Multi-view clustering has been found useful to leverage diverse data sources for accurate and robust underlying data representations. It typically relies on effectively integrating the latent features from different views through allocating weights while simultaneously mining their specificity and consensus information. However, it remains open how to achieve a more fine-grained sample-level weight allocation for promoting view-specific information fusion and view-shared consensus. To address this problem, we propose a novel multi-expert learning framework named Gated Variational Graph AutoEncoder with Competition and Consensus (GVGAE-C2). In particular, it employs multiple view-specific Variational Graph AutoEncoders (VGAEs) as experts to capture the latent features from their own views. Furthermore, we design a fine-grained structure-aware gating network, which dynamically computes sample-level weights based on the proposed structure-aware quality evaluation on each expert, thus facilitating competition among experts. Meanwhile, each expert is trained not only to study its assigned view's specificity features, but also explicitly encouraged to learn consensus-aware features across views. Extensive multi-view clustering experiments on benchmark datasets reveal that GVGAE-C2 significantly outperforms state-of-the-art methods.

While large language models (LLMs) have demonstrated strong capabilities in code generation, current benchmarks primarily focus on single-turn scenarios, neglecting the complexity of multi-turn interactions and user diversity. To address this gap, we introduce Talk2Code, the first benchmark for user-stratified multi-turn dialogue code generation evaluation across algorithmic problem-solving and backend programming tasks.A distinctive feature of our benchmark is its user-stratified interaction modeling. For identical coding tasks, we construct dialogue trajectories tailored for novice, intermediate, and expert users, capturing their distinct expectations and communication patterns.To facilitate comprehensive evaluation, we propose a multi-dimensional evaluation framework assessing both code quality and interaction experience through a novel Dual-track Evaluation Method. In the Direct Generation Track, the benchmark provides golden dialogue context (excluding the final code) directly to the LLM for code generation. In contrast, the Interactive Dialogue Track simulates realistic multi-turn interactions, prompting the model to proactively clarify instructions and gather requirements before generating solutions. Code quality is evaluated in both tracks by Test Pass Rate and Success Rate, while interaction experience is assessed exclusively within the Interactive Dialogue Track through subjective and alignment indicators. Our benchmark and multi-dimensional indicator system collectively establish a new paradigm for evaluating adaptive, user-aware AI coding assistants.

Federated Graph Learning (FGL) has emerged as a compelling paradigm for collaboratively training a global model while preserving the privacy of multi-source graphs. Nonetheless, FGL faces a critical challenge of data heterogeneity, where semantic and structural discrepancies across clients significantly degrade its performance. Although existing methods attempt to calibrate client-specific graph distributions during federated training, they inevitably fall short in aligning the optimization behaviors across clients due to dynamic parameter updates, thereby inducing a bottleneck in generalization improvement. To tackle this challenge, we propose a solution from a new perspective of prior refinement, which seeks to proactively harmonize client graph distributions before the federated training. In particular, we propose a Federated Graph Harmonization (FedGH) framework that exploits the generative strengths of graph diffusion models to perform prior refinement of local graphs. In a nutshell, FedGH designs a conditional diffusion mechanism on each client that synthesizes pseudo-graphs encapsulating both feature and structural priors, thereby facilitating explicit correction of inter-client distributional bias. On the server side, we employ the graph contrastive learning between various client-specific pseudo-graphs to incorporate the global information, subsequently guiding local data reconstruction. Importantly, model-agnostic FedGH can be seamlessly deployed as a plug-and-play module to be easily integrated with existing FGL architectures. Extensive experiments demonstrate that FedGH consistently outperforms state-of-the-art FGL baselines.

Accurate classification of encrypted IoT traffic remains challenging due to evolving applications and distribution shifts. This work presents a self-updating hybrid meta-learning framework that integrates Bayesian Neural Networks (BNN) for uncertainty-aware update triggering with a Random Forest meta-classifier for robust decision fusion. The proposed design improves scalability and interpretability through feature-importance analysis and lightweight ensemble learning. Prediction instability is quantified using the Hellinger distance, avoiding normalization overhead and enabling an adaptive familiarity score via a tunable parameter α. Experimental results on encrypted traffic datasets demonstrate significant gains in reliability, achieving up to 95.7% accuracy and 0.95 macro-F1, and effective selective retraining under distribution shifts.

The Execute-Order-Validate (EOV) framework has been used to improve the scalability of blockchains by concurrently executing transactions. However, the EOV framework also poses a critical performance issue. Specifically, when multiple transactions access the same data, only one of them can be committed eventually while the others are aborted due to the strong concurrency control restriction. This inefficiency makes the EOV framework far from practicality since there always exist hotspot variables that can be frequently accessed in real-world scenarios, such as the Fungible Token (FT) and Non-Fungible Token (NFT) online marketplace. In this paper, we propose RapidSnail, a novel EOV framework that enables transactions to execute based on the uncommitted data to reduce the transaction abort rate in such scenarios with hotspot variables. We first propose a new read-write set representation and a concurrency execution schedule algorithm in the execution phase to maintain the concurrent efficiency. Then we propose an effect-based conflict graph construction algorithm in the order phase to handle the conflict transactions based on the new read-write set. Finally, we propose a concurrent commitment schedule algorithm to adopt the new read-write set to validate the transactions concurrently in the validation phase. Our experiment results show that RapidSnail increases the throughput by at least 4× compared to the state-of-the-art EOV framework under high contention workload. More specifically, RapidSnail reduces the abort rate by 50%, and achieves at least 4× speedup in the order phase and 2.94× speedup in the validation phase over the existing EOV frameworks.

Location point set similarity range query aims to retrieve candidate point sets that are similar to the given point set in terms of location distribution patterns and geographical features, and it is vital in GIS (Geographic Information Systems), IoT (Internet of Things), and biometrics. Due to the economic and flexible advantages of cloud services, location data is frequently outsourced to cloud servers, which simultaneously increases the risk of privacy breaches. To address this, service providers choose to encrypt data before outsourcing. However, the existing schemes for similarity range query of encrypted location point sets have some problems, such as high computational complexity of measurements, which limit the query efficiency and security of schemes. To tackle these problems, this paper achieves the efficient and privacy-preserving location-point-set similarity range query for cloud computing (LPSQ). Firstly, we propose a lightweight similarity measurement called Geo-Jaccard similarity, to reduce the time complexity to$O(n)$. Secondly, to enhance the efficiency of the scheme, we integrate the kd-tree with pivot point technology to construct a pkd-tree, and design a corresponding filtering and verification algorithm. Thirdly, to enhance the security of our scheme, we encrypt the pkd-tree using a mix of matrix encryption and SHE (Symmetric Homomorphic Encryption), and design a series of protocols under SHE, such as SHE batch minimum value calculation protocol, SHE division protocol, and the approximation algorithm for computing Jaccard similarity securely. Finally, we prove that the security of our proposed LPSQ achieves CPA (Chosen Plaintext Attack) security. Furthermore, we conduct experiments to assess the performance, and the results demonstrate that LPSQ achieves sublinear search efficiency, while Geo-Jaccard similarity proves effective for similarity range queries on location point sets.

As a paradigm for encouraging users to contribute data spontaneously, mobile crowdsensing (MCS) has received considerable attention recently. It is crucial to evaluate the truthfulness of MCS data by proper truth discovery mechanisms. Although recent truth discovery schemes can determine truthful information, they either provide limited privacy preservation or have heavy computation and communication overheads. Moreover, most of them are not resilient to malicious faults and active attacks. To tackle the above problems, we propose two fault-tolerant and privacy-preserving truth discovery solutions. Our first scheme is mainly used for scenarios with a relatively stable number of users, where participants do not frequently join or leaves. Integrating ring signature with the perturbation technique, we design an anonymous and privacy-preserving truth discovery scheme, namely RsAnonTD, which can achieve privacy preservation and resist active attacks. To address the challenge with dynamically changed workers, we devise a multi-client inner product functional encryption scheme with a lightweight zero-knowledge proof protocol (namely McFeKDeTD) for defending against active attacks. The security analysis shows that both schemes can preserve the privacy of sensory data, weights, and estimated truths while resisting active attacks, thereby guaranteeing fault tolerance. Extensive experiments demonstrate that our designs achieve superior performance than other schemes in terms of accuracy, convergence speed, and system overheads. For example, compared with the state-of-the-art approach RPTD-II, which has a security level comparable to ours, our proposed schemes, RsAnonTD and McFeKDeTD, reduce the computational overheads approximately by 98% and 69%, respectively.

The rapid evolution of unmanned aerial vehicles (UAVs) has positioned air-ground networks as vital infrastructures for diverse applications. However, the open channels of air-ground networks remain inherently vulnerable to persistent eavesdropping threats. While physical-layer key generation (PLKG) offers a lightweight security mechanism by leveraging channel reciprocity to extract shared secrets, the inherent mobility of UAVs introduces a paradoxical tradeoff. Increased channel randomness from dynamic flight patterns enhances security through entropy amplification but simultaneously disrupts channel reciprocity, leading to key mismatch between legitimate parties. Existing PLKG schemes struggle to maintain reliability in key generation due to static channel characteristics and synchronization overhead, limiting their practical deployment in air-ground networks. To resolve this conflict, we propose a Doppler shift key generation (DSKG) scheme that systematically regulates Doppler shifts through UAV trajectory design to derive secure keys. By formulating the problem as a Markov decision process, we develop a proximal policy optimization (PPO)-clip-based reinforcement learning algorithm to dynamically control UAV speed and steering angle, ensuring robust Doppler shift reciprocity while maximizing both key entropy and generation rate. Experimental results quantify the improvements of our scheme over benchmarks in maintaining high key unpredictability and generation efficiency. Furthermore, the analysis provides valuable insights into parameter impacts, confirming the practical viability of the DSKG scheme for securing air-ground communications.

Recently, the success of large models has demonstrated the importance of scaling up model sizes. However, it is difficult to directly train large models locally on multiple mobile devices due to their intrinsic computational constraints. To address this challenge, it becomes a crucial need to train larger global models by training small local models on devices. As a distributed learning approach, federated learning (FL) allows multiple devices to train models locally and aggregate them to form the global model by sharing the updated parameters with the server, thus enabling the co-training of models. This promising feature has spurred an increasing interest in exploring the collaborative training of large models. Despite the advent of existing device-heterogeneity FL approaches, they still have limitations in fully covering the parameter space of the global model. To fill this gap, we propose a novel approach calledFedBRB(Block- wiseRolling and weightedBroadcast). The core idea of FedBRB is to utilize local models of small devices to train all modules of a large global model and broadcast the trained parameters to the entire space, thereby enabling faster information sharing. This approach not only improves training efficiency but also fully utilizes limited computational resources. Experiments demonstrate that FedBRB can produce significant performance gains, achieving state-of-the-art results. Additionally, this paper provides theoretical and experimental analyses of FedBRB convergence, thereby paving a theoretical ground and providing practical guidance for further research and application of the FedBRB method.

Despite federated learning (FL)'s potential in collaborative learning, its performance has deteriorated due to the data heterogeneity of distributed users. Recently, clustered federated learning (CFL) has emerged to address this challenge by partitioning users into clusters according to their similarity. However, CFL faces difficulties in training when users are unwilling to share their cluster identities due to privacy concerns. To address these issues, we present an innovative Efficient and Robust Secure Aggregation scheme for CFL, dubbed EBS-CFL. The proposed EBS-CFL supports effectively training CFL while maintaining users' cluster identity confidentially. Moreover, it detects potential poisonous attacks without compromising individual client gradients by discarding negatively correlated gradients and aggregating positively correlated ones using a weighted approach. The server also authenticates correct gradient encoding by clients. EBS-CFL has high efficiency with client-side overhead O(ml + m^2) for communication and O(m^2l) for computation, where m is the number of cluster identities, and l is the gradient size. When m = 1, EBS-CFL's computational efficiency of client is at least O(log(n)) times better than comparison schemes, where n is the number of clients. In addition, we validate the scheme through extensive experiments. Finally, we theoretically prove the scheme's security.

Graph Neural Networks (GNNs) have exhibited remarkable capabilities for dealing with graph-structured data. However, recent studies have revealed their fragility to adversarial attacks, where imperceptible perturbations to the graph structure can easily mislead predictions. To enhance adversarial robustness, some methods attempt to learn robust representation through improving GNN architectures. Subsequently, another approach suggests that these GNNs might taint feature information and have poor classifier performance, leading to the introduction of Graph Contrastive Learning (GCL) methods to build a refining-classifying pipeline. However, existing methods focus on global-local contrastive strategies, which fails to address the robustness issues inherent in the contexts of adversarial robustness. To address these challenges, we propose a novel paradigm named GRANCE to enhance the robustness of learned representations by shifting the focus to local neighborhoods. Specifically, a dual neighborhood contrastive learning strategy is designed to extract local topological and semantic information. Paired with a neighbor estimator, the strategy can learn robust representations that are resilient to adversarial edges. Additionally, we also provide an improved GNN as classifier. Theoretical analyses provide a stricter lower bound of mutual information, ensuring the convergence of GRANCE. Extensive experiments validate the effectiveness of GRANCE compared to state-of-the-art baselines against various adversarial attacks.

The space-air-ground integrated network (SAGIN) can enable global range and seamless coverage in the future network. SAGINs consist of three spatial layer network nodes: 1) satellites on the space layer, 2) aerial vehicles on the aerial layer, and 3) ground devices on the ground layer. Data transmissions in SAGINs include six unique cross-spatial-layer scenarios, i.e., three uplink and three downlink transmissions across three spatial layers. For simplicity, we call them six cross-layer scenarios. Considering the diverse cross-layer scenarios, it is crucial to conduct a unified network modeling regarding node coverage and distributions in all scenarios. To achieve this goal, we develop a unified modeling approach of coverage regions for all six cross-layer scenarios. Given a receiver in each scenario, its coverage region on a transmitter-distributed surface is modeled as a spherical dome. Utilizing spherical geometry, the analytical models of the spherical-dome coverage regions are derived and unified for six cross-layer scenarios. We conduct extensive numerical results to examine the coverage models under varying carrier frequencies, receiver elevation angles, and transceivers' altitudes. Based on the coverage model, we develop an algorithm to generate node distributions under spherical coverage regions, which can assist in testing SAGINs before practical implementations.

With the proliferation of Virtual Reality (VR) markets, VR applications are rapidly expanding in scale and complexity, thereby driving an urgent need for assuring VR software quality. Different from traditional mobile applications and computer software, VR testing faces unique challenges due to diverse interactions with virtual objects, complex 3D virtual environments, and intricate sequences to complete tasks. All of these emerging challenges hinder existing VR testing tools from effectively and systematically testing VR applications. In this paper, we present VRExplorer, a novel model-based testing tool to effectively interact with diverse virtual objects and explore complex VR scenes. Particularly, we design the Entity, Action, and Task (EAT) framework for modeling diverse VR interactions in a generic way. Built upon the EAT framework, we then present the VRExplorer agent, which can achieve effective scene exploration by incorporating meticulously designed path-finding algorithms into Unity’s NavMesh. Moreover, the VRExplorer agent can also systematically execute interaction decisions on top of the Probabilistic Finite State Machine (PFSM). Experimental evaluation on 11 representative VR projects shows that VRExplorer consistently outperforms the state-of-the-art (SOTA) approach VRGuide by achieving significantly higher coverage and better efficiency. Specifically, VRExplorer yields up to 122.8% and 52.8% improvements over VRGuide in terms of executable lines of code (ELOC) coverage and method (function) coverage, respectively. Furthermore, ablation results also verify the essential contributions of each designed module. More importantly, our VRExplorer has successfully detected two functional bugs and one non-functional bug from real-world projects.

Time series anomaly detection has garnered significant research attention due to growing demands for temporal data monitoring across diverse domains. Despite the rapid advent of unsupervised anomaly detection models, existing approaches face two critical challenges in understanding the mechanisms of reconstruction-based models when handling diverse temporal dependencies: (1) the insufficient exploration of complex inter-timestamp relationships encompassing both short-term and long-term dependencies, and (2) the lack of integrated frameworks for jointly learning short-term patterns and long-term temporal characteristics. To address these challenges, we propose the novel Multi-Scale Hypergraph Transformer (MSHTrans), which leverages the capacity of hypergraphs for modeling multi-order temporal dependencies. Particularly, our method employs multi-scale downsampling to derive complementary fine-grained and coarse-grained representations, integrated with trainable hypergraph neural networks that can adaptively learn inter-timestamp relationships. The framework further integrates time series decomposition to systematically extract periodic and trend components from multi-granular features, thereby enhancing long-term dependency modeling. Through synergistic integration of learned short-term patterns and long-term temporal structures, the model achieves comprehensive time series reconstruction for effective anomaly detection. Extensive experiments demonstrate that MSHTrans outperforms state-of-the-art competitors with an average performance improvement of 8.21% (without point adjustment) and 3.52% (with point adjustment).

With highly mobile and flexible-configurable, autonomous aerial vehicles (AAVs) are becoming crucial wireless communication infrastructures. To improve the reliability and throughput of data delivery for wireless networks, network coding, as a progressive technology, can be applied in AAV networks. However, network coding incurs a security problem called pollution attacks for AAV networks. Although homomorphic network coding signature can prevent pollution attacks, existing schemes are not suitable for AAV networks due to cumbersome certificate management, the key-escrow issue, or insecurity. In this article, we propose an efficient certificateless homomorphic network coding signature scheme for secure transmission of AAV networks, which can avoid certificate management and the key-escrow issue. Then our scheme is proven to be secure against adaptive chosen identity-and-subspace attacks in the random oracle model, thus our scheme can guarantee data integrity and authenticity to resist pollution attacks. We provide a performance evaluation for the proposed scheme and prior research, and experimental results illustrate the efficiency and feasibility of our scheme for practical application, reducing the verification overhead by 42.918% for a 72-dimensional data vector.

Federated Learning (FL) is a distributed machine learning framework that allows for model training across multiple clients without requiring access to their local data. However, FL poses some risks, for example, curious clients might conduct inference attacks (e.g., membership inference attacks, model-inversion attacks) to extract sensitive information from other participants. Existing solutions typically fail to strike a good balance between performance and privacy, or are only applicable to specific FL scenarios. To address these challenges, we propose a universal and efficient privacy-preserving FL framework based on matrix theory. Specifically, we design the Improved Extended Hill Cryptosystem (IEHC), which efficiently encrypts model parameters while supporting the secure ReLU function. To accommodate different training tasks, we design the Secure Loss Function Computation (SLFC) protocol, which computes derivatives of various loss functions while maintaining data privacy of both client and server. And we implement SLFC specifically for three classic loss functions, including MSE, Cross Entropy, and L1. Extensive experimental results demonstrate that our approach robustly defends against various inference attacks. Furthermore, model training experiments conducted in various FL scenarios indicate that our method shows significant advantages across most metrics.

Accurate traffic flow measurement is essential for the development of smart cities, yet the deployment of ubiquitous monitoring sensors using traditional methods is often cost-prohibitive. This paper proposes an innovative entropy-aware masked autoencoder framework, namely TMAE, for low-cost traffic flow inference. TMAE leverages a small number of selectively measured regions with few deployed sensors to infer traffic flow across entire urban areas, incorporating prior knowledge from road distribution maps. Specifically, TMAE employs a shared encoder to process traffic flow context, using self-attention scores to identify the importance of each region and guide a masking policy that retains regions rich in traffic flow information. The road distribution map, reflecting inherent traffic flow patterns, is incorporated as prior knowledge by substituting masked tokens during training. A cross-attention mechanism in the decoder further refines inference, where embeddings from the road distribution map serve as queries, and retained visible patches act as keys and values. Additionally, regional traffic entropy is introduced to quantify the information richness of each region, enabling the selection of minimal measurement regions to optimize inference for other areas. Extensive experiments conducted on datasets from various cities demonstrate the effectiveness and efficiency of TMAE, highlighting its potential as a scalable solution for low-cost traffic flow inference in urban environments. The source code of this work is released at https://github.com/TextGraph/TMAE.

Federated learning (FL) is a promising privacy-preserving distributed machine learning paradigm. However, data privacy leakage and Byzantine clients are common challenges in the FL aggregation phase. While extensive research has been conducted to explore defenses for these risks independently, there is a notable lack of scholarly work on integrated defense strategies to address both challenges simultaneously. To bridge this gap, we propose a novel two-party secure robust aggregation (TPSRA) framework. The critical insight of TPSRA is to couple client-side gradient obfuscation with server-side secure two-party computation to achieve robust and private FL aggregation. Specifically, clients obfuscate and split local gradients using matrix theory, while servers utilize a novel secure multiparty computation protocol based on mutually orthogonal matrices to preserve the privacy of local gradients. Additionally, TPSRA designs and integrates state-of-the-art robust aggregation algorithms into compatible subprotocols, enabling efficient parallel computation. This establishes a highly efficient and versatile secure robust aggregation framework for FL. Experiments demonstrate that our TPSRA framework not only effectively resists gradient leakage attacks and detects malicious gradients, but also exhibits superior computational and communication efficiency. We also prove theoretically that TPSRA is secure under the semi-honest adversary model.

Federated learning allows clients to share model gradients instead of privacy-sensitive data, which can solve the issue of data silos, but lead to the problem of data privacy leakage due to the model gradient revealing the characteristics of the training data. Privacy-preserving federated learning based on homomorphic encryption schemes (HE-based PPFL) can properly solve the issues of participantsfs data privacy leakage, but they encounter some new challenges. Existing PPFL-based single-key homomorphic encryption schemes face the problem that clients can obtain othersf model gradients due to the shared key and PPFL-based multi-key homomorphic encryption schemes face the issues of incomplete privacy protection for models and high communication overhead due to the requirement of the collaborated decryption. Moreover, existing PPFL schemes either assume the server is always honest or the verification method is unreliable and expensive. To tackle these emerging challenges in HE-based PPFL, we propose an enhancing privacy-preserving and verifiable federated learning scheme. Specifically, we first construct a novel multi-key homomorphic encryption algorithm that achieves single-key decryption instead of the collaborated decryption in traditional PPFL-based multi-key homomorphic encryption. Meanwhile, we design a blockchain-based public verification method for the global model by applying a vector homomorphic hash, which can properly solve the issues of unreliable and expensive global model verification of the existing global model verification methods. Formal security analysis shows that the proposed scheme can well provide complete privacy protection and guarantee the integrity of the global model. Extensive experiments demonstrate that the proposed schemes can keep high accuracy (≈95%) compared with existing differential privacy-based PPFL schemes (≤90%). Meanwhile, the proposed schemes can achieve no decryption share size (0MB) compared to existing HE-based PPFL schemes and efficient verification compared wit

A critical challenge for predicting urban traffic flows is to simultaneously process time series and spatial features from heterogeneous traffic data collected by diverse Internet of Things (IoT) devices. Despite the advent of Transformer-based models with an advanced network structure and excellent prediction performance, standard Transformer models are still struggling to combine both spatial information and temporal relations of traffic flows. To address these challenges, we design a novel Transformer network, namely temporal-spatial traffic-flow Transformer (TST-Trans), for traffic flow prediction with high accuracy. In particular, we use learnable position encoders to replace traditional fixed position encoders. Meanwhile, we introduce a spatiotemporal embedding method that integrates temporal relationships and spatial information with external inputs, thereby capturing the spatiotemporal dependencies of traffic flows. Experiments with the real-world datasets demonstrate that our proposed TST-Trans achieves better prediction accuracy than state-of-the-art methods while requiring fewer parameters. The research results increased by more than 10% compared with Transformer. Compared to spatiotemporal deep hybrid neural network, there is a 2% to 10% improvement in performance on different datasets.

Inadequate resource coordination and control can result in poor quality of experience (QoE) for user devices in heterogeneous edge-enabled cyber-physical systems. Unfortunately, in a cooperative edge network, existing studies have rarely jointly optimized communication, computing resources, and batch size for QoE guarantee when controlling task offloading. To this end, we investigate the problem of harnessing bandwidth, computation, and batch size for fair quality of experience (HARBOR) in a practical collaborative edge-AI environment, where UEs have different accuracy requirements of inference services and edge devices possess different batch processing capabilities. Specifically, we introduce the task completion efficiency as the task-completion-time-to-deadline ratio to quantify individual QoE. Then, we formulate the problem HARBOR as a mixed integer nonlinear programming with constraints of accuracy, bandwidth, computation, task hard deadlines and so on. The objective is to minimize the maximum task completion efficiency among all tasks to achieve task-level fairness. After providing the NP-hardness proof for HARBOR, we then devise an efficient scheme named e-HARBOR with a competitive ratio guarantee, to solve the decoupled sub-problems of HARBOR with calibrated long short-term memory network for resource prediction. Both testbed and simulation experiments evidently demonstrate that the proposed scheme works efficiently and scales well compared to baselines.

Traditional reverse k-nearest neighbor (RkNN) query schemes typically assume that users are available online in real-time for interactive key reception, overlooking scenarios where users might be offline. Moreover, existing privacy-preserving RkNN query schemes primarily focus on user features or spatial data, neglecting the significance of user reputation values. To address these limitations, we propose a privacy-preserving resilient RkNN query scheme over encrypted outsourced multi-attribute data (PRRQ). Specifically, to mitigate the challenges posed by resilient online presence (i.e., non-real-time online) of users for interactive key reception, we incorporate a non-interactive key exchange (NIKE) protocol and the Diffie-Hellman two-party key exchange algorithm to propose a multi-party NIKE algorithm (2K-NIKE), facilitating non-interactive key reception for multiple users. Considering the privacy leakage issues, PRRQ encodes original multi-attribute data (i.e., spatial, feature, and reputation values) alongside query requests based on formalized criteria. Additionally, we integrate the proposed 2K-NIKE and the improved symmetric homomorphic encryption (iSHE) algorithms to encrypt them. Furthermore, catering to the requirements of ciphertext-based RkNN queries, we propose a private RkNN query eligibility-checking (PREC) algorithm and a private reputation-verifying (PRRV) algorithm, which validate the compliance of encrypted outsourced multi-attribute data with query requests. Security analysis demonstrates that PRRQ achieves simulation-based security under anhonest-but-curiousmodel. Experimental results show that PRRQ offers superior computational efficiency compared to comparative schemes.

With the explosion of big data in cloud environments, data owners tend to delegate the storage and computation to cloud servers. Since cloud servers are generally untrustworthy, data owners often encrypt data before outsourcing it to the cloud. Numerous privacy-preserving schemes for the multi-keyword ranked query have been proposed, but most of these schemes do not support ciphertext access control, which can easily lead to malicious access by unauthorized users, causing serious damage to personal privacy and commercial secrets. To address the above challenges, we propose an efficient and privacy-preserving multi-keyword ranked query scheme (MKAC) that supports ciphertext access control. Specifically, in order to enhance the efficiency of the multi-keyword ranked query, we employ a vantage point (VP) tree to organize the keyword index. Additionally, we develop a VP tree-based multi-keyword ranked query algorithm, which utilizes the pruning strategy to minimize the number of nodes to search. Next, we propose a privacy-preserving multi-keyword ranked query scheme that combines asymmetric scalar-product-preserving encryption with the VP tree. Furthermore, attribute-based encryption mechanism is used to generate the decryption key based on the query user's attributes, which is then employed to decrypt the query results and trace any malicious query user who may leak the secret key. Finally, a rigorous analysis of the security of MKAC is conducted. The extensive experimental evaluation shows that the proposed scheme is efficient and practical.

Knowledge distillation has emerged as a primary solution for anomaly detection, leveraging feature discrepancies between teacher–student (T–S) networks to locate anomalies. However, previous approaches suffer from ambiguous feature discrepancies, which hinder effective anomaly detection due to two main challenges: 1) overgeneralization, where the student network excessively mimics teacher features in anomalous regions, and 2) semantic bias between T–S networks in normal regions. To address these issues, we propose an Adaptive Discrepancy Optimization (Ado) block. The Ado block adaptively calibrates feature discrepancies by reducing overgeneralization in anomalous regions and selectively aligning semantic features in normal regions via learnable feature offsets. This versatile block can be seamlessly integrated into various distillation-based methods. Experimental results demonstrate that the Ado block significantly enhances performance across 11 different knowledge distillation frameworks on two widely used datasets. Notably, when integrated with the Ado block, RD4AD achieves a 22% relative improvement in pixel-level PRO on the VisA dataset. In addition, a real-world keyboard inspection application further validates the effectiveness of the Ado block.

Ensuring safety in both autonomous driving and advanced driver-assistance systems (ADAS) depends critically on the efficient deployment of traffic sign recognition technology. While current methods show effectiveness, they often compromise between speed and accuracy. To address this issue, we present a novel real-time and efficient road sign detection network, YOLO-TS. This network significantly improves performance by optimizing the receptive fields of multi-scale feature maps to align more closely with the size distribution of traffic signs in various datasets. Moreover, our innovative feature-fusion strategy, leveraging the flexibility of Anchor-Free methods, allows for multi-scale object detection on a high-resolution feature map abundant in contextual information, achieving remarkable enhancements in both accuracy and speed. To mitigate the adverse effects of the grid pattern caused by dilated convolutions on the detection of smaller objects, we have devised a unique module that not only mitigates this grid effect but also widens the receptive field to encompass an extensive range of spatial contextual information, thus boosting the efficiency of information usage. Moreover, to address the scarcity of traffic sign datasets, especially under adverse weather conditions, we introduce two novel datasets: Generated-TT100K-weather and CAWTSSS. Extensive evaluations conducted on challenging public benchmarks—including TT100K, CCTSDB2021, and GTSDB—as well as on our proposed datasets, demonstrate that YOLO-TS surpasses current state-of-the-art methods in both accuracy and inference speed. The code, datasets and weights are available athttps://github.com/Heqiang-Huang/YOLO-TS

Although Graph Neural Networks (GNNs) have exhibited the powerful ability to gather graph-structured information from neighborhood nodes via various message-passing mechanisms, the performance of GNNs is limited by poor generalization and fragile robustness caused by noisy and redundant graph data. As a prominent solution, Graph Augmentation Learning (GAL) has recently received increasing attention in the literature. Among the existing GAL approaches, edge-dropping methods that randomly remove edges from a graph during training are effective techniques to improve the robustness of GNNs. However, randomly dropping edges often results in bypassing critical edges. Consequently, the effectiveness of message passing is weakened. In this paper, we propose a novel adversarial edge-dropping method (ADEdgeDrop) that leverages an adversarial edge predictor guiding the removal of edges, which can be flexibly incorporated into diverse GNN backbones. Employing an adversarial training framework, the edge predictor utilizes the line graph transformed from the original graph to estimate the edges to be dropped, which improves the interpretability of the edge-dropping method. The proposed ADEdgeDrop is optimized alternately by stochastic gradient descent and projected gradient descent. Comprehensive experiments on eight graph benchmark datasets demonstrate that the proposed ADEdgeDrop outperforms state-of-the-art baselines across various GNN backbones, demonstrating improved generalization and robustness.

Federated learning provides a privacy-preserving modeling schema for distributed data, which coordinates multiple clients to collaboratively train a global model. However, data stored in different clients may be collected from diverse domains, and the resulting feature shift is prone to the degraded performance of global model. In this paper, we propose a Federated Domain-Independent Prototype Learning (FedDP) method with Alignments of Representation and Parameter Spaces for Feature Shift. Concretely, FedDP aims to eliminate the domain-specific information and explore the pure representations via information bottleneck, thus integrating the local and global domain-independent prototypes, respectively. To align the cross-domain representation spaces, the global domain-independent prototypes serve as the supervised signals to enable local intra-class representations to approach them. Further, to mitigate the divergences of optimization directions between multiple clients induced by the feature shift, the global representations are yielded by the global model on the client-side and guide the learning of local representations, thus unifying the parameter spaces of multiple local models. We derive the theoretical lower bound of the optimization objective based on mutual information, which is transformed into a computable loss. The proposed FedDP can be applied in the scenarios of homogeneous and heterogeneous models. Extensive experiments are conducted on three challenging multi-domain datasets. The experimental results illustrate the superiority of FedDP compared with state-of-the-art federated learning methods.

The spotlight on autonomous aerial vehicles (AAVs) is to enhance wireless communications while ignoring the potential risk of AAVs acting as adversaries. Due to their mobility and flexibility, AAV eavesdroppers pose an immeasurable threat to legitimate wireless transmissions. However, the existing fixed jamming scheme without cooperation cannot counter the flexible and dynamic AAV eavesdropping. In this article, a cooperative intelligent jamming scheme is proposed, authorizing ground jammers (GJs) to interfere with AAV eavesdroppers, generating specific jamming shields between AAV eavesdroppers and legitimate users. Toward this end, we formulate a secrecy capacity maximization problem and model the problem as a decentralized partially observable Markov decision process (Dec-POMDP). To address the challenge of the huge state space and action space with network dynamics, we leverage a deep reinforcement learning (DRL) algorithm with a dueling network and double-Q learning (i.e., dueling double deep Q-network) to train policy networks. Then, we propose a multi-agent mixing network framework (QMIX)-based collaborative jamming algorithm to enable GJs to independently make decisions without sharing local information. Additionally, we perform extensive simulations to validate the superiority of our proposed scheme and present useful insights into practical implementation by elucidating the relationship between the deployment settings of GJs and the instantaneous secrecy capacity.

User authentication is evolving with expanded applications and innovative techniques. New authentication approaches utilize RF signals to sense specific human characteristics, offering a contactless and nonintrusive solution. However, these RF signal-based methods struggle with challenges in open-world scenarios, i.e., dynamic environments, daily behaviors with unrestricted postures, and identification of unauthorized users with security threats. In this paper, we present an open-world user authentication system, OpenAuth, which leverages a commercial off-the-shelf (COTS) mmWave radar to sense unrestricted human postures and behaviors for identifying individuals. First, OpenAuth utilizes a MUSIC-based neural network imaging model to eliminate environmental clutter and generate environment-independent human silhouette images. Then, the human silhouette images are normalized to consistent topological structures of human postures, ensuring robustness against unrestricted human postures. Next, fine-grained body features are extracted from these environment-independent and posture-independent human silhouette images using a metric learning model. To eliminate potential security threats that arise from unauthorized users, OpenAuth synthesizes data placeholders for enhancing unauthorized user identification. Finally, a k-NN-based authentication model is constructed to authenticate users' identities. Experiments in real environments show that the proposed OpenAuth achieves an average authentication accuracy of 93.4% and false acceptance rate (FAR) of 1.8% in open-world scenarios.

Federated learning, as a privacy-preserving learning paradigm, restricts the access to data of each local client, for protecting the privacy of the parties. However, in the case of heterogeneous data settings, the different data distributions among clients usually lead to the divergence of learning targets, which is an essential challenge for federated learning. In this article, we propose a federated learning framework with a unified coding space, called FedUCS, for learning cross-client uniform coding rules to solve the problem of divergent targets among multiple clients due to heterogeneous data. A cross-client coordinator co-trained by multiple clients is used as a criterion of the coding space to supervise all clients coding to a uniform space, which is the significant contribution of this article. Furthermore, in order to appropriately retain historical information and avoid forgetting previous knowledge, a partial memory mechanism is applied. Moreover, in order to further enhance the distinguishability of the unified encoding space, supervised contrastive learning is used to avoid the intersection of the encoding spaces belonging to different categories. A series of experiments are performed to verify the effectiveness of the proposed method in a federated learning setting with heterogeneous data.

Virtual Reality (VR) has accelerated its prevalent adoption in emerging metaverse applications, but it is not a fundamentally new technology. On the one hand, most VR operating systems (OS) are based on off-the-shelf mobile OS (e.g., Android OS). As a result, VR apps also inevitably inherit privacy and security deficiencies from conventional mobile apps. On the other hand, in contrast to traditional mobile apps, VR apps can achieve an immersive experience via diverse VR devices, such as head-mounted displays, body sensors, and controllers. However, achieving this requires the extensive collection of privacy-sensitive human biometrics (e.g., hand-tracking and face-tracking data). Moreover, VR apps have been typically implemented by 3D gaming engines (e.g., Unity), which also contain intrinsic security vulnerabilities. Inappropriate use of these technologies may incur privacy leaks and security vulnerabilities although these issues have not received significant attention compared to the proliferation of diverse VR apps. In this paper, we develop a security and privacy assessment tool, namely the VR-SP detector for VR apps. The VR-SP detector has integrated program static analysis tools and privacy-policy analysis methods. Using the VR-SP detector, we conduct a comprehensive empirical study on 900 popular VR apps. We obtain the original apps from the popular SideQuest app store and extract Android PacKage (APK) files via the Meta Quest 2 device. We evaluate the security vulnerabilities and privacy data leaks of these VR apps through VR app analysis, taint analysis, privacy policy analysis, and user review analysis. We find that a number of security vulnerabilities and privacy leaks widely exist in VR apps. Moreover, our results also reveal conflicting representations in the privacy policies of these apps and inconsistencies of the actual data collection with the privacy-policy statements of the apps. Further, user reviews also indicate their privacy concerns about relevant biometric data. Based on these findings, we make suggestions for the future development of VR apps.

Sea state information is significant for the guide of maritime activities of autonomous vessels. The sea state estimation (SSE) model, powered by artificial intelligence (AI), has shown great effectiveness but is susceptible to malicious data attacks. These attacks can lead to significant declines in the system’s performance and result in incorrect predictions about the sea state. This study introduces SecureSSE, a strategy for protecting SSE models in autonomous marine vessels from adversarial attacks. This approach incorporates three main components: 1) the multiscale feature extraction learning (MFEL) module; 2) the feature convolution aggregation learning (FCAL) module; and 3) the perturbation examples training (PET) module. The PET module is specifically crafted to create perturbation examples that are in line with unaltered data, leveraging the capabilities of both the MFEL and FCAL modules to efficiently extract and integrate detailed features from ship motion data. Our proposed SecureSSE approach is shown to significantly improve the resilience of deep learning models against potential attacks. Through experimental testing, we have validated the effectiveness of this method in enhancing SSE. Additional ablation studies highlight the critical role of each module within the SecureSSE framework. To our knowledge, this is the first study to address adversarial attacks in this context and to propose a comprehensive defense mechanism for SSE systems in autonomous marine vessels.

User authentication is evolving with expanded application scenarios and innovative techniques. New authentication approaches utilize RF signals to sense specific human behaviors and characteristics, such as faces, specific gestures, etc., offering a contactless and nonintrusive solution. However, these RF signal-based methods struggle with challenges in open-world scenarios, i.e., dynamic environments, daily behaviors with unrestricted postures, and identification of unauthorized users with security threats. In this paper, we present an open-world user authentication system, OpenAuth, which leverages a commercial off-the-shelf (COTS) mmWave radar to sense unrestricted human postures and behaviors for identifying individuals. First, OpenAuth utilizes a MUSIC-based neural network imaging model to eliminate environmental clutter and generates environment-independent human silhouette images. Then, the human silhouette images are normalized to consistent topological structures of human postures, ensuring robustness against unrestricted human postures. Based on the environment-independent and posture-independent human silhouette images, OpenAuth further extracts fine-grained body features through a metric learning model for user authentication. To eliminate potential security threats that arise from frequent accesses by unauthorized users, OpenAuth synthesizes data placeholders for enhancing the applicability of unauthorized user identification. Finally, a k-NN-based authentication model is constructed based on the extracted body features to authenticate users' identities. Experiments in real environments show that the proposed OpenAuth achieves an average authentication accuracy of 93.4 % and false acceptance rate (FAR) of 1.8% in open-world scenarios.

Recently, stateless blockchains have been proposed to alleviate the storage overhead for nodes. A stateless blockchain achieves storage-consensus parallelism, where storage workloads are offloaded from on-chain consensus, enabling more resource-constraint nodes to participate in the consensus. However, existing stateless blockchains still suffer from limited throughput. In this paper, we present Porygon, a novel stateless blockchain with three-dimensional (3D) parallelism. First, Porygon separates the storage and consensus of transactions as the stateless blockchain, achieving the storage-consensus parallelism. This first-dimensional parallelism divides the processing of transactions into several stages and scales the network by supporting more nodes in the system. Based on such a design, we then propose a pipeline mechanism to achieve second-dimensional inter-block parallelism, where relevant stages of processing transactions are pipelined efficiently, thereby reducing transaction latency. Finally, Porygon presents a sharding mechanism to achieve third-dimensional inner-block parallelism. By sharding the executions of transactions of a block and adopting a lightweight cross-shard coordination mechanism, Porygon can effectively execute both intra-shard and cross-shard transactions, consequently achieving outstanding transaction throughput. We evaluate the performance of Porygon by extensive experiments on an implemented prototype and large-scale simulations. Compared with existing blockchains, Porygon boosts throughput by up to 20x, reduces network usage by more than 50%, and simultaneously requires only 5MB of storage consumption per node.

Although Virtual Reality (VR) has accelerated its prevalent adoption in emerging metaverse applications, it is not a fundamentally new technology. On one hand, most VR operating systems (OS) are based on off-the-shelf mobile OS (e.g., Android). As a result, VR apps also inherit privacy and security deficiencies from conventional mobile apps. On the other hand, in contrast to conventional mobile apps, VR apps can achieve immersive experience via diverse VR devices, such as head-mounted displays, body sensors, and controllers though achieving this requires the extensive collection of privacy-sensitive human biometrics (e.g., hand-tracking and face-tracking data). Moreover, VR apps have been typically implemented by 3D gaming engines (e.g., Unity), which also contain intrinsic security vulnerabilities. Inappropriate use of these technologies may incur privacy leaks and security vulnerabilities although these issues have not received significant attention compared to the proliferation of diverse VR apps. In this paper, we develop a security and privacy assessment tool, namely the VR-SP detector for VR apps. The VR-SP detector has integrated program static analysis tools and privacy-policy analysis methods. Using the VR-SP detector, we conduct a comprehensive empirical study on 500 popular VR apps. We obtain the original apps from the popular Oculus and SideQuest app stores and extract APK files via the Meta Oculus Quest 2 device. We evaluate security vulnerabilities and privacy data leaks of these VR apps by VR app analysis, taint analysis, and privacy-policy analysis. We find that a number of security vulnerabilities and privacy leaks widely exist in VR apps. Moreover, our results also reveal conflicting representations in the privacy policies of these apps and inconsistencies of the actual data collection with the privacy-policy statements of the apps. Based on these findings, we make suggestions for the future development of VR apps.

As an essential component in Ethereum and other blockchains, token assets have been interacted with by diverse smart contracts. Effective permission policies of smart contracts must prevent token assets from being manipulated by unauthorized adversaries. Recent efforts have studied the accessibility of privileged functions or state variables to unauthorized users. However, little attention is paid to how publicly accessible functions of smart contracts can be manipulated by adversaries to steal users' digital assets. This attack is mainly caused by the permission re-delegation (PRD) vulnerability. In this work, we propose PrettySmart, a bytecode-level Permission re-delegation vulnerability detector for Smart contracts. Our study begins with an empirical study on 0.43 million open-source smart contracts, revealing that five types of widely-used permission constraints dominate 98% of the studied contracts. Accordingly, we propose a mechanism to infer these permission constraints, as well as an algorithm to identify constraints that can be bypassed by unauthorized adversaries. Based on the identification of permission constraints, we propose to detect whether adversaries could manipulate the privileged token management functionalities of smart contracts. The experimental results on real-world datasets demonstrate the effectiveness of the proposed PrettySmart, which achieves the highest precision score and detects 118 new PRD vulnerabilities.

Since the advent of decentralized financial applications based on blockchains, new attacks that take advantage of manipulating the order of transactions have emerged. To this end, order fairness protocols are devised to prevent such order manipulations. However, existing order fairness protocols adopt time-consuming mechanisms that bring huge computation overheads and defer the finalization of transactions to the following rounds, eventually compromising system performance. In this work, we present Auncel, a novel consensus protocol that achieves both order fairness and high performance. Auncel leverages a weight-based strategy to order transactions, enabling all transactions in a block to be committed within one consensus round, without cost computation and further delays. Furthermore, Auncel achieves censorship resistance by integrating the consensus protocol with the fair ordering strategy, ensuring all transactions can be ordered fairly. To reduce the overheads introduced by the fair ordering strategy, we also design optimization mechanisms, including dynamic transaction compression and adjustable replica proposal strategy. We implement a prototype of Auncel based on HotStuff and construct extensive experiments. Experimental results show that Auncel can increase the throughput by 6× and reduce the confirmation latency by 3× compared with state-of-the-art order fairness protocols.

Mobile crowdsourcing federated learning (FL-MCS) allows a requester to outsource its model-training tasks to other workers who have the desired data as well as strong computing power. FL-MCS can thereby overcome the limitations of computing capability as well as the data availability of participants. However, FL-MCS still faces the problem of workers’ data privacy leakage when diverse malicious attacks (e.g., gradient inference attacks) are launched. To address these problems, some privacy-preserving FL-MCS (PPFL-MCS) schemes are proposed to aggregate local models at a central server. Unfortunately, these schemes are vulnerable to single-point-of-failure and other malicious attacks at the central server. Meanwhile, the workers may drop from the online task due to the erratic communication network in PPFL-MCS schemes, thereby resulting in the failure of the entire model aggregation. To solve these issues, we propose a novel dropout-tolerated and privacy-preserving decentralized FL-MCS scheme, namely DTPP-DFL-MCS based on blockchain. Specifically, we define a novel cryptographic primitive, i.e., ID-based Aggregated Decryptable Broadcast Encryption (AD-IBBE) based on traditional ID-based broadcast encryption. In AD-IBBE, the senders’ ciphertexts can only be decrypted by themselves while the aggregated ciphertexts can be decrypted by all receivers in the broadcast group. Then, we design a homomorphic AD-IBBE algorithm, which is formally proved to be semantically secure. We next devise the decentralized PPFL-MCS scheme to guarantee the confidentiality of model gradients against internal and external adversaries. Moreover, we design a dropout-tolerated aggregation method to ensure the robustness of our decentralized PPFL-MCS scheme even if some workers lose connection. Extensive experimental results on different models and datasets demonstrate that the proposed scheme guarantees a close model accuracy to the non-dropout case. Even when some workers are offline, our scheme still performs more efficiently than existing schemes in terms of dropout aggregation overhead.

It is of the utmost importance to achieve flexible and fine-grained access control of electronic health records (EHR) in smart elderly healthcare (SEH) for providing high-quality healthcare services for the elderly and protecting their privacy simultaneously. In this paper, a flexible, fine-grained, and elderly-centric access control scheme is presented for EHR data in SEH. In the proposed scheme, Ciphertext Policy Attribute Based Encryption (CP-ABE), permission token, dual-key regression, and blockchain techniques are leveraged to realize multi-dimensional access control of EHR data in terms of data generation time, data user properties, access times, and access period. Moreover, a novel token segmentation algorithm is designed to transfer access rights between doctors efficiently for multi-party diagnosis and treatment. Since the elderly can define the attributes of users accessing his/her EHR data, the access number, the access time, and the access range of data from the time dimension of data generation with the cooperation of the Smart Elderly Healthcare (SEH) institution, the privacy of EHR data of the elderly is well protected. The security analysis demonstrates that our scheme can achieve EHR ciphertext indistinguishability under chosen-plaintext attacks and token unlinkability and unforgeability under data users’ collusion attacks. The experimental results show that our scheme performs well in terms of time cost and computational overhead.

The 6G network is expected to accommodate a wide array of connected devices, supporting diverse services from any location at any time. In this article, we introduce an aerial mobile edge computing (MEC) framework composed of high-altitude platforms (HAPs) and low-altitude unmanned aerial vehicles (UAVs), to cater to computing offloading for Internet of Things (IoT) devices, particularly in rural/remote areas or disaster zones. The framework accommodates various types of tasks, each computed by the corresponding Docker container. The objective is to achieve optimal workload fairness for UAVs while simultaneously minimizing the weighted processing costs among IoT devices in terms of task computation latency and energy consumption over the long term. This is achieved by jointly optimizing the flight trajectories and Docker image caching decisions of the UAVs with limited storage capacities, alongside ensuring service fairness for IoT devices. We tailor a multiagent deep deterministic policy gradient (MADDPG)-based approach to solve the long-term joint optimization problem, normalizing continuous actions and sampling discrete actions by generalizing the Gumbel-Softmax reparameterization trick. Experimental results indicate that our approach significantly outperforms benchmark schemes in terms of processing delay, energy consumption, and fairness.

Presently, resource-constrained devices in manufacturing Internet of Things (MIoT), such as sensors and radio frequency identification (RFID) devices, collect a large amount of privacy-sensitive data. However, weak passwords and vulnerable encryption capabilities in MIoT have often become loopholes of security risks. To this end, this paper proposes a novel secure data-sharing scheme based on the integration of blockchain and fusion of both real and fake data to address the data security requirements of resource-constrained MIoT devices. First, a computing resource collaboration architecture is designed, thereby enabling these devices to interface with multiple devices with full resource to implement flexible resource scheduling. Then, a resource-assistance mechanism is devised through blockchain-based smart contracts by utilizing the idle resources of full nodes to complement the computational tasks launched by resource-constrained nodes. In addition, polygon semantic rules are proposed to improve the security of private data. Subsequently, real data artifacts are generated by data tampering to achieve privacy cover, avoiding the consumption of computing resources in traditional encryption algorithms. Finally, the feasibility of the proposed scheme is verified on a customized candy production line. The experimental results validate that data protection using polygon semantic rules can prevent actual data from being peeped. Moreover, the results also indicate that the proposed method can obtain resource assistance from other nodes through the resource compensation mechanism.

With the intensification of mobile devices, vast amounts of spatial data have been outsourced to cloud servers to provide query services. However, existing privacy-preserving schemes for spatial data only support spatial range queries and keyword searches and do not scale well in the scenario of multidimensional range queries. To address the above challenges, we propose a privacy-preserving scheme for the multidimensional range query with keyword search over spatial data (PMRK). Specifically, based on the encoding technique, we design data comparison and text matching algorithms, which can convert range queries and keyword searches into Hadamard-product-based operations. To improve the search efficiency, we index the spatial data by R-tree and propose the range intersection algorithm to implement the multidimensional range query with keyword search on R-tree simultaneously. Furthermore, the homomorphic encryption and matrix encryption techniques are leveraged to design the intersection predicate encryption (IPE) and subset predicate encryption (SPE) schemes, which preserve the privacy of range queries and keyword searches. Then, we propose our PMRK scheme, which not only supports efficient and secure multidimensional range queries and keyword searches at the same time but also preserves the single-dimensional privacy for multidimensional queries, and the path pattern privacy of the R-tree. In addition, the security of IPE and SPE is formally proved, and the security of PMRK is analyzed. In the experimental part, the feasibility and efficiency of PMRK are demonstrated by conducting experiments on real data sets.

The advent of the Internet of Medical Things (IoMT) has greatly fastened the digitization of current medical institutions. Mobile crowdsourcing is an effective strategy for health data collection in IoMT environments to overcome the data-scarce problem. However, due to the openness of IoMT networks, users’ identities and sensitive data may be leaked during IoMT crowdsourcing. Meanwhile, IoMT crowdsourcing may introduce low-quality data from unreliable participants. Multireceiver signcryption is a promising mechanism to ensure confidentiality and authenticity in an efficient manner. However, existing multireceiver signcryptions cannot fully meet the needs of IoMT crowdsourcing in terms of privacy protection, on-demand participation, and malicious behavior resistance. In this article, we integrate attribute-based credentials with multireceiver encryption and propose a novel multireceiver conditional anonymous signcryption (MCAS) scheme for crowdsourced IoMT environments to address the above challenge. Specifically, conditional anonymous authentication with selective attribute disclosure is achieved, thereby allowing a worker to self-disclose some attributes and anonymously certify his/her crowdsourcing qualifications, and also achieving the traceability of malicious behaviors. Meanwhile, one-to-many secure data sharing with outsourced data signcryption and unsigncryption is realized to prevent the leakage of sensitive IoMT data and mitigate the computational burden of power-limited mobile devices. Moreover, rigorous security analysis demonstrates that our MCAS scheme achieves the expected properties, i.e., confidentiality, anonymity, fine-grained authentication, traceability, and nonrepudiation. Extensive experimental results show that our MCAS outperforms state-of-the-art schemes, demonstrating our scheme’s appropriateness for IoMT crowdsourcing.

By integrating the merits of aerial, terrestrial, and satellite communications, the space-air-ground integrated network (SAGIN) is an emerging solution that can provide massive access, seamless coverage, and reliable transmissions for global-range applications. In SAGINs, the uplink connectivity from ground users (GUs) to the satellite is essential because it ensures global-range data collections and interactions, thereby paving the technical foundation for practical implementations of SAGINs. In this article, we aim to establish an accurate analytical model for the uplink connectivity of SAGINs in consideration of the global distributions of both GUs and aerial vehicles (AVs). Particularly, we investigate the uplink path connectivity of SAGINs, which refers to the probability of establishing the end-to-end path from GUs to the satellite with or without AV relays. However, such an investigation on SAGINs is challenging because all GUs and AVs are approximately distributed on a spherical surface (instead of the horizontal surface), resulting in the complexity of network modeling. To address this challenge, this paper presents a new analytical approach based on spherical stochastic geometry. Based on this approach, we derive the analytical expression of the path connectivity in SAGINs. Extensive simulations confirm the accuracy of the analytical model.

Although deep learning for Big Data analytics has achieved promising results in the field of optical coherence tomography (OCT) image denoising, the low recognition rate caused by complex noise distribution and a large number of redundant features is still a challenge faced by deep learning-based denoising methods. Moreover, the network with large depth will bring high computational complexity. To this end, we propose a progressive feature fusion attention dense network (PFFADN) for speckle noise removal in OCT images. We arrange densely connected dense blocks in the deep convolution network, and sequentially connect the shallow convolution feature map with the deep one extracted from each dense block to form a residual block. We add attention mechanism to the network to extract the key features and suppress the irrelevant ones. We fuse the output feature maps from all dense blocks and input them to the reconstruction output layer. We compare PFFADN with the state-of-the-art denoising algorithms on retinal OCT images. Experiments show that our method has better improvement in denoising performance.

In this paper, we propose a novel Multi-keyword Fuzzy Symmetric Searchable Encryption (SSE) with patterns hidden, namely MFSSE. In MFSSE, the search trapdoor can be modified differently each time even if the keywords are the same when performing multi-keyword search to prevent the leakage of search patterns. Moreover, MFSSE modifies the search trapdoor by introducing random false negative and false positive errors to resist access pattern leakage. Furthermore, MFSSE utilizes efficient cryptographic algorithms (e.g., Locality-Sensitive Hashing) and lightweight operations (such as, integer addition, matrix multiplication, etc.) to minimize computational and communication, and storage overheads on mobile devices while meeting security and functional requirements. Specifically, its query process requires only a single round of communication, in which, the communication cost is linearly related to the number of the documents in the database, and is independent of the total number of keywords and the number of queried keywords; its computational complexity for matching a document is$O(1)$; and it requires only a small amount of fixed local storage (i.e., secret key) to be suitable for mobile scenarios. The experimental results demonstrate that MFSSE can prevent the leakage of access patterns and search patterns, while keeping a low communication and computation overheads.

Private car-based carpooling (PCC) has become an important transportation mode in our daily life. Unlike ride-hailing or taxi-based carpooling, PCC has two unique features that have yet to be fully explored: (i) A private-car driver has more bargaining space than a non-private car driver; (ii) There exists unfriendly congestion in private car-based carpooling if not handled well. Existing carpooling schemes are not tailored for PCC services with an oversimplified assumption that passengers pay detour fees and there is no guarantee on the passenger’s travel time. Consequently, such limitations not only harm the passenger’s carpooling incentive but also hurt the passenger’s quality of experience as well as the driver’s utility. We propose a novel framework for the double-sided preference-aware carpooling (D-SPAC) problem, after comprehensively addressing the above two unique features. We formulate the D-SPAC problem as a mixed-integer non-linear programming problem, which is proved to be NP-hard, to maximize the total utility of passengers while meeting the driver’s buyout asking price, traversal radius, passenger’s waiting time, budget and both sides’ detour length constraints. We design a coalitional double auction-based scheme that can better motivate both sides with guaranteed economic properties. We further design a deep reinforcement learning algorithm to cope with the position dynamics and the changing user requests. Extensive experimental results based on real-world data sets demonstrate the effectiveness of proposed algorithms over three benchmark algorithms.

Being the largest Initial Coin Offering project, EOSIO has attracted great interest in cryptocurrency markets. Despite its popularity and prosperity (e.g., 26,311,585,008 token transactions occurred from June 8, 2018 to Aug. 5, 2020), there is almost no work investigating the EOSIO token ecosystem. To fill this gap, we are the first to conduct a systematic investigation of the EOSIO token ecosystem by conducting a comprehensive graph analysis of the entire on-chain EOSIO data (nearly 135 million blocks). We construct token-creator graphs, token-contract creator graphs, token-holder graphs, and token-transfer graphs to characterize token creators, holders, and transfer activities. Through graph analysis, we have obtained many insightful findings and observed some abnormal trading patterns. Moreover, we propose a fake-token detection algorithm to identify tokens generated by fake users or fake transactions and analyze their corresponding manipulation behaviors. Evaluation results also demonstrate the effectiveness of our algorithm.

In wireless content caching networks (WCCNs), a user's content consumption crucially depends on the assortment offered. Here, the assortment refers to the recommendation list. An appropriate user choice model is essential for greater revenue. Therefore, in this paper, we propose a practical multinomial logit choice model to capture users' content requests. Based on this model, we first derive the individual demand distribution per user and then investigate the effect of the interplay between the assortment decision and cache planning on WCCNs' achievable revenue. A revenue maximization problem is formulated while incorporating the influences of the screen size constraints of users and the cache capacity budget of the base station (BS). The formulated optimization problem is a non-convex integer programming problem. For ease of analysis, we decompose it into two folds, i.e., the personalized assortment decision problem and the cache planning problem. By using structure-oriented geometric properties, we design an iterative algorithm with examinable quadratic time complexity to solve the non-convex assortment problem in an optimal manner. The cache planning problem is proved to be a 0-1 Knapsack problem and thus can be addressed by a dynamic programming approach with pseudo-polynomial time complexity. Afterwards, an alternating optimization method is used to optimize the two types of variables until convergence. It is shown by simulations that the proposed scheme outperforms various existing benchmark schemes.

Mobile crowdsensing (MCS) can promote data acquisition and sharing among mobile devices. Traditional MCS platforms are based on a triangular structure consisting of three roles: data requester, worker (i.e. , sensory data provider) and MCS platform. However, this centralized architecture suffers from poor reliability and difficulties in guaranteeing data quality and privacy, even provides unfair incentives for users. In this paper, we propose a blockchain-based MCS platform, namely BlockSense, to replace the traditional triangular architecture of MCS models by a decentralized paradigm. To achieve the goal of trustworthiness of BlockSense, we present a novel consensus protocol, namely Proof-of-Data (PoD), which leverages miners to conduct useful data quality validation work instead of “useless” hash calculation. Meanwhile, in order to preserve the privacy of the sensory data, we design a homomorphic data perturbation scheme, through which miners can verify data quality without knowing the contents of the data. We have implemented a prototype of BlockSense and conducted case studies on campus, collecting over 7,000 data from workers' mobile phones. Both simulations and real-world experiments show that BlockSense can not only improve system security, preserve data privacy and guarantee incentives fairness, but also achieve at least 5.6x faster than Ethereum smart contracts in verification efficiency.

Automated classification of breast cancer subtypes from digital pathology images has been an extremely challenging task due to the complicated spatial patterns of cells in the tissue micro-environment. While newly proposed graph transformers are able to capture more long-range dependencies to enhance accuracy, they largely ignore the topological connectivity between graph nodes, which is nevertheless critical to extract more representative features to address this difficult task. In this paper, we propose a novel connectivity-aware graph transformer (CGT) for phenotyping the topology connectivity of the tissue graph constructed from digital pathology images for breast cancer classification. Our CGT seamlessly integrates connectivity embedding to node feature at every graph transformer layer by using local connectivity aggregation, in order to yield more comprehensive graph representations to distinguish different breast cancer subtypes. In light of the realistic intercellular communication mode, we then encode the spatial distance between two arbitrary nodes as connectivity bias in self-attention calculation, thereby allowing the CGT to distinctively harness the connectivity embedding based on the distance of two nodes. We extensively evaluate the proposed CGT on a large cohort of breast carcinoma digital pathology images stained by Haematoxylin & Eosin. Experimental results demonstrate the effectiveness of our CGT, which outperforms state-of-the-art methods by a large margin. Codes are released on https://github.com/wang-kang-6/CGT.

Tensor analysis has received widespread attention in high-dimensional data learning. Unfortunately, the tensor data are often accompanied by arbitrary signal corruptions, including missing entries and sparse noise. How to recover the characteristics of the corrupted tensor data and make it compatible with the downstream clustering task remains a challenging problem. In this article, we study a generalized transformed tensor low-rank representation (TTLRR) model for simultaneously recovering and clustering the corrupted tensor data. The core idea is to find the latent low-rank tensor structure from the corrupted measurements using the transformed tensor singular value decomposition (SVD). Theoretically, we prove that TTLRR can recover the clean tensor data with a high probability guarantee under mild conditions. Furthermore, by using the transform adaptively learning from the data itself, the proposed TTLRR model can approximately represent and exploit the intrinsic subspace and seek out the cluster structure of the tensor data precisely. An effective algorithm is designed to solve the proposed model under the alternating direction method of multipliers (ADMMs) algorithm framework. The effectiveness and superiority of the proposed method against the compared methods are showcased over different tasks, including video/face data recovery and face/object/scene data clustering.

Code commenting plays an important role in program comprehension. Automatic comment generation helps improve software maintenance efficiency. The code comments to annotate a method mainly include header comments and snippet comments. The header comment aims to describe the functionality of the entire method, thereby providing a general comment at the beginning of the method. The snippet comment appears at multiple code segments in the body of a method, where a code segment is called a code snippet. Both of them help developers quickly understand code semantics, thereby improving code readability and code maintainability. However, existing automatic comment generation models mainly focus more on header comments, because there are public datasets to validate the performance. By contrast, it is challenging to collect datasets for snippet comments, because it is difficult to determine their scope. Even worse, code snippets are often too short to capture complete syntax and semantic information. To address this challenge, we propose a novel S nippet C omment Gen eration approach called SCGen . First, we utilize the context of the code snippet to expand the syntax and semantic information. Specifically, 600,243 snippet code-comment pairs are collected from 959 Java projects. Then, we capture variables from code snippets and extract variable-related statements from the context. After that, we devise an algorithm to parse and traverse abstract syntax tree (AST) information of code snippets and corresponding context. Finally, SCGen generates snippet comments after inputting the source code snippet and corresponding AST information into a sequence-to-sequence-based model. We conducted extensive experiments on the dataset we collected to evaluate our SCGen . Our approach obtains 18.23 in BLEU-4 metrics, 18.83 in METEOR, and 23.65 in ROUGE-L, which outperforms state-of-the-art comment generation models.

With the increasing popularity of Decentralized Applications (DApps) in blockchain, securing smart contracts has been a long-term, high-priority subject in the domain. Among the various research directions for vulnerability detection, fuzzing has received extensive attention because of its high effectiveness. However, with the increasing complexity of smart contracts, existing fuzzers may waste substantial time exploring locations irrelevant to smart contract vulnerabilities. In this article, we present FunFuzz, a function-oriented fuzzer, which is dedicatedly tailored for detecting smart contract vulnerability with high effectiveness and efficiency. The key observation in our research is that most smart contract vulnerabilities exist in specific functions rather than randomly distributed in all program code like other traditional software. To this end, unlike traditional fuzzers which mainly target code coverage, FunFuzz identifies risky functions while pruning non-risky ones in smart contracts. In this way, it significantly narrows down the exploration scope during the fuzzing process. In addition, FunFuzz employs three unique strategies to direct itself toward effectively discovering vulnerabilities specific to smart contracts (e.g., reentrancy, block dependency, and gasless send). Extensive experiments on 170 real-world contracts demonstrate that FunFuzz outperforms state-of-the-art fuzzers in terms of effectiveness and efficiency.

Sea State Estimation (SSE) is essential for Internet of Things (IoT)-enabled autonomous ships, which rely on favorable sea conditions for safe and efficient navigation. Traditional methods, such as wave buoys and radars, are costly, less accurate, and lack real-time capability. Model-driven methods, based on physical models of ship dynamics, are impractical due to wave randomness. Data-driven methods are limited by the data imbalance problem, as some sea states are more frequent and observable than others. To overcome these challenges, we propose a novel data-driven approach for SSE based on ship motion data. Our approach consists of three main components: a data preprocessing module, a parallel convolution feature extractor, and a theoretical-ensured distance-based classifier. The data preprocessing module aims to enhance the data quality and reduce sensor noise. The parallel convolution feature extractor uses a kernel-varying convolutional structure to capture distinctive features. The distance-based classifier learns representative prototypes for each sea state and assigns a sample to the nearest prototype based on a distance metric. The efficiency of our model is validated through experiments on two SSE datasets and the UEA archive, encompassing thirty multivariate time series classification tasks. The results reveal the generalizability and robustness of our approach.

For the complicated input-output systems with nonlinearity and stochasticity, Deep State Space Models (SSMs) are effective for identifying systems in the latent state space, which are of great significance for representation, forecasting, and planning in online scenarios. However, most SSMs are designed for discrete-time sequences and inapplicable when the observations are irregular in time. To solve the problem, we propose a novel continuous-time SSM named Ordinary Differential Equation Recurrent State Space Model (ODE-RSSM). ODE-RSSM incorporates an ordinary differential equation (ODE) network (ODE-Net) to model the continuous-time evolution of latent states between adjacent time points. Inspired from the equivalent linear transformation on integration limits, we propose an efficient reparameterization method for solving batched ODEs with non-uniform time spans in parallel for efficiently training the ODE-RSSM with irregularly sampled sequences. We also conduct extensive experiments to evaluate the proposed ODE-RSSM and the baselines on three input-output datasets, one of which is a rollout of a private industrial dataset with strong long-term delay and stochasticity. The results demonstrate that the ODE-RSSM achieves better performance than other baselines in open loop prediction even if the time spans of predicted points are uneven and the distribution of length is changeable. Code is availiable at https://github.com/yuanzhaolin/ODE-RSSM.

Federated Learning (FL) enables participants to collaboratively train a global model by sharing their gradients without the need for uploading privacy-sensitive data. Despite certain privacy preservation of FL, local gradients in plaintext may reveal data privacy when gradient-leakage attacks are launched. To further protect local gradients, privacy-preserving FL schemes have been proposed. However, these existing schemes that require a fully trusted central server are vulnerable to a single point of failure and malicious attacks. Although more robust privacy-preserving decentralized FL schemes have recently been proposed on multiple servers, they will fail to aggregate the local gradients with message transmission errors or data packet dropping out due to the instability of the communication network. To address these challenges, we propose a novel privacy-preserving decentralized FL scheme system based on the blockchain and a modified identity-based homomorphic broadcast encryption algorithm. This scheme achieves both privacy protection and error/dropout tolerance. Security analysis shows that the proposed scheme can protect the privacy of the local gradients against both internal and external adversaries, and protect the privacy of the global gradients against external adversaries. Moreover, it ensures the correctness of local gradients' aggregation even when transmission error or data packet dropout happens. Extensive experiments demonstrate that the proposed scheme guarantees model accuracy and achieves performance efficiency.

Backscatter communication (BackCom) is an intriguing technology that enables devices to transmit information by reflecting environmental radio frequency signals while consuming ultra-low energy. Applying BackCom in the Internet of things (IoT) networks can effectively address the power-unsustainability issue of energy-constraint devices. Considering many practical IoT applications, networks are finite-scale and devices are needed to be deployed at hotspot regions organized in clusters to cooperate for specific tasks. This paper considers finite-scale clustered backscatter communication networks (F-CBackCom Nets). To ensure communications, this paper establishes a theoretic model to analyze the communication connectivity of F-CBackCom Nets. Different from prior studies analyzing the connectivity with a focus on the transmission pair located at the center of the network, this paper analyzes the connectivity of a transmission pair located in an arbitrary location, because the performance of transmission pairs potentially varies with their network location. Extensive simulations validate the accuracy of our analytical model. Our results show that the connectivity of a transmission pair can be affected by its network location. Our analytical model and results can offer beneficial implications for constructing F-CBackCom Nets.

It is a challenging task to teach machines to paint like human artists in a stroke-by-stroke fashion. Despite advances in stroke-based image rendering and deep learning-based image rendering, existing painting methods have limitations: they (i) lack flexibility to choose different art-style strokes, (ii) lose content details of images, and (iii) generate few artistic styles for paintings. In this paper, we propose a stroke-style generative adversarial network, called Stroke-GAN, to solve the first two limitations. Stroke-GAN learns styles of strokes from different stroke-style datasets, so can produce diverse stroke styles. We design three players in Stroke-GAN to generate pure-color strokes close to human artists’ strokes, thereby improving the quality of painted details. To overcome the third limitation, we have devised a neural network named Stroke-GAN Painter, based on Stroke-GAN; it can generate different artistic styles of paintings. Experiments demonstrate that our artful painter can generate various styles of paintings while well-preserving content details (such as details of human faces and building textures) and retaining high fidelity to the input images.

The recent proliferation of Internet of Things (IoT) sensors has driven a myriad of industrial and urban applications. Through analyzing massive data collected by these sensors, the proactive maintenance management can be achieved such that the maintenance schedule of the installed equipment can be optimized. Despite recent progress in proactive maintenance management in industrial scenarios, there are few studies on proactive maintenance management in urban informatics. In this article, we present an integrated framework of IoT and cloud computing platform for the proactive maintenance management in smart city. Our framework consists of: 1) an IoT monitoring system for collecting time-series data of operating and ambient conditions of the equipment and 2) a hybrid deep learning model, namely, convolutional bidirectional long short-term memory (CBLM) model for forecasting the operating and ambient conditions based on the collected time-series data. In addition, we also develop a naïve Bayes classifier to detect abnormal operating and ambient conditions and assist management personnel in scheduling maintenance tasks. To evaluate our framework, we deployed the IoT system in a Hong Kong public toilet, which is the first application of proactive maintenance management for a public hygiene and sanitary facility to the best of our knowledge. We collected the sensed data more than 33 days (808 h) in this real system. Extensive experiments on the collected data demonstrated that our proposed CBLM outperformed six traditional machine learning algorithms.

For future Internet of Things (IoT) systems, data-driven and dynamic spectrum-sharing schemes can significantly improve the spectrum utilization and efficiency. However, conventional centralized architecture of such dynamic IoT spectrum-sharing systems is often considered to be nontransparent, costly, and vulnerable to potential attacks and single-point failures. To address the aforementioned issues, a blockchain-based dynamic spectrum-sharing scheme has been proposed and investigated in this work, which aims at enhancing the system by providing desirable features, such as decentralization, transparency, immutability, and auditability. By considering the privacy and transaction dynamics issues when blockchain is integrated into spectrum-sharing systems, a privacy-preserving double auction mechanism based on differential privacy is developed for incentivizing spectrum sharing, where the time-varying valuations of the spectrum resources are also taken into consideration. In the proposed auction, a winner determination problem (WDP) is formulated to decide the winning bidders and spectrum allocation. A deep reinforcement learning (DRL)-based method is then proposed for efficiently solving the WDP. The proposed auction mechanism can be integrated with smart contracts on blockchain platforms. Furthermore, the computation of the DRL-based method for solving the WDP is designed as part of the consensus mechanism in the blockchain. Theoretical analysis show that the proposed privacy-aware double auction mechanism satisfies the properties of differential privacy, individual rationality, and truthfulness. Finally, simulation results are provided to validate the performance of the spectrum-sharing approach.

Abstract Face recognition (FR) systems based on convolutional neural networks have shown excellent performance in human face inference. However, some malicious users may exploit such powerful systems to identify others' face images disclosed by victims' social network accounts, consequently obtaining private information. To address this emerging issue, synthesizing face protection images with visual and protective effects is essential. However, existing face protection methods encounter three critical problems: poor visual effect, limited protective effect, and trade‐off between visual and protective effects. To address these challenges, we propose a novel face protection approach in this article. Specifically, we design a generative adversarial network (GAN) framework with an autoencoder (AEGAN) as the generator to synthesize the protection images. It is worth noting that we introduce an interpolation upsampling module in the decoder in order to let the synthesized protection images evade recognition by powerful convolution‐based FR systems. Furthermore, we introduce an attention module with a perceptual loss in AEGAN to enhance the visual effects of synthesized images by AEGAN. Extensive experiments have shown that AEGAN not only can maintain the comfortable visual quality of synthesized images but also prevent the recognition of commercial FR systems, including Baidu and iKLYTEK.

The metaverse and its underlying blockchain technology have attracted extensive attention in the past few years. How to mine, process, and analyze the tremendous data generated by the metaverse systems has posed a number of challenges. Aiming to address them, we mainly focus on modeling and understanding the blockchain transaction network from a structural identity perspective, which represents the entire network structure and reveals the relations among multiple entities. In this article, we analyze three metaverse-related systems: non-fungible token (NFT), Ethereum (ETH), and Bitcoin (BTC) from the structural-identity perspective. First, we conduct the complex network analysis of the metaverse network and obtain several new insights (i.e., power-law degree distribution, disconnection, disassortativity, preferential attachment, and non-rich-club effect). Secondly, based on such findings, we propose a novel representation learning method named structure-to-vector with random pace (SVRP) for learning both the latent representation and structural identity of the network. Thirdly, we conduct node classification and link prediction tasks with the integration of graph neural networks (GNNs). Empirical results on three real-world datasets demonstrate that our proposed SVRP outperforms other existing methods in multiple tasks. In particular, our SVRP achieves the highest node classification accuracy (Acc) (99.3$\%$) and$F$1-score (96.7$\%$) while only requiring original non-attributed graphs.

Payment channel networks (PCNs) are considered as a prominent solution for scaling blockchain, where users can establish payment channels and complete transactions in an off-chain manner. However, it is non-trivial to schedule transactions in PCNs and most existing routing algorithms suffer from the following challenges: 1) one-shot optimization, 2) privacy-invasive channel probing, 3) vulnerability to DoS attacks. To address these challenges, we propose a privacy-aware transaction scheduling algorithm with defence against DoS attacks based on deep reinforcement learning (DRL), namely PTRD. Specifically, considering both the privacy preservation and long-term throughput into the optimization criteria, we formulate the transaction-scheduling problem as a Constrained Markov Decision Process. We then design PTRD, which extends off-the-shelf DRL algorithms to constrained optimization with an additional cost critic-network and an adaptive Lagrangian multiplier. Moreover, considering the distribution nature of PCNs, in which each user schedules transactions independently, we develop a distributed training framework to collect the knowledge learned by each agent so as to enhance learning effectiveness. With the customized network design and the distributed training framework, PTRD achieves a good balance between the optimization of the throughput and the minimization of privacy risks. Evaluations show that PTRD outperforms the state-of-the-art PCN routing algorithms by 2.7%–62.5% in terms of the long-term throughput while satisfying privacy constraints.

Intelligent Transport Systems (ITS) have received growing attention recently driven by technical advances in Industrial Internet of Vehicles (IIoV). In IIoV, vehicles report traffic data to management infrastructures to achieve better ITS services. To ensure security and privacy, many anonymous authentication-enabled data reporting protocols are proposed. However, these protocols usually require a large number of preloaded pseudonyms or involve a costly and irrevocable group signature. Thus, they are not ready for realistic deployment due to large storage overhead, expensive computation costs, or absence of malicious users' revocation. To address these issues, we present a novel data reporting protocol for edge-assisted ITS in this paper, where the traffic data is sent to distributed edge nodes for local processing. Specifically, we propose a new anonymous authentication scheme fine-tuned to fulfill the needs of vehicular data reporting, which allows authenticated vehicles to report unlimited unlinkable messages to edge nodes without huge pseudonyms download and storage costs. Moreover, we designed an efficient certificate update scheme based on a bivariate polynomial function. In this way, malicious vehicles can be revoked with time complexity$\mathcal {O}$(1). The security analysis demonstrates that our protocol satisfies source authentication, anonymity, unlinkability, traceability, revocability, nonframeability, and nonrepudiation. Further, extensive simulation results show that the performance of our protocol is greatly improved since the signature size is reduced by at least 8%, the computation costs in message signing and verification are reduced by at least 56% and 67%, respectively, and the packet loss rate is reduced by at least 14%.

Periodic Jump processes commonly occur in complex industrial systems. As the systems vary dynamically between different stages, learning their dynamics in an unified model, so as to forecast and simulation accurately is challenging. In this study, we propose autonomous jump ordinary differential equation net (AJ-ODENet) to learn the continuous-time periodic jump system. The model consists of several Hierarchical ODENets (H-ODENets) and a stage transition predictor. Each H-ODENet is an advanced version of ordinary differential equations network to individually learn specific dynamics in each stage from irregularly sampled sequence data. The stage transition predictor realizes autonomous stage transition during open-loop simulation. Furthermore, an encoder–decoder framework built on AJ-ODENet is employed on a real cooling system of data center to simulate some variables in runtime. With multivariate data given, such as server power and environmental temperature, the model can simulate the working patterns as in reality, and the relative error of the predicted energy consumption is within 5%. Furthermore, based on the model, we infer the optimal cooling temperature settings under different heat loads. The simulation results indicate that 6%–25% of cooling energy consumption can be optimized.

Intelligent transportation systems (ITS) with surveillance cameras capture traffic images or videos. However, images or videos in ITS often encounter blurs due to various reasons. Considering resource limitations, although recent technologies make progress in image-deblurring, there are still challenges in applying image-deblurring models in practical transportation systems: the model size and the running time. This work proposes an artful variant-depth network (VDN) to address the challenges. We design variant-depth sub-networks in a coarse-to-fine manner to improve the deblurring effect. We also adopt a new connection namely stack connection to connect all sub-networks to reduce the running time and model size while maintaining high deblurring quality. We evaluate the proposed VDN with the state-of-the-art (SOTA) methods on several typical datasets. Results on Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) show that the VDN outperforms SOTA image-deblurring methods. Furthermore, the VDN also has the shortest running time and the smallest model size.

Graph auto-encoder is considered a framework for unsupervised learning on graph-structured data by representing graphs in a low dimensional space. It has been proved very powerful for graph analytics. In the real world, complex relationships in various entities can be represented by heterogeneous graphs that contain more abundant semantic information than homogeneous graphs. In general, graph auto-encoders based on homogeneous graphs are not applicable to heterogeneous graphs. In addition, little work has been done to evaluate the effect of different semantics on node embedding in heterogeneous graphs for unsupervised graph representation learning. In this work, we propose a novel Heterogeneous Graph Attention Auto-Encoders (HGATE) for unsupervised representation learning on heterogeneous graph-structured data. Based on the consideration of semantic information, our architecture of HGATE reconstructs not only the edges of the heterogeneous graph but also node attributes, through stacked encoder/decoder layers. Hierarchical attention is used to learn the relevance between a node and its meta-path based neighbors, and the relevance among different meta-paths. HGATE is applicable to transductive learning as well as inductive learning. Node classification and link prediction experiments on real-world heterogeneous graph datasets demonstrate the effectiveness of HGATE for both transductive and inductive tasks.

Sharding has been considered as a prominent approach to enhance the limited performance of blockchain. However, most sharding systems leverage a non-cooperative design, which lowers the fault tolerance resilience due to the decreased mining power as the consensus execution is limited to each separated shard. To this end, we present Benzene, a novel sharding system that enhances the performance by cooperation-based sharding while defending the per-shard security. First, we establish a double-chain architecture for function decoupling. This architecture separates transaction-recording functions from consensus-execution functions, thereby enabling the cross-shard cooperation during consensus execution while preserving the concurrency nature of sharding. Second, we design a cross-shard block verification mechanism leveraging Trusted Execution Environment (TEE), via which miners can verify blocks from other shards during the cooperation process with the minimized overheads. Finally, we design a voting-based consensus protocol for cross-shard cooperation. Transactions in each shard are confirmed by all shards that simultaneously cast votes, consequently achieving an enhanced fault tolerance and lowering the confirmation latency. We implement Benzene and conduct both prototype experiments and large-scale simulations to evaluate the performance of Benzene. Results show that Benzene achieves superior performance than existing sharding/non-sharding blockchain protocols. In particular, Benzene achieves a linearly-improved throughput with the increased number of shards (e.g., 32,370 transactions per second with 50 shards) and maintains a lower confirmation latency than Bitcoin (with more than 50 shards). Meanwhile, Benzene maintains a fixed fault tolerance at 1/3 even with the increased number of shards.

Terrestrial-satellite networks (TSNs) can provide worldwide users with ubiquitous and seamless network services. Meanwhile, malicious eavesdropping is posing tremendous challenges on secure transmissions of TSNs due to their widescale wireless coverage. In this paper, we propose an aerial bridge scheme to establish secure tunnels for legitimate transmissions in TSNs. With the assistance of unmanned aerial vehicles (UAVs), massive transmission links in TSNs can be secured without impacts on legitimate communications. Owing to the stereo position of UAVs and the directivity of directional antennas, the constructed secure tunnel can significantly relieve confidential information leakage, resulting in the precaution of wiretapping. Moreover, we establish a theoretical model to evaluate the effectiveness of the aerial bridge scheme compared with the ground relay, non-protection, and UAV jammer schemes. Furthermore, we conduct extensive simulations to verify the accuracy of theoretical analysis and present useful insights into the practical deployment by revealing the relationship between the performance and other parameters, such as the antenna beamwidth, flight height and density of UAVs.

Multi-view clustering (MVC) aims at exploiting the consistent features within different views to divide samples into different clusters. Existing subspace-based MVC algorithms usually assume linear subspace structures and two-stage similarity matrix construction strategies, thereby posing challenges in imprecise low-dimensional subspace representation and inadequacy of exploring consistency. This paper presents a novel hierarchical representation for MVC method via the integration of intra-sample, intra-view, and inter-view representation learning models. In particular, we first adopt the deep autoencoder to adaptively map the original high-dimensional data into the latent low-dimensional representation of each sample. Second, we use the self-expression of the latent representation to explore the global similarity between samples of each view and obtain the subspace representation coefficients. Third, we construct the third-order tensor by arranging multiple subspace representation matrices and impose the tensor low-rank constraint to sufficiently explore the consistency among views. Being incorporated into a unified framework, these three models boost each other to achieve a satisfactory clustering result. Moreover, an alternating direction method of multipliers algorithm is developed to solve the challenging optimization problem. Extensive experiments on both simulated and real-world multi-view datasets show the superiority of the proposed method over eight state-of-the-art baselines.

Blockchain technology has gained popularity owing to the success of cryptocurrencies such as Bitcoin and Ethereum. Nonetheless, the scalability challenge largely limits its applications in many real-world scenarios. Off-chain payment channel networks (PCNs) have recently emerged as a promising solution by conducting payments through off-chain channels. However, the throughput of current PCNs does not yet meet the growing demands of large-scale systems because: 1) most PCN systems only focus on maximizing the instantaneous throughput while failing to consider network dynamics in a long-term perspective; 2) transactions are re-actively routed in PCNs, in which intermediate nodes only passively forward every incoming transaction. These limitations of existing PCNs inevitably lead to channel imbalance and the failure of routing subsequent transactions. To address these challenges, we propose a novel proactive look-ahead algorithm (PLAC) that controls transaction flows from a long-term perspective and proactively prevents channel imbalance. In particular, we first conduct a measurement study on two real-world PCNs to explore their characteristics in terms of transaction distribution and topology. On that basis, we propose PLAC based on deep reinforcement learning (DRL), which directly learns the system dynamics from historical interactions of PCNs and aims at maximizing the long-term throughput. Furthermore, we develop a novel graph convolutional network-based model for PLAC, which extracts the inter-dependency between PCN nodes to consequently boost the performance. Extensive evaluations on real-world datasets show that PLAC improves state-of-the-art PCN routing schemes w.r.t the long-term throughput from 6.6% to 34.9%.

Spectrum sensing with transmit power identification can greatly facilitate the application of the hybrid spectrum access strategy in cognitive radio (CR) networks. Conventional model-driven methods suffer from severe performance degradation in low signal-to-noise ratio (SNR) regime. In this paper, we propose a multiple transmit power levels identification network (TPIN) which consists of three components. In the data preprocessing components, the covariance matrix (COV) of the received data is first employed as the observation data. Then, the residual network (ResNet) based feature extractor components is used to construct the test statistic by extracting high-dimensional features of the observation data. Furthermore, the likelihood ratio test (LRT) criterion is leveraged to design the cost function for obtaining the maximum posterior probability in the classifier components. Different from the assumption in conventional method, the prior probability of each transmit power levels is unknown to the TPIN, and the array of training set is randomly disturbed. In addition, in order to verify the ability of TPIN in data features extraction, a comparison reference experiment using a general test statistic (e.g., higher-order cumulative) as the observation data is introduced. Finally, simulation results demonstrate the identification performance of the COV-based (COV-TPIN) scheme.

As a rapidly growing subset of the Internet of Thing (IoT), the cloud-based Internet of Medical Thing (IoMT) has been widely applied in remote healthcare industries, which allows the physicians to monitor patients' body parameters remotely to offer continuous and timely healthcare. These healthcare parameters usually contain sensitive information, such as heart rates, glucose levels and etc., and the exposure of them may pose serious threats to the patients' health and lives. To guarantee security and privacy, many IoMT data sharing schemes have been proposed. However, most of these schemes either exhibit a one-to-one data sharing structure or fail to protect the patients' privacy. Since the data usually needs to be shared to different physicians, patients may want to be assisted without revealing their identities. To meet these requirements in healthcare systems, we propose a multi-receiver secure healthcare data sharing scheme, in which the patients are allowed to share their IoMT data to multiple physicians simultaneously for a multidisciplinary treatment, and the conditional anonymity is achieved where data source authentication is provided without revealing the patient's identity. When the patient health condition is abnormal, the hospital can correctly and quickly trace the patient's identity and inform him/her immediately. Our scheme is formally proved to achieve multiple security properties including confidentiality, unforgeability and anonymity. Simulation results demonstrate that the proposed scheme is efficient and practical.

The ground-to-satellite communication network (G2SN) has highlighted the significance of constructing ubiquitous and seamless networks for the next-generation communication system. However, in the presence of secret eavesdroppers, securing massive transmission links is posing tremendous challenges for G2SNs. In this paper, we propose an aerial assistant scheme to safeguard legitimate transmissions in G2SNs, where multiple unmanned aerial vehicles (UAVs) are deployed between the ground users and the satellite. With the assistance of flexible UAVs and the directivity of directional antennas, the constructed link can significantly reduce the risk of wiretapping, resulting in the improvement of security. Furthermore, to evaluate the performance of G2SNs, we introduce the eavesdropping probability and link connectivity as metrics. With the comparison of the non-protection scheme, we validate the effectiveness of our aerial assistant scheme. Finally, we present useful insights into practical deployment by revealing the relationship between the performance and other parameters, such as antenna beamwidth, deployment height and density of UAVs.

In this paper, we study the information freshness on covert communication in the Internet of Things (IoT) networks. The freshness of information is characterized by a recently introduced metric, termed as age of information (AoI). Specifically, without a feedback channel, each packet generated at the transmitter is only allowed to be transmitted during one time slot no matter whether it is successfully decoded at the receiver. In this case, the average AoI at the receiver and the average probability of error detection at the warden are derived. Then, the transmit power is optimized to minimize the AoI while guaranteeing the covertness requirement. On the other hand, with a perfect feedback channel, packet re-transmission is adopted to make the information fresh enough. The average AoI and the average probability of error detection are analyzed. Then, the transmit power is also optimized in this case. Simulation results reveal that the proposed scheme can minimize average AoI under the requirement of covertness, and the case using re-transmission with feedback achieves a lower AoI under the same requirement of covertness.

As computer programs run on top of blockchain, smart contracts have proliferated a myriad of decentralized applications while bringing security vulnerabilities, which may cause huge financial losses. Thus, it is crucial and urgent to detect the vulnerabilities of smart contracts. However, existing fuzzers for smart contracts are still inefficient to detect sophisticated vulnerabilities that require specific vulnerable transaction sequences to trigger. To address this challenge, we propose a novel vulnerability-guided fuzzer based on reinforcement learning, namely RLF, for generating vulnerable transaction sequences to detect such sophisticated vulnerabilities in smart contracts. In particular, we firstly model the process of fuzzing smart contracts as a Markov decision process to construct our reinforcement learning framework. We then creatively design an appropriate reward with consideration of both vulnerability and code coverage so that it can effectively guide our fuzzer to generate specific transaction sequences to reveal vulnerabilities, especially for the vulnerabilities related to multiple functions. We conduct extensive experiments to evaluate RLF’s performance. The experimental results demonstrate that our RLF outperforms state-of-the-art vulnerability-detection tools (e.g., detecting 8%-69% more vulnerabilities within 30 minutes).

Both the metaverse and its underlying blockchain technology have attracted extensive attention in the past few years. It becomes a natural problem to extract, process, and analyze the tremendous data generated by the blockchain systems for various metaverse applications though it also poses diverse challenges. Amongst those challenges, this paper mainly focuses on modeling and understanding the blockchain transaction network from a structural identity perspective, which represents the entire network structure and reveals the relations among multiple entities. In this paper, we propose a novel representation learning method named Structure-to-Vector with Random Pace (SVRP) for learning both latent representation and structural identity of blockchain transaction networks. We then conduct node classification and link prediction tasks with integration with Graph Neural Networks (GNNs). Empirical results on three representative blockchain data sets, namely Non-fungible token (NFT), Ethereum (ETH), and Bitcoin (BTC), demonstrate that our proposed SVRP outperforms other existing methods in multiple tasks. In particular, our SVRP achieves the highest node classification accuracy (99.3%) while only requiring original non-attributed graphs (i.e., graphs without node features).

Stock movement prediction is a critical issue in the field of financial investment. It is very challenging since a stock usually shows highly stochastic property in price and has complex relationships with other stocks. Most existing approaches cannot jointly take the above two issues into account and thus cannot yield satisfactory prediction result. This paper contributes a new stock movement prediction model, Selective Transfer Learning with Adversarial Training (STLAT). Our STLAT method advances existing solutions in two major aspects: (i) tailoring the pre-trained and fine-tuned method for stock movement prediction and (ii) introducing the data selector module to select the more relevant training samples. More specifically, we pre-train the shared base model using three different tasks. The predictor task is constructed to measure the performance of the shared base model with source domain data and target domain data. The adversarial training task is constructed to improve the generalisation of the shared base model. The data selector task is introduced to select the most relevant and high-quality training samples from stocks in source domain. All three tasks are jointly trained with a loss function. As a result, the pre-trained shared base model can be fine-tuned with the stock data in target domain. To validate our method, we perform the back-testing on the historical data of two public datasets and a newly constructed dataset. Extensive experiments demonstrate the superiority of our STLAT method. It outperforms state-of-the-art stock prediction solutions on ACC evaluation of 3.76%, 4.12%, 4.89% on ACL18, KDD17 and CN50, respectively.

To ensure the security of Internet of Things (IoT) communications, one can use deep packet inspection (DPI) on network middleboxes to detect and mitigate anomalies and suspicious activities in network traffic of IoT, although doing so over encrypted traffic is challenging. Therefore, in this article, an efficient and privacy-preserving encrypted traffic detection scheme is proposed. The scheme uses only lightweight cryptographic operations (i.e., symmetric encryption, hash functions, and pseudorandom functions) to achieve both privacy and security within an inspection round. A dispute resolution mechanism is also designed to address potential disputes between client(s) and server(s). We also present the corresponding security proof and experimental evaluation, which demonstrate that our proposed scheme achieves strong security and privacy preservation and good performance.

This article presents an analytical model to analyze the communication performance of cluster backscatter communication networks (CBackCom Nets) by considering their unique interferences. In CBackCom Nets, interferences are from both backscatter transmitters (BTs) and carrier emitters (CEs), i.e., RF signal emitters. Because BTs are distributed in clusters around CEs, interferences from BTs and interferences from CEs constitute coupled interferences. In addition, since BTs conduct backscatter communications by reflecting RF signals from CEs, interfering signals from BTs, and interfering signals from CEs are power-correlated, leading to the particularity and complexity of coupled interferences of CBackCom Nets. In contrast to previous studies that analyze the performance of CBackCom Nets ignoring coupled interferences, this article develops a novel interference analysis approach to analyze their coupled interferences, and then analyze performance, including coverage probability and spatial throughput of a cluster. Our numerical results show that our analytical model can obtain more accurate results than prior analytical models. In addition, our results reveal the relationship between the communication performance and multiple factors, such as the node density, the energy harvesting model, the interferences from CEs, and the cluster size, offering insightful implications for constructing and configuring CBackCom Nets.

The Industrial Internet of Things (IIoT) plays an important role in digital transformation of traditional industries toward Industry 4.0. By connecting sensors, instruments, and other industry devices to the Internet, IIoT facilitates the data collection, data analysis, and automated control, thereby improving the productivity and efficiency of the business as well as the resulting economic benefits. Due to the complex IIoT infrastructure, anomaly detection becomes an important tool to ensure the success of IIoT. Due to the nature of IIoT, graph-level anomaly detection has been a promising means to detect and predict anomalies in many different domains, such as transportation, energy, and factory, as well as for dynamically evolving networks. This article provides a useful investigation on graph neural networks (GNNs) for anomaly detection in IIoT-enabled smart transportation, smart energy, and smart factory. In addition to the GNN-empowered anomaly detection solutions on point, contextual, and collective types of anomalies, useful data sets, challenges, and open issues for each type of anomalies in the three identified industry sectors (i.e., smart transportation, smart energy, and smart factory) are also provided and discussed, which will be useful for future research in this area. To demonstrate the use of GNN in concrete scenarios, we show three case studies in smart transportation, smart energy, and smart factory, respectively.

Massive physical devices are deployed in the Industrial Internet of Things (IoT) to collect ambiance data while heavy storage and communication cost are imposed on these IoT devices. To overcome this constraint, cloud-assisted technologies are introduced to store and manage the collected data. In order to protect data quality and security, encryption is required before uploading data to remote clouds. Consequently, a search function is added to cloud services to find the specific data. However, traditional data searching schemes are constructed in user-oriented systems, where the search function is mainly involved with the relationship between data and users rather than data and devices. As a result, traditional search schemes are not suitable to find special IoT devices. On the other hand, the status of these devices is described by many attributes, e.g., temperature, clean water storage, and machine speed in an early warning system for industrial sewage disposal equipment. Hence, multi-keyword conjunctive queries for partial attributes should be introduced so as to find the target device more accurately and more efficiently. To address these challenges, we propose a new universal device-oriented keyword searchable encryption (Do-KSE) scheme for cloud-assisted IoT in this paper. Furthermore, the functions of a single and conjunctive keyword search are maintained to handle the device search requirement of partial attributes. We conduct extensive experiments to evaluate the proposed scheme. Experimental results show that our scheme has excellent performance because of the lightweight index and query trapdoor.

The emergence of infectious disease COVID-19 has challenged and changed the world in an unprecedented manner. The integration of wireless networks with edge computing (namely wireless edge networks) brings opportunities to address this crisis. In this paper, we aim to investigate the prediction of the infectious probability and propose precautionary measures against COVID-19 with the assistance of wireless edge networks. Due to the availability of the recorded detention time and the density of individuals within a wireless edge network, we propose a stochastic geometry-based method to analyze the infectious probability of individuals. The proposed method can well keep the privacy of individuals in the system since it does not require to know the location or trajectory of each individual. Moreover, we also consider three types of mobility models and the static model of individuals. Numerical results show that analytical results well match with simulation results, thereby validating the accuracy of the proposed model. Moreover, numerical results also offer many insightful implications. Thereafter, we also offer a number of countermeasures against the spread of COVID-19 based on wireless edge networks. This study lays the foundation toward predicting the infectious risk in realistic environment and points out directions in mitigating the spread of infectious diseases with the aid of wireless edge networks.

Abstract Motion deblurring is a challenging task in vision and graphics. Recent researches aim to deblur by using multiple sub‐networks with multi‐scale or multi‐patch inputs. However, scaling or splitting operations on input images inevitably loses the spatial details of the images. Meanwhile, their models are usually complex and computationally expensive. To address these problems, we propose a novel variant‐depth scheme. In particular, we utilize the multiple variant‐depth sub‐networks with scale‐invariant inputs to combine into a variant‐depth network (VDN). In our design, different levels of sub‐networks accomplish progressive deblurring effects without transforming the inputs, thereby effectively reducing the computational complexity of the model. Extensive experiments have shown that our VDN outperforms the state‐of‐the‐art motion deblurring methods while maintaining a lower computational cost. The source code is publicly available at: https://github.com/CaiGuoHS/VDN .

Most of existing Multi-access edge computing (MEC) studies consider the remote cloud server as a special edge server, the opportunity of edge-cloud collaboration has not been well exploited. We propose a dependency-aware offloading scheme in MEC with edge-cloud cooperation under task dependency constraints. Each mobile device has a limited budget and has to determine which sub-task should be computed locally or should be sent to the edge or remote cloud. To address this issue, we divide the offloading problem into two application finishing time minimization sub-problems with two different cooperation modes, both of which are proved to be NP-hard. We then devise one greedy algorithm with approximation ratio of$1+\epsilon$for the first mode with edge-cloud cooperation but no edge-edge cooperation. Then we design an efficient greedy algorithm for the second mode, considering both edge-cloud and edge-edge co-operations. Extensive simulation results show that for the first mode, the proposed greedy algorithm achieves near optimal performance for typical task topologies. On average, it outperforms the modified Hermes benchmark algorithm by about$23\%\sim 43.6\%$in terms of application finishing time with given budgets. By further exploiting collaborations among edge servers in the second cooperation mode, the proposed algorithm helps to achieve over 20.3 percent average performance gain on the application finishing time over the first mode under various scenarios. Real-world experiments comply with simulation results.

Hong Kong Baptist University, Hong Kong

The site selection of sports facilities is a pivotal link in the construction of city livable environment and the development of sports business in digital-twinning cities. Recent years have witnessed data mining and visualization technologies bringing the convenience as well as opportunities for intelligent site selection. However, the lack of effective and reliable systematic analysis leads to difficulties in developing sports facilities planning schemes and constructing the site-selection system. In this article, we design Sport facility Visual analysis system (SpoVis), an interactive visual analysis system for planning sports facilities as well as site selection. SpoVis provides users with the distribution status and statistical analysis of various sports facilities. Based on a comprehensive consideration of city population distribution, construction cost, existing sports facilities, traffic situation, and development potential, SpoVis provides users with a reasonable site-selection scheme of sports facilities from both macro and microperspectives and recommends results through topology and map. Meanwhile, based on the distribution of existing sports facilities and city influencing factors, a set of visual analysis components are designed to facilitate users to evaluate the status and information of existing sports facilities. We have carried out extensive experiments on a real platform with real-world data. The experimental results show that the proposed site-selection models and algorithms have excellent accuracy and operation efficiency.

Context prediction plays a crucial role in implementing autonomous driving applications. As one of important context-prediction tasks, crowd-and-vehicle counting is critical for achieving real-time traffic and crowd analysis, consequently facilitating decision-making processes for autonomous vehicles. However, the completion of crowd-and-vehicle counting also faces challenges, such as large-scale variations, imbalanced data distribution, and insufficient local patterns. To tackle these challenges, we put forth a novel frequency feature pyramid network (FFPNet) in this paper. Our proposed FFPNet extracts the multi-scale information by frequency feature pyramid module, which can tackle the issue of large-scale variations. Meanwhile, the frequency feature pyramid module uses different frequency branches to obtain different scale information. We also adopt the attention mechanism to strength the extraction of different scale information. Moreover, we devise a novel loss function, namely global-local consistency loss, to address the existing problems of imbalanced data distribution and insufficient local patterns. Furthermore, we conduct extensive experiments on six datasets to evaluate our proposed FFPNet. It is worth mentioning that we also construct a novel crowd-and-vehicle dataset (CROVEH), which is the only dataset that contains both crowd-and-vehicle annotations. The experimental results show that FFPNet achieves the best performance on different backbones, e.g., 52.69 mean absolute error (MAE) on P2PNet with FFP module. The codes are available at:https://github.com/MUST-AI-Lab/FFPNet.

This article studies the PBFT-based sharded permissioned blockchain, which executes in either a local datacenter or a rented cloud platform. In such permissioned blockchain, the transaction (TX) assignment strategy could be malicious such that the network shards may possibly receive imbalanced transactions or even bursty-TX injection attacks. An imbalanced transaction assignment brings serious threats to the stability of the sharded blockchain. A stable sharded blockchain can ensure that each shard processes the arrived transactions timely. Since the system stability is closely related to the blockchain throughput, how to maintain a stable sharded blockchain becomes a challenge. To depict the transaction processing in each network shard, we adopt the Lyapunov Optimization framework. Exploitingdrift-plus-penalty(DPP) technique, we then propose an adaptive resource-allocation algorithm, which can yield the near-optimal solution for each network shard while the shard queues can also be stably maintained. We also rigorously analyze the theoretical boundaries of both the system objective and the queue length of shards. The numerical results show that the proposed algorithm can achieve a better balance between resource consumption and queue stability than other baselines. We particularly evaluate two representative cases of bursty-TX injection attacks, i.e., the continued attacks against all network shards and the drastic attacks against a single network shard. The evaluation results show that the DPP-based algorithm can well alleviate the imbalanced TX assignment, and simultaneously maintain high throughput while consuming fewer resources than other baselines.

Non-terrestrial Networks (NTNs) refer to the networks, where either satellites or unmanned aerial vehicles (UAVs) are deployed to extend the current terrestrial networks for serving the growing mobile broadband and machine-type communications. With the advantages of UAVs' flexibility and satellites' global coverage, the solution of UAV-To-satellite communications (U2SC) can provide promising global communication services for the emerging NTNs. Previous literature has explored many potential directions of U2SC, including channel tracking, deployment design, and link analysis. However, as a vital role in system performance, the connectivity of U2SC has not been well investigated yet. This research gap motivates us to present an analytical model to evaluate the connectivity of U2SC. In particular, we first present the system model of the U2SC by considering the distribution model of UAVs, antenna models, and the path loss model. We then utilize stochastic geometry to derive a theoretical formulation of the successful connection probability of U2SC. The comprehensive numerical results are given to evaluate the received power, the interference, and the successful connection probability of U2SC and analyze the impacts of system parameters, such as the number of frequency carriers, the type of frequency bands, the number of UAVs, and the satellite altitude.

The emerging unmanned aerial vehicles (UAVs) pose a potential security threat for terrestrial communications when UAVs can be maliciously employed as UAV-eavesdroppers to wiretap confidential communications. To address such an aerial security threat, we present a friendly jamming scheme named terrestrial mobile jamming (TMJ) to protect terrestrial confidential communications from UAV eavesdropping. In our TMJ scheme, a jammer moving along the protection area can emit jamming signals toward the UAV-eavesdropper so as to reduce the eavesdropping risk. We evaluate the performance of our scheme by analyzing a secrecy-capacity maximization problem subject to the legitimate connectivity and eavesdropping probability. In addition, we investigate the optimized position for the jammer as well as its jamming power. Simulation results verify the effectiveness of the proposed scheme.

In this paper, we employ stochastic geometry to analyze ground-to-unmanned aerial vehicle (UAV) communications. We consider multiple UAVs to provide user-equipments (UEs) with uplink transmissions, where the distribution of UEs follows the Poisson Cluster process (PCP) and each UAV is dedicated to a specific cluster. In particular, we characterize the Laplace transform of the interference caused by multiple UEs in terms of the distribution of UEs as well as the transmission probability of each UE. We then derive analytical expressions of the successful transmission probability. We next conduct a comprehensive numerical analysis with consideration of different system parameters. The results show that four factors (i.e., the geographical surroundings, the transmission powers, the Signal-to-Interference-plus-Noise Ratio (SINR) thresholds, and the UAV height) have main influences on ground-to-UAV communications.

In this paper, we conduct a complex-network analysis of the Bitcoin network. In particular, we design a new sampling method namely random walk with flying-back (RWFB) to conduct effective data sampling. We then conduct a comprehensive analysis of the Bitcoin network in terms of the degree distribution, clustering coefficient, the shortest path length, the assortativity, and the rich-club coefficient. There are several important observations from the Bitcoin network, such as small- world phenomenon and non-rich-club effect. This work brings up an in-depth understanding of the current Bitcoin blockchain network and offers implications for future directions in malicious activity and fraud detection in cryptocurrency blockchain networks.

With the continuous accumulation of users' check-in data, we can gradually capture users' behavior patterns and mine users' preferences. Based on this, the next point-of-interest (POI) recommendation has attracted considerable attention. Its main purpose is to simulate users' behavior habits of check-in behavior. Then, different types of context information are used to construct a personalized recommendation model. However, the users' check-in data are extremely sparse, which leads to low performance in personalized model training using recurrent neural network. Therefore, we propose a category-aware gated recurrent unit (GRU) model to mitigate the negative impact of sparse check-in data, capture long-range dependence between user check-ins and get better recommendation results of POI category. We combine the spatiotemporal information of check-in data and take the POI category as users' preference to train the model. Also, we develop an attention-based category-aware GRU (ATCA-GRU) model for the next POI category recommendation. The ATCA-GRU model can selectively utilize the attention mechanism to pay attention to the relevant historical check-in trajectories in the check-in sequence. We evaluate ATCA-GRU using a real-world data set, named Foursquare. The experimental results indicate that our ATCA-GRU model outperforms the existing similar methods for next POI recommendation.

Critical infrastructure systems are vital to underpin the functioning of a society and economy. Due to the ever-increasing number of Internet-connected Internet-of-Things (IoT)/Industrial IoT (IIoT), and the high volume of data generated and collected, security and scalability are becoming burning concerns for critical infrastructures in industry 4.0. The blockchain technology is essentially a distributed and secure ledger that records all the transactions into a hierarchically expanding chain of blocks. Edge computing brings the cloud capabilities closer to the computation tasks. The convergence of blockchain and edge computing paradigms can overcome the existing security and scalability issues. In this article, we first introduce the IoT/IIoT critical infrastructure in industry 4.0, and then we briefly present the blockchain and edge computing paradigms. After that, we show how the convergence of these two paradigms can enable secure and scalable critical infrastructures. Then, we provide a survey on the state of the art for security and privacy and scalability of IoT/IIoT critical infrastructures. A list of potential research challenges and open issues in this area is also provided, which can be used as useful resources to guide future research.

Deep neural networks (DNNs) have shown great success in completing complex tasks. However, DNNs inevitably bring high computational cost and storage consumption due to the complexity of hierarchical structures, thereby hindering their wide deployment in Internet-of-Things (IoT) devices, which have limited computational capability and storage capacity. Therefore, it is a necessity to investigate the technologies to compact DNNs. Despite tremendous advances in compacting DNNs, few surveys summarize compacting-DNNs technologies, especially for IoT applications. Hence, this article presents a comprehensive study on compacting-DNNs technologies. We categorize compacting-DNNs technologies into three major types: 1) network model compression; 2) knowledge distillation (KD); and 3) modification of network structures. We also elaborate on the diversity of these approaches and make side-by-side comparisons. Moreover, we discuss the applications of compacted DNNs in various IoT applications and outline future directions.

The traditional production paradigm of large batch production does not offer flexibility toward satisfying the requirements of individual customers. A new generation of smart factories is expected to support new multivariety and small-batch customized production modes. For this, artificial intelligence (AI) is enabling higher value-added manufacturing by accelerating the integration of manufacturing and information communication technologies, including computing, communication, and control. The characteristics of a customized smart factory are: self-perception, operations optimization, dynamic reconfiguration, and intelligent decision-making. The AI technologies will allow manufacturing systems to perceive the environment, adapt to the external needs, and extract the process knowledge, including business models, such as intelligent production, networked collaboration, and extended service models. This article focuses on the implementation of AI in customized manufacturing (CM). The architecture of an AI-driven customized smart factory is presented. Details of intelligent manufacturing devices, intelligent information interaction, and construction of a flexible manufacturing line are showcased. The state-of-the-art AI technologies of potential use in CM, that is, machine learning, multiagent systems, Internet of Things, big data, and cloud-edge computing, are surveyed. The AI-enabled technologies in a customized smart factory are validated with a case study of customized packaging. The experimental results have demonstrated that the AI-assisted CM offers the possibility of higher production flexibility and efficiency. Challenges and solutions related to AI in CM are also discussed.

In the past decades, due to the low design cost and easy maintenance, text-based CAPTCHAs have been extensively used in constructing security mechanisms for user authentications. With the recent advances in machine/deep learning in recognizing CAPTCHA images, growing attack methods are presented to break text-based CAPTCHAs. These machine learning/deep learning-based attacks often rely on training models on massive volumes of training data. The poorly constructed CAPTCHA data also leads to low accuracy of attacks. To investigate this issue, we propose a simple, generic, and effective preprocessing approach to filter and enhance the original CAPTCHA data set so as to improve the accuracy of the previous attack methods. In particular, the proposed preprocessing approach consists of a data selector and a data augmentor. The data selector can automatically filter out a training data set with training significance. Meanwhile, the data augmentor uses four different image noises to generate different CAPTCHA images. The well-constructed CAPTCHA data set can better train deep learning models to further improve the accuracy rate. Extensive experiments demonstrate that the accuracy rates of five commonly used attack methods after combining our preprocessing approach are 2.62% to 8.31% higher than those without preprocessing approach. Moreover, we also discuss potential research directions for future work.

Nowadays, IoT systems can better satisfy the service requirements of users with effectively utilizing edge computing resources. Designing an appropriate pricing scheme is critical for users to obtain the optimal computing resources at a reasonable price and for service providers to maximize profits. This problem is complicated with incomplete information. The state-of-the-art solutions focus on the pricing game between a single service provider and users, which ignoring the competition among multiple edge service providers. To address this challenge, we design an edge-intelligent hierarchical dynamic pricing mechanism based on cloud-edge-client collaboration. We introduce an improved double-layer Stackelberg game model to describe the cloud-edge-client collaboration. Technically, we propose a novel pricing prediction algorithm based on double-label Radius K-nearest Neighbors, thereby reducing the number of invalid games to accelerate the game convergence. The experimental results show that our proposed mechanism effectively improves the quality of service for users and realizes the maximum benefit equilibrium for service providers, compared with the traditional pricing scheme. Our proposed mechanism is highly suitable for the IoT applications (e.g., intelligent agriculture or Internet of Vehicles), where there are multiple competing edge service providers for resource allocation.

360-degree video streaming shows great potential to revolutionize the streaming market, by providing much better immersive experience than standard video streams. However, its wide adoption is hindered by the surging demand of network bandwidth due to multi-screen video transmission. To reduce the bandwidth cost, one promising approach is to predict a user’s field of view (FoV), and then prefetch video tiles that a user will view a few seconds ahead. The challenge lies in that user behaviors cannot be properly captured with very limited information, especially the viewing time spent on each tile and the FoV switching behavior are hard to predict. In this paper, we propose a novel 360-degree video streaming algorithm calledTVG-Streamingto optimize user experiences by learning user view behaviors. Different from previous approaches, our idea is to exploit tile-view graphs (TVGs) generated by real user behaviors and accurately estimate the probability that each tile falls in the FoV. With the tile view probability, we can determine the bitrate of each tile for delivery and buffering with limited bandwidth budget so as to maximize users’ quality of experience (QoE). For evaluation, we conduct extensive experiments using real traces and the results show that our proposedTVG-Streamingalgorithm significantly outperforms other algorithms by at least 20% improvement in terms of users’ QoE.

Industrial Internet of Things (IoT) has suffered from insufficient identity authentication and dynamic network topology, thereby resulting in vulnerabilities to data confidentiality. Recently, the attribute-based encryption (ABE) schemes have been regarded as a solution to ensure data transmission security and the fine-grained sharing of encrypted IoT data. However, most of existing ABE schemes that bring tremendous computational cost are not suitable for resource-constrained IoT devices. Therefore, lightweight and efficient data sharing and searching schemes suitable for IoT applications are of great importance. To this end, In this article, we propose a light searchable ABE scheme (namely LSABE). Our scheme can significantly reduce the computing cost of IoT devices with the provision of multiple-keyword searching for data users. Meanwhile, we extend the LSABE scheme to multiauthority scenarios so as to effectively generate and manage the public/secret keys in the distributed IoT environment. Finally, the experimental results demonstrate that our schemes can significantly maintain computational efficiency and save the computational cost at IoT devices, compared to other existing schemes.

The papers in this special section explores the state-of-the-art advances in adopting blockchain technologies for the Industrial Internet of Things (IIoT). There is a growing trend of adopting blockchain technologies to the IIoT due to the traceability, nonrepudiation, and immutability of blockchain systems. The proliferation of IIoT to industrial systems is fostering the fourth industrial revolution (aka Industry 4.0) while IIoT also confronts several challenges exhibiting in the following two perspectives: 1) security and privacy protection of IIoT data; 2) interoperability absence across IIoT systems. Blockchain and blockchain-enabled smart contracts can essentially offer solutions to address the emerging challenges in IIoT.

Recently, traffic flow prediction has drawn significant attention because it is a prerequisite in intelligent transportation management in urban informatics. The massively available traffic data collected from various sensors in transportation cyber-physical systems brings the opportunities in accurately forecasting traffic trend. Recent advances in deep learning shows the effectiveness on traffic flow prediction though most of them only demonstrate the superior performance on traffic data from a single type of vehicular carriers (e.g., cars) and does not perform well in other types of vehicles. To fill this gap, in this article, we propose a wide-attention and deep-composite (WADC) model, consisting of a wide-attention module and a deep-composite module, in this article. In particular, the wide-attention module can extract global key features from traffic flows via a linear model with self-attention mechanism. The deep-composite module can generalize local key features via convolutional neural network component and long short-term memory network component. We also perform extensive experiments on different types of traffic flow datasets to investigate the performance of WADC model. Our experimental results exhibit that WADC model outperforms other existing approaches.

Due to the advanced capabilities of the Internet of Vehicles (IoV) components such as vehicles, Roadside Units (RSUs) and smart devices as well as the increasing amount of data generated, Federated Learning (FL) becomes a promising tool given that it enables privacy-preserving machine learning that can be implemented in the IoV. However, the performance of the FL suffers from the failure of communication links and missing nodes, especially when continuous exchanges of model parameters are required. Therefore, we propose the use of Unmanned Aerial Vehicles (UAVs) as wireless relays to facilitate the communications between the IoV components and the FL server and thus improving the accuracy of the FL. However, a single UAV may not have sufficient resources to provide services for all iterations of the FL process. In this paper, we present a joint auction-coalition formation framework to solve the allocation of UAV coalitions to groups of IoV components. Specifically, the coalition formation game is formulated to maximize the sum of individual profits of the UAVs. The joint auction-coalition formation algorithm is proposed to achieve a stable partition of UAV coalitions in which an auction scheme is applied to solve the allocation of UAV coalitions. The auction scheme is designed to take into account the preferences of IoV components over heterogeneous UAVs. The simulation results show that the grand coalition, where all UAVs join a single coalition, is not always stable due to the profit-maximizing behavior of the UAVs. In addition, we show that as the cooperation cost of the UAVs increases, the UAVs prefer to support the IoV components independently and not to form any coalition.

Distributed peer-to-peer power energy markets are emerging quickly. Due to central governance and lack of effective information aggregation mechanisms, energy trading cannot be efficiently scheduled and tracked. We devise a new distributed energy transaction system over the energy Industrial Internet of Things based on predictive analytics, blockchain, and smart contract technologies. We propose a solution for scheduling distributed energy sources based on the Minimum Cut Maximum Flow theory. Blockchain is used to record transactions and reach consensus. Payment clearing for the actual power consumption is executed via smart contracts. Experimental results on real data show that our solution is practical and achieves a lower total cost for power energy consumption.

With the rapid development of cloud computing technology, massive services and online information cause information overload. Collaborative Filtering (CF) is one of the most successful and widely used technologies in personalized recommendation system to deal with information overload. However, traditional CF recommendation algorithms go through high time cost and poor real-time performance when dealing with the large-scale behavior data. Moreover, most collaborative recommendation methods mainly focus on improving recommendation accuracy, while ignore privacy preservation. In addition, the recommendation results of traditional CF recommendation algorithms are often too single, which could not meet user's diverse requirements. To solve these problems, this paper proposes a privacy-aware collaborative recommendation algorithm based on local sensitive hash (LSH) and factorization techniques. First, LSH is adopted to determine nearest neighbor set of the target users, where a neighbor matrix for the target user can be generated. The matrix factorization technique is applied in the neighbor matrix to predict the missing ratings. Then the nearest neighbors can be determined based on the predicted ratings. Finally, predictions for the target user are made based on the neighborhood-based CF recommendation model and diversified recommendations are made for the target user. Experimental results show that the proposed algorithm can effectively improve the efficiency of recommendation on the premise of protecting the privacy of users.

Due to the advanced capabilities of the Internet of Vehicles (IoV) components such as vehicles, Roadside Units (RSUs) and smart devices as well as the increasing amount of data generated, Federated Learning (FL) becomes a promising tool given that it enables privacy-preserving machine learning. However, the performance of the FL suffers from the failure of communication links and missing nodes. Therefore, we propose the use of Unmanned Aerial Vehicles (UAVs) as wireless relays to facilitate the communications between the IoV components and the FL server and thus improving the accuracy of the FL. However, a single UAV may not have sufficient resources for all iterations of the FL process. In this paper, we present a joint auction-coalition formation framework. The joint auctioncoalition formation algorithm is proposed to achieve a stable partition of UAV coalitions in which an auction scheme is applied. The auction scheme is designed to take into account the preferences of IoV components over heterogeneous UAVs. The simulation results show that the grand coalition, where all UAVs join a single coalition, is not always stable due to the profitmaximizing behavior of the UAVs. In addition, we show that as the cooperation cost of the UAVs increases, the UAVs prefer not to form any coalition.

The appearance of tumor cell clusters in pleural effusion is usually a vital sign of cancer metastasis. Segmentation, as an indispensable basis, is of crucial importance for diagnosing, chemical treatment, and prognosis in patients. However, accurate segmentation of unstained cell clusters containing more detailed features than the fluorescent staining images remains to be a challenging problem due to the complex background and the unclear boundary. Therefore, in this paper, we propose a fused 3-stage image segmentation algorithm, namely Coarse segmentation-Mapping-Fine segmentation (CMF) to achieve unstained cell clusters from whole slide images. Firstly, we establish a tumor cell cluster dataset consisting of 107 sets of images, with each set containing one unstained image, one stained image, and one ground-truth image. Then, according to the features of the unstained and stained cell clusters, we propose a three-stage segmentation method: 1) Coarse segmentation on stained images to extract suspicious cell regions-Region of Interest (ROI); 2) Mapping this ROI to the corresponding unstained image to get the ROI of the unstained image (UI-ROI); 3) Fine Segmentation using improved automatic fuzzy clustering framework (AFCF) on the UI-ROI to get precise cell cluster boundaries. Experimental results on 107 sets of images demonstrate that the proposed algorithm can achieve better performance on unstained cell clusters with an F1 score of 90.40%.

Underwater exploration activities have grown significantly due to the proliferation of underwater Internet of Things (UIoT). However, to transmit sensor data from UIoT to remote onshore data processing center requires a huge cost of deploying and maintaining communication infrastructures. In this article, we propose an unmanned aerial vehicles (UAVs)-assisted underwater data acquisition scheme by placing multiple sink nodes on the water surface to serve as intermediate relays between underwater sensors (IoT nodes) and UAVs. In our scheme, the sensor data are first transmitted via an acoustic-signal link to a buoyant sink node, which then forwards the data to a UAV via an electromagnetic link. In particular, we adopt two sink-node-deployment methods, i.e., grid placement and random placement of sink nodes. Since the path connectivity from an underwater sensor node to the UAV is crucial to guarantee reliable data acquisition tasks, we establish a theoretical framework to analyze the path connectivity via the intermediate sink node for both grid and random sink-node-deployment methods. Extensive simulation results validate the accuracy of the proposed analytical model. Moreover, our results also reveal the relationship between the path connectivity and other factors, such as sink node placements, antenna beamwidth of UAVs, and wind speed. We also further extend our UAV-assisted data acquisition to other scenarios with the consideration of trajectories of UAVs, movements of sink nodes, interference of both underwater acoustic and terrestrial radio links, and integration with edge computing.

The smart factory is a representative element reshaping conventional computer-aided industry to data-driven smart industry, while it is nontrivial to achieve cost effectiveness, reliability, mobility, and scalability of smart industrial systems. Data-driven industrial systems mainly rely on sensory data collected from statically deployed sensors. However, the spatial coverage of industrial sensor networks is constrained due to the high deployment and maintenance cost. Recently, mobile crowd sensing (MCS) has become a new sensing paradigm owing to its merits, such as cost effectiveness, mobility, and scalability. Nevertheless, traditional MCS systems are vulnerable to malicious attacks and single point of failure due to the centralized architecture. To this end, in this article we integrate MCS with industrial systems without introducing any additional dedicated devices. To overcome the drawbacks of traditional MCS systems, we propose a blockchain-based MCS system (BMCS). In particular, we exploit miners to verify the sensory data and design a dynamic reward ranking incentive mechanism to mitigate the imbalance of multiple sensing tasks. Meanwhile, we also develop a sensory data quality detection scheme to identify and mitigate the data anomaly. We implement a prototype of the BMCS on top of Ethereum and conduct extensive experiments on a realistic factory workroom. Both experimental results and security analysis demonstrate that the BMCS can secure industrial systems and improve the system reliability.

Artificial Intelligence (AI) based technologies have deeply changed people's daily lives. There are many AI-based applications used in industrial scenarios such as Internet of Things (IoT), smart grids, and edge computing. Although bringing AI into industrial scenarios could improve the performance in many aspects, new security and privacy issues are also introduced consequently. Subsequently, machine learning technologies require a training process which introduces the protection problems in the training data and algorithms. As many machine learning and deep learning models are vulnerable against well-designed adversarial input samples, outsourcing data and algorithms for training will require the integrity of the training data. Also, data privacy of the end users must be protected. On the other hand, traditional solutions for industrial system security could also be enhanced by these AI schemes. The papers in this special section focus on emerging privacy and security issues brought by Artificial Intelligence in industrial informatics.

Internet of Things (IoT) is reshaping the incumbent industry to smart industry featured with data-driven decision-making. However, intrinsic features of IoT result in a number of challenges, such as decentralization, poor interoperability, privacy, and security vulnerabilities. Blockchain technology brings the opportunities in addressing the challenges of IoT. In this paper, we investigate the integration of blockchain technology with IoT. We name such synthesis of blockchain and IoT as blockchain of things (BCoT). This paper presents an in-depth survey of BCoT and discusses the insights of this new paradigm. In particular, we first briefly introduce IoT and discuss the challenges of IoT. Then, we give an overview of blockchain technology. We next concentrate on introducing the convergence of blockchain and IoT and presenting the proposal of BCoT architecture. We further discuss the issues about using blockchain for fifth generation beyond in IoT as well as industrial applications of BCoT. Finally, we outline the open research directions in this promising area.

Assisting traffic control is one of the most important applications on the Internet of Vehicles (IoVs). Traffic information provided by vehicles is desired since drivers or vehicle sensors are sensitive in perceiving or detecting nuances on roads. However, the availability and privacy preservation of this information are critical while conflicted with each other in the vehicular communication. In this paper, we propose a semicentralized mode with attribute-based blockchain in IoVs to balance the tradeoff between the availability and the privacy preservation. In this mode, a method of control-by-vehicles is used to control signals of traffic lights to increase traffic efficiency. Users are grouped their attributes such as locations and directions before starting the communication. The users reach an agreement on determining a temporary signal timing by interacting with each other without leaking privacy. Final decisions are verifiable to all users, even if they have no a priori agreement and processes of consensus. The mode not only achieves the aim of privacy preservation but also supports responsibility investigation for historical agreements via ciphertext-policy attribute-based encryption (CP-ABE) and blockchain technology. Extensive experimental results demonstrated that our mode is efficient and practical.

Banks are collecting an unprecedentedly large amount of data about their customers from difference sources, considering their cyber, physical, social activities. The focus of this paper is to study the problem of information sharing and lower the communication overhead among different nodes for a specific data mining approach in distributed big data architectures. This problem can be abstracted as how to efficiently search under a specific cluster node topology. This paper proposes a new design rule for topologies including: 1) low coordination number; 2) high packing density; and 3) having a 3-D structure. According to this rule, a rhombic dodecahedron topology is proposed. A distributed banking big data mining framework based on the proposed topology is implemented. The experiments based on multioptimization benchmark functions show the excellent searching ability of the proposed topology; and a banking customer feature reduction prototype has been implemented to showcase the practicality of the data mining framework.

At present, smart manufacturing computing framework has faced many challenges such as the lack of an effective framework of fusing computing historical heritages and resource scheduling strategy to guarantee the low-latency requirement. In this paper, we propose a hybrid computing framework and design an intelligent resource scheduling strategy to fulfill the real-time requirement in smart manufacturing with edge computing support. First, a four-layer computing system in a smart manufacturing environment is provided to support the artificial intelligence task operation with the network perspective. Then, a two-phase algorithm for scheduling the computing resources in the edge layer is designed based on greedy and threshold strategies with latency constraints. Finally, a prototype platform was developed. We conducted experiments on the prototype to evaluate the performance of the proposed framework with a comparison of the traditionally-used methods. The proposed strategies have demonstrated the excellent real-time, satisfaction degree (SD), and energy consumption performance of computing services in smart manufacturing with edge computing.

The papers in this special section focus on blockchain for the Industrial Internet of Things (IoT). Industrial IoT is reshaping various industrial sectors, such as manufacturing, logistics, transportation, healthcare, energy, and utilities. IIoT consists of various smart objects distributed throughout the whole industrial system to collect massive ambient data, which can be used to identify performance bottlenecks, troubleshoot faults, and detect malicious behaviors consequently enforcing effective control to the physical world. However, there are several challenges posed on IIoT before the formal adoption of IIoT across various industrial sectors. Among them, security and privacy preservation on IIoT data are the most crucial concerns. On the other hand, the blockchain technology is transforming industries by enabling anonymous and trustful transactions in decentralized and trustless environment. As a result, blockchains help to reduce system risks, mitigate financial fraud, and cut down operational cost. The convergence of IIoT and blockchains can potentially overcome the deficiencies of IIoT consequently resulting in the realization of IIoT in various industrial sectors. Both industry practitioners and academic researchers aim at realizing general, scalable and deployable blockchain-based IIoT platforms in various application domains while there are a number of challenges such as distributed consensus algorithms and data analytics with privacy-preservation in IIoT systems.

Cloud computing extends Transportation Cyber-Physical Systems (T-CPS) with provision of enhanced computing and storage capability via offloading computing tasks to remote cloud servers. However, cloud computing cannot fulfill the requirements such as low latency and context awareness in T-CPS. The appearance of Mobile Edge Computing (MEC) can overcome the limitations of cloud computing via offloading the computing tasks at edge servers in approximation to users, consequently reducing the latency and improving the context awareness. Although MEC has the potential in improving T-CPS, it is incapable of processing computational-intensive tasks such as deep learning algorithms due to the intrinsic storage and computing-capability constraints. Therefore, we design and develop a lightweight deep learning model to support MEC applications in T-CPS. In particular, we put forth a stacked convolutional neural network (CNN) consisting of factorization convolutional layers alternating with compression layers (namely, lightweight CNN-FC). Extensive experimental results show that our proposed lightweight CNN-FC can greatly decrease the number of unnecessary parameters, thereby reducing the model size while maintaining the high accuracy in contrast to conventional CNN models. In addition, we also evaluate the performance of our proposed model via conducting experiments at a realistic MEC platform. Specifically, experimental results at this MEC platform show that our model can maintain the high accuracy while preserving the portable model size.

Traffic flow prediction has received extensive attention recently, since it is a key step to prevent and mitigate traffic congestion in urban areas. However, most previous studies on traffic flow prediction fail to capture fine-grained traffic information (like link-level traffic) and ignore the impacts from other factors, such as route structure and weather conditions. In this paper, we propose a deep and embedding learning approach (DELA) that can help to explicitly learn from fine-grained traffic information, route structure, and weather conditions. In particular, our DELA consists of an embedding component, a convolutional neural network (CNN) component and a long short-term memory (LSTM) component. The embedding component can capture the categorical feature information and identify correlated features. Meanwhile, the CNN component can learn the 2-D traffic flow data while the LSTM component has the benefits of maintaining a long-term memory of historical data. The integration of the three models together can improve the prediction accuracy of traffic flow. We conduct extensive experiments on realistic traffic flow dataset to evaluate the performance of our DELA and make comparison with other existing models. The experimental results show that the proposed DELA outperforms the existing methods in terms of prediction accuracy.

Electricity theft is harmful to power grids. Integrating information flows with energy flows, smart grids can help to solve the problem of electricity theft owning to the availability of massive data generated from smart grids. The data analysis on the data of smart grids is helpful in detecting electricity theft because of the abnormal electricity consumption pattern of energy thieves. However, the existing methods have poor detection accuracy of electricity theft since most of them were conducted on one-dimensional (1-D) electricity consumption data and failed to capture the periodicity of electricity consumption. In this paper, we originally propose a novel electricity-theft detection method based on wide and deep convolutional neural networks (CNN) model to address the above concerns. In particular, wide and deep CNN model consists of two components: the wide component and the deep CNN component. The deep CNN component can accurately identify the nonperiodicity of electricity theft and the periodicity of normal electricity usage based on 2-D electricity consumption data. Meanwhile, the wide component can capture the global features of 1-D electricity consumption data. As a result, wide and deep CNN model can achieve the excellent performance in electricity-theft detection. Extensive experiments based on realistic dataset show that wide and deep CNN model outperforms other existing methods.

Mobile edge computing (MEC) enables battery‐powered mobile nodes to acquire information technology services at the network edge. These nodes desire to enjoy their service under power saving. The sampling rate invariant detection (SRID) is the first downclocking WiFi technique that can achieve this objective. With SRID, a node detects one packet arrival at a downclocked rate. Upon a successful detection, the node reverts to a full‐clocked rate to receive the packet immediately. To ensure that a node acquires its service immediately, the detection performance (namely, the miss‐detection probability and the false‐alarm probability) of SRID is of importance. This paper is the first one to theoretically study the crucial impact of SRID attributes (e.g., tolerance threshold, correlation threshold, and energy ratio threshold) on the packet detection performance. Extensive Monte Carlo experiments show that our theoretical model is very accurate. This study can help system developers set reasonable system parameters for WiFi downclocking.

In cognitive radio networks (CRNs), the transmissions of primary users (PUs) will cause interference to secondary users (SUs), consequently resulting in the less spectrum available at SUs. Instead of regarding the radio signals radiated by PUs as interference, these signals can be opportunistically harvested by SUs as energy. We name such cognitive radio networks with wireless energy harvesting technology as (WEH-CRNs). However, most of previous studies mainly consider using omni-directional antennas in WEH-CRNs (OMN-WEH-CRNs), consequently resulting in high interference. In this paper, we consider using directional antennas in WEH-CRNs (DIR-WEH-CRNs) to improve the performance of WEH-CRNs. We establish an analytical model to analyze the transmission probability of SUs in both DIR-WEH-CRNs and OMN-WEH-CRNs. Our simulation results show that our proposed model can accurately analyze the transmission probability of SUs in both DIR-WEH-CRNs and OMN-WEH-CRNs. Moreover, our results also show that the transmission probability of SUs depends on both the spectrum availability and the probability of SUs being charged. Compared with OMN-WEH-CRNs, DIR-WEH-CRNs can achieve higher transmission probability of SUs due to the reduced interference.

This paper studies the throughput performance of a two-way energy harvesting relaying system. Network Coding and Energy Harvesting are promising techniques that can improve the transmission efficiency and the energy efficiency of wireless systems, respectively. In particular, we focus on the energy harvesting system with the power splitting-based relaying (PSR) protocol, and consider both the amplify-and-forward (AF) relaying and decode-and-forward (DF) relaying methods for the network coding. We successfully derive the expressions for both the outage probability and the system throughput for each case. It can be shown that DF relaying system outperforms AF relaying system in terms of both the outage probability and the throughput. Furthermore, simulations show that the throughput gain brought by network coding highly depends on the signal-to-noise ratio (SNR) at the receiver. The throughput gain can be up to 33% in the high SNR region.

To protect confidential communications from eavesdropping attacks in wireless networks, we propose a novel anti-eavesdropping scheme named AE-Shelter. In our proposed scheme, we place a number of friendly jammers at a circular boundary to protect legitimate communications. The jammers sending artificial noise can mitigate the eavesdropping capability of wiretapping the confidential information. We also establish a theoretical model to evaluate the performance of AE-Shelter. Our results show that our proposed AE-Shelter scheme can significantly reduce the eavesdropping risk without significantly degrading the network performance.

This paper proposes a novel anti-eavesdropping scheme by introducing artificial noise caused by friendly jammers deployed in wireless networks. In particular, we propose an analytical model to quantify the eavesdropping risk of wireless network with friendly jammers. Our approach considers both large-scale path loss and Rayleigh fading. Our numerical results show that the eavesdropping risk of wireless networks can be significantly reduced with the aid of firendly jammers.

In this paper, we propose a theoretical framework to investigate the eavesdropping behavior in underwater acoustic sensor networks. In particular, we quantify the eavesdropping activities by the eavesdropping probability. Our derived results show that the eavesdropping probability heavily depends on acoustic signal frequency, underwater acoustic channel characteristics (such as spreading factor and wind speed) and different hydrophones (such as isotropic hydrophones and array hydrophones). Simulation results have further validate the effectiveness and the accuracy of our proposed model.

In this paper, we propose a novel anti- eavesdropping scheme by introducing friendly jammers to a wireless network of things (WNoT). In particular, we establish a theoretical framework to evaluate the eavesdropping risk of WNoT with friendly jammers and the eavesdropping risk of WNoT without jammers. Our theoretical model takes into account various channel conditions such as the path loss and Rayleigh fading as well as the placement schemes of jammers. Our extensive numerical results show that using jammers in WNoT can effectively reduce the eavesdropping risk. Besides, our results also show that the eavesdropping risk heavily depends on both the channel conditions and the placements of jammers.

We investigate the percolation connectivity of wireless ad hoc networks with directional antennas (called DIR networks). One of major concerns is to derive bounds on the number of edge-disjoint directed paths (or highways). However, it is non-trivial to obtain bounds on the number of directed highways in DIR networks since the conventional undirected percolation theory cannot be directly used in DIR networks. In this paper, we exploit the directed percolation theory to derive bounds on the number of directed highways. In particular, we make new constructions in bond directed percolation model. We show that with high probability there are at least Ω(√n/log log √n) directed highways in a network with n nodes, which is much tighter than the existing results in DIR networks.

This paper concerns with the local connectivity (i.e., the probability of node isolation) of wireless networks with directional antennas. We propose an analytical framework to study the local connectivity with the consideration of directional antenna models and various channel conditions. With the framework, we construct a novel directional antenna model called Iris. We show that Iris can better approximate realistic directional antennas and can be easily used to analyze the local connectivity compared with existing directional antenna models. Extensive simulations show that the theoretical results are in good agreement with the simulation results verifying the accuracy and the effectiveness of our analytical framework.

In this paper, we propose a novel multi-channel wireless network with infrastructure support, called an MC-IS network. To the best of our knowledge, we are the first to study the capacity and the delay of such an MC-IS network. In particular, we derive the upper bounds and the lower bounds on the network capacity of such MC-IS networks contributed by ad hoc communications, where the orders of the upper bounds are the same as the orders of the lower bounds, implying that the bounds are tight. We also found that the capacity of MC-IS networks contributed by ad hoc communications is mainly limited by connectivity requirement, interference requirement, destination-bottleneck requirement and interface-bottleneck requirement. In addition, we also derive the average delay of MC-IS networks contributed by ad hoc communications, which is bounded by the maximum number of hops.

This paper concerns the impact of various antenna models on the network connectivity of wireless ad hoc networks. Existing antenna models have their pros and cons in the accuracy reflecting realistic antennas and the computational complexity. We therefore propose a new directional antenna model called Approx-real to balance the accuracy against the complexity. We then run extensive simulations to compare the existing models and the Approx-real model in terms of the network connectivity. The study results show that the Approx-real model can better approximate the best accurate existing antenna models than other simplified antenna models, while introducing no high computational overheads.

This paper investigates the problem of link scheduling to meet traffic demands with minimum airtime in a multi-transmit-receive (MTR) wireless network. MTR networks are a new class of networks, in which each node can simultaneously transmit to a number of other nodes, or simultaneously receive from a number of other nodes. The MTR capability can be enabled by the use of multiple directional antennas or multiple channels. Potentially, MTR can boost the network capacity significantly. However, link scheduling that makes full use of the MTR capability must be in place before this can happen. We show that optimal link scheduling can be formulated as a linear program (LP). However, the problem is NP-hard because we need to find all the maximal independent sets in a graph first. We propose two computationally efficient algorithms, called Heavy-Weight-First (HWF) and Max-Degree-First (MDF) to solve this problem. Simulation results show that both HWF and MDF can achieve superior performance in terms of runtime and optimality.

There are a number of studies on using directional antennas in wireless networks. Many of them concentrate on analyzing the theoretical capacity improvement by using directional antennas. Other studies focus on designing proper Medium Access Control (MAC) protocols to improve the practical network throughput. There are few works on the security improvement using directional antennas. In this paper, we explore the benefits of directional antennas in security improvements on both single-hop and multi-hop wireless networks. In particular, we found that using directional antennas in wireless networks can significantly reduce the eavesdropping probabilities of both single-hop transmissions as well as multi-hop transmissions and consequently improve the network security.

Recent studies have found that using multiple channels can separate concurrent transmissions and significantly improve network throughput. However, these studies have only considered wireless nodes that are equipped with omni-directional antennas, which have high interference. On the other hand, other researchers have found that using directional antennas in wireless networks can reduce interference and improve the network performance. But their studies have only considered single channel to be used in wireless networks with directional antennas. Thus, integrating the two technologies of multiple channels and directional antennas together can potentially bring more benefits. Some previous works have studies the capacity on the multi-channel wireless networks using directional antennas. However, the channel assignment problem of such networks has not been well studied. In this paper, we study the channel assignment problem in the multi-channel wireless networks using directional antennas. In particular, we study the problem: given a set of wireless nodes equipped with directional antennas, how many channels are needed to ensure collision-free transmission? We derive the upper bounds on the number of channels, which depend on the node density. We also construct several scenarios to examine the tightness of the derived bounds. Our result can be used to estimate the number of channels required for a practical wireless network. Besides, our results can also be used to provide a suggestion on the proper node density in the node deployment when the number of channels is given for a wireless network.

The capacity of wireless ad hoc networks is affected by two key factors: the interference among concurrent transmissions and the number of simultaneous transmissions on a single interface. Recent studies found that using multiple channels can separate concurrent transmissions and greatly improve network throughput. However, those studies only consider that wireless nodes are equipped with only omnidirectional antennas, which cause high collisions. On the other hand, some researchers found that directional antennas bring more benefits such as reduced interference and increased spatial reuse compared with omnidirectional antennas. But, they only focused on a single-channel network which only allows finite concurrent transmissions. Thus, combining the two technologies of multiple channels and directional antennas together potentially brings more benefits. In this paper, we propose a multi-channel network architecture (called MC-MDA) that equips each wireless node with multiple directional antennas. We derive the capacity bounds of MC-MDA networks under arbitrary and random placements. We will show that deploying directional antennas to multi-channel networks can greatly improve the network capacity due to increased network connectivity and reduced interference. We have also found that even a multi-channel network with a single directional antenna only at each node can give a significant improvement on the throughput capacity. Besides, using multiple channels mitigates interference caused by directional antennas. MC-MDA networks integrate benefits from multi-channel and directional antennas and thus have significant performance improvement.

The application of directional antennas in wireless ad hoc networks brings numerous benefits, such as increased spatial reuse and mitigated interferences. Most MAC protocols with directional antennas are based on the RTS/CTS mechanism which works well in wireless ad hoc networks using omnidirectional antennas. However, RTS/CTS frames cannot mitigate the interference completely. Besides, they also contribute a lot to the performance overhead. This paper studies the problem from a new perspective. We have found that the transmission success probability under directional transmission and directional reception is quite high when the antenna beamwidth is quite narrow. Motivated by the analytical results, we design a lightweight MAC protocol without RTS/CTS frames. The preliminary results demonstrate that this new protocol performs better than MAC protocols based on the RTS/CTS mechanism. The results also show that a collision-tolerant transmission is feasible under the narrow beam configuration.

Applying directional antennas in wireless ad hoc networks offers numerous benefits, such as extended communication range, increased spatial reuse, improved capacity and suppressed interference. However, directional antennas can cause new location-dependent carrier sensing problems, such as new hidden terminal and deafness problems, which can cause severe penalties to the performance. Recently, a few schemes have been proposed to tackle these problems. However, these methods can provide limited solutions on the hidden terminal and deafness problems. We propose a new MAC protocol, termed the busy-tone based directional medium access control (BT-DMAC) protocol. When the transmission is in progress, the sender and the receiver will turn on their omnidirectional busy tones to protect the transmission. By combining with directional network allocation vector (DNAV), the scheme almost mitigates the hidden and the deafness problems completely. The mechanism increases the probability of successful data transmission and consequently improves the network throughput. This paper describes the BT- DMAC scheme and analyzes its performance. The simulation results also demonstrate the effectiveness of the protocol.