSC) has become a new approach for overcoming many difficult problems confronted by B2B e-commerce, inter-organization workflow management, enterprise application integration etc. Due to the uncertainty of the NGNs and various web services, the performance of the WSC cannot be ensured. How to model it and predict its performance becomes a difficult problem for WSC. In this paper, we propose a novel performance simulation model for WSC, called STPM+. The STPM+ model can support modeling and simulating the time and non-time QoS metrics. Based on the stochastic timed colored Petri net, the STPM+ model can simulate and predict multiple QoS metrics, such as cost, reliability, reputation and so on. To examine the validation of our STPM+ model, we have designed and realized a visual performance simulation tool, called VisualWSCPE. Based on VisualWSCPE, we have fulfilled some simulation experiments. The experiment results demonstrate the feasibility and efficiency of the STPM+ model.

With the rapid development of vehicular sensor networks (VSNs) technology, the potential use of networked real-time control and automation is enormous and appealing. However, closed-loop control VSNs via the Internet are very difficult to implement practically because of their stochastic nature. One of the biggest challenges in VSNs is how to deal with sensor networks time delay and data loss. The authors investigate the potential of using the predictive control scheme for VSNs based on round-trip time (RTT) delay prediction to overcome the VSNs transmission delay and data loss. The predicted RTT delay is taken as the sampling interval reference for variable-period sampling approach. Modelling the time delay of networked control systems as a non-linear time series, the least mean square (LMS) filter algorithm is adopted to predict online the time delay induced in VSNs. The simulation results show that the LMS algorithm can achieve ideal efficacy if time delay has no variety abnormally. The predictive compensation strategy is proposed to reduce the detrimental effect of stochastic time delays induced by communication networks on control performance. The results of offline simulations via the Internet illustrate that the predictive control scheme based on RTT delay prediction has the potential to overcome the VSNs time delay and data loss.

Free-riding is a grave threat against the existence and efficient operation of peer-to-peer networks. This study proposes a rank-based fair incentive mechanism to restrain free-riding and solve the problem of the tragedy of the commons. In this mechanism, each user has a rank according to its utility value and it can just visit the corresponding rating files with its rank in the system. The utility function in this study takes the absolute contribution value and the physical performance into account for fairness and their values are calculated by the analytic hierarchy process. Besides, a pyramidal rank structure is constructed in this mechanism, and the computation formula for the node's transfer in each layer is established. In addition, the four defects in other restrain mechanisms are resolved in this incentive mechanism. The simulations in three aspects have verified that this mechanism is effective and practical.