Abstract In recent years, deep learning-based emotion recognition from electroencephalography (EEG) signals has garnered significant attention in brain-computer interfaces. However, effectively capturing local and global dependencies remains a challenge due to the complexities of EEG data. Furthermore, traditional convolutional neural networks and RNNs often struggle to fully explore the spatio-temporal relationships between different features. To address these issues, we propose an end-to-end model with the augmented capsule-gated Transformer to improve the performance of EEG emotion recognition, in which we learn cross-channel spatial features effectively, and the raw EEG signals are automatically weighted to emphasize key attributes. Subsequently, the capsule network extracts low-level and high-level spatial information, fully leveraging the potential insights within the signals. Building on this, an efficient Transformer is employed to model the relationships among different electrodes, allowing for a more in-depth analysis of the temporal dependencies across multiple features. Extensive experiments are conducted on the Dataset for Emotion Analysis using Physiological Signals (DEAP) dataset, and comparison results with existing state-of-the-art methods demonstrate the superior performance of the proposed method. Specifically, for the arousal and valence dimensions, the average recognition accuracies in subject-dependent experiments reach 93.51% and 94.24%, while the subject-independent experiments achieve average accuracies of 86.78% and 87.59%.

The evolution of the Internet of Underwater Things has positioned Autonomous Underwater Vehicles as critical mobile edge nodes, yet the adverse underwater acoustic communication environment, characterized by high latency and low bandwidth, severely constrains the performance of collaborative sensing. To address these challenges, we propose the Kernelized Intrinsic McAulay-Seidman Bound as an online proxy for network Quality of Service. This metric overcomes the optimal test point selection difficulty and theO(L3)computational complexity inherent in the standard Intrinsic McAulay-Seidman Bound. The K-IMSB framework reformulates the discrete problem into a continuous functional optimization within a Reproducing Kernel Hilbert Space. By leveraging variational methods and a heat kernel onSO(3), we derive a closed-form expression that ultimately involves solving an ill-posed Fredholm integral equation of the first kind. To efficiently solve this, a Recursive Ridge Leverage Score Nystr¨om approximation algorithm is introduced, enabling lightweight, energy-efficient computation on the edge. This algorithm utilizes statistical leverage scores to adaptively identify critical manifold regions, thereby solving the dual challenges of operator discretization and numerical instability. Simulation results for an AUV attitude estimation scenario with Out-of-Sequence Measurements demonstrate that the K-IMSB provides a tight lower bound, and the RLS-Nyström method improves computational efficiency by approximately 21.17%, achieving real-time feasibility for IoUT edge deployment.

The rapid popularization of the Internet of Vehicles demands efficient, privacy-preserving distributed learning. However, deploying Federated Learning in dynamic IoV environments faces a ”trilemma” of model adaptability, communication efficiency, and privacy, exacerbated by severe spatiotemporal data heterogeneity. To address this issue, we propose a personalized hierarchical framework that integrates split meta-learning within a ”vehicle-edge-cloud” architecture, referred to as pHFSML. At the vehicle-edge layer, semi-asynchronous split meta-learning protocols significantly reduce vehicular computational burdens, enabling rapid local adaptation. At the edge-cloud layer, gradient-sensitive momentum aggregation and loss-adaptive personalization ensure global stability while retaining local precision. Extensive experiments on non-IID benchmarks verify pHFSML’s superiority. Specifically, on the domain-relevant GTSRB dataset, pHFSML achieves 93.03% accuracy, outperforming state-of-the-art baselines by 0.80%, with a convergence speed 38.8% faster than FedAvg. Ablation studies further validate the necessity and synergistic effects of the proposed components.

Optimizing sensor placement is crucial for enhancing the coverage and data-acquisition efficiency of ocean monitoring systems. Traditional approaches primarily rely on univariate ocean data for sensor placement, failing to capture the multidimensional coupling characteristics of the ocean environment, while the potential of multivariate datasets remains underexplored. To address this limitation, this work proposes an innovative Multivariate Fractal Autoencoder (MFAE) framework that leverages multivariate data to solve the sparse sensor placement problem. The MFAE optimizes sensor placement by dynamically updating multivariate feature weights and extracting latent spatial correlations. Furthermore, by optimizing feature weight initialization and enhancing autoencoder training protocols, we propose an Entropy-weighted Multivariate Fractal Autoencoder (EnMFAE) to establish an accurate nonlinear mapping between low-dimensional sampling spaces and full-state reconstructions. Validation experiments are conducted on temperature and salinity datasets from the North Pacific and Arctic Oceans, and the results demonstrate the superior performance of MFAE and EnMFAE relative to the POD, QR, and random placement baselines. With only 10 selected sensors, the MFAE achieves average reconstruction error reductions of 2.96% (for temperature) and 2.12% (for salinity) in the North Pacific, and 5.78% (for temperature) and 7.71% (for salinity) in the Arctic, respectively, significantly outperforming the compared random placement method with decoder-based reconstruction. MFAE offers a novel paradigm for optimizing sensor networks in complex ocean environments by leveraging multivariate data reconstruction.

With the proliferation of Internet of Things (IoT) devices, vast amounts of sensitive data are frequently exchanged across networks, making identity authentication crucial to secure communication. However, existing authentication schemes generally suffer from complex certificate management, difficult key custody, vulnerability to various attacks, and high overhead, making them unsuitable for resource-constrained IoT devices. Despite blockchain technology showing promise in decentralized authentication, consensus mechanisms’ high computational and latency costs hinder their application in cross-domain environments. To address these challenges, this paper proposes a lightweight cross-domain authentication and key agreement protocol, termed LCH-AKA, designed for heterogeneous IoT systems. LCH-AKA integrates Physical Unclonable Function (PUF), blockchain, and edge computing technologies to construct a decentralized and tamper-resistant authentication framework. The proposed scheme eliminates the need for traditional certificates and centralized key management, while enabling efficient device-to-device and device-to-server mutual authentication. Formal verification and experimental evaluation demonstrate that LCH-AKA achieves strong security, scalability, and low resource consumption. Compared with existing approaches, it provides lower computational and communication overhead, reduced latency, and improved energy efficiency, making it suitable for large-scale deployment in heterogeneous IoT networks.

The hierarchical federated learning framework significantly reduces communication burdens on central servers; however, the integration of edge servers introduces potential risks such as Single Point of Failure (SPOF) and ongoing challenges like imbalanced data distribution. To tackle these challenges, we propose TrustHFL, an innovative aggregation method designed for secure hierarchical federated learning. TrustHFL enhances training efficiency by employing group training that clusters clients with similar data distribution characteristics. Inside each cluster, synchronous aggregation is implemented, while asynchronous aggregation is utilised between clusters to alleviate delays from bottleneck clients. We also introduce a robust access control mechanism for secure interactions between clients and edge servers, ensuring data privacy and system integrity. Moreover, our design favours off-chain computation and training, limiting on-chain storage to essential information and thereby minimising both storage and computational demands on the blockchain, ultimately enhancing training efficiency. Extensive experimental results demonstrate that the proposed method accelerates convergence speed and enhances model accuracy. Compared to existing classical federated learning methods, the model accuracy is improved by an average of 1.98% under various data distribution scenarios, while the time required to achieve the same accuracy is reduced by an average of 65.54%.

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

Unmanned Aerial Vehicles (UAVs) play a significant role in wireless communication because of their high maneuverability and the advantage of forming Line-of-Sight (LoS) links with ground users. In this research, we investigate the trajectory design and resource scheduling problem of UAV relay communication optimization scenarios, considering heterogeneous tasks and QoS (Quality of Service) awareness. First, we model the optimization scenario and transform the problem-solving into a Markov Decision Process (MDP). Next, we propose R-DQN, a robust DQN (Deep Q-Network) algorithm tailored for heterogeneous tasks and QoS-aware UAV relay communication optimization scenarios. R-DQN introduces corresponding mechanisms in the training process, network structure, and sampling method to improve the effective exploration capability of DQN, making it more robust and suitable for the dynamic constrained optimization scenario tackled. Simulations and experimental results show that the proposed R-DQN algorithm has better convergence and global optimization abilities than other algorithms, such as Dueling DQN, Noisy DQN, and DDQN.

In Unmanned Aerial Vehicle Ad Hoc Networks (UANETs), rapid movement of nodes leads to frequent changes in network topology, increasing the risk of packet loss and affecting data transmission. Furthermore, current research on drone clustering lacks security considerations, which reduces the reliability of data transmission. Due to this, improving the stability and reliability of network data transmission in dynamically changing UANETs remains a challenge. In this work, we propose the reputation mechanism DSCR (Dynamically Secure Cluster Routing) Scheme for UANETs, a design for cluster head election in UANETs using the evaluated value and reputation value of drones and forms clusters through this reputation mechanism to avoid malicious nodes from interfering with the clusters to improve the security of UANETs. We apply a residual link survival time-based intra-and inter-cluster algorithm based on residual link survival time for data forwarding of nodes, optimizing the dynamic routing strategy and reducing the packet loss rate of nodes. In addition, reinforcement learning is used in UANETs to achieve cluster decisions based on the current network state and cluster dynamics, effectively improving the stability of clusters. Experiments were conducted on the proposed method to verify its efficiency and stability. Compared to the ICRA, RICR, and DCA algorithms, the stability of the cluster is improved by 11.92%, 28.12%, and 75.2%, respectively, and the packet loss rate reduced by 41.48%, 44.17%, and 55.74%, demonstrating that DSCR is a compelling dynamic routing solution applicable to UANETs.

Range-based localization technology achieves high accuracy under clear signal paths (Line-of-Sight, LOS). However, its performance deteriorates significantly due to errors in distance estimation when signals encounter obstructions, resulting in Non-Line-Of-Sight (NLOS) propagation. In light of these challenges, we investigate the combined effects of measurement noise and NLOS errors on target localization performance and propose a novel approach using Rotated Quadratic Cone Programming (RotQCP) for target localization in Wireless Sensor Networks (WSNs). By formulating the localization problem as a Maximum Likelihood (ML) estimation and employing relaxation techniques, we demonstrate that RotQCP can effectively address it even in the worst-case scenario. Compared to existing methods, the proposed approach eliminates the requirement for specific NLOS error statistics and delivers robust performance in sparsely and heavily congested NLOS environments. The simulation results demonstrate the efficacy of the proposed method in mitigating NLOS errors and attaining accurate localization. Moreover, the experimental outcomes based on open datasets substantiate the effectiveness of the proposed algorithm and indicate its superiority over existing algorithms. Notably, this research offers a robust and efficient solution for target localization in WSNs, particularly in a real harsh environment characterized by mixed LOS and NLOS propagation conditions.

Optimizing multi-instance service composition and dynamic request routing has become a critical challenge in cloud-edge collaborative systems. Existing solutions struggle with effectively balancing performance, cost, and bandwidth constraints in dynamic and resource-constrained environments. In this work, we address these challenges by proposing the Removed Minimum Cost Flow (RMCF) algorithm, aiming to minimize average response time while considering constraints such as budget and bandwidth, making it well-suited for time-sensitive services in a cloud-edge collaborative service provision system. Simulations were conducted using real-world data from China Telecom’s base stations in Shanghai, and experiments in various scenarios were considered, including time-sensitive services and general application services, with key performance indicators such as time utility, cost-utility, request completion rate, and timeout rate. The experimental results demonstrate that RMCF achieves lower response times, superior performance, and improved cost-effectiveness compared to other baseline algorithms.

Underwater Acoustic Internet of Things Networks (UAIoTNs) can furnish excellent technical support and information services for applications involving marine observation and detection, marine disaster prevention and mitigation, and maritime search and rescue, in which accurate positioning information is the fundamental requirement. The combination of high dynamics and complexity of the ocean environment to the high latency and narrowband of underwater acoustic communication are complex challenges in UAIoTNs. Due to these facts, this work investigates the received signal strength (RSS)-based three-dimensional (3D) target localization in UAIoTNs taking into account the absorption effect, uncertain transmission power (UTP), and a time-varying Path Loss Exponent (PLE). Through Taylor’s first-order expansion and certain approximations, we envision the underwater stratified acoustic propagation localization challenge as an Alternating Non-negative Constrained Least Squares (ANCLS) framework. To address the challenges posed by unknown multi-parameters, a robust coarse-to-fine localization algorithm (RCFLA) is proposed. At first, the coarse localization phase utilizes the Active Set Method (ASM), while the subsequent fine localization one employs the improved Broyden-Fletcher-Goldfarb-Sanno (BFGS) trust region method to enhance convergence towards the global optimal solution. The iterative process refines the underwater target location, UTP, and PLE, using the ASM-derived rough solution as the initial estimate. Analysis of computational complexity and derivation of the Cramér-Rao Lower Bound (CRLB) with stratified propagation and absorption effect demonstrates the superiority of RCFLA. Furthermore, Lyapunov’s second stability theorem is used to prove the stability of the RCFLA and presents a complete proof of global convergence. Numerical simulation and experimental results validate the algorithm’s optimal localization accuracy across various scenarios, showing reduced overhead compared to benchmark algorithms.

This article presents a robust 3-D Received Signal Strength Difference (RSSD) localization algorithm under mixed Gaussian noise in Underwater Wireless Sensor Networks (UWSNs) with Non-Line-Of-Sight (NLOS) paths. To mitigate the adverse effects, concurrent to absorption and path losses on accurate underwater localization, an Efficient RSSD-based Iterative Estimator (ERIE) in mixed Gaussian noise and NLOS environments is proposed. First, the corresponding non-convex problem in such environments is formulated, and the direct solution to this problem is not tractable unfortunately. Considering underwater acoustic signal attenuation, an RSSD-based min-max strategy is designed to transform it into a problem minimizing the worst-case loss, combined with the Huber cost function, constitutes a Huber function-based equivalent problem (H-ADMM) solved by Alternating Direction Method of Multipliers (ADMM). A compensation matrix is designed based on the H-ADMM solution to compensate for the bias introduced by the transformation, and the corresponding Cramér-Rao Lower Bound (CRLB) is derived to provide a performance benchmark. Numerical results indicate that the proposed approach achieves a higher localization accuracy than state-of-the-art methods.

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

Federated Learning (FL) enables collaborative model training across maritime devices without the need to share raw data. However, challenges such as data heterogeneity and unreliable marine communications impede its performance and security. In this work, we propose a Blockchain-based Active Client Selection Strategy for FL in Shipping (BAFLS), which utilizes blockchain technology to create a secure and auditable environment for node registration and parameter exchange. A lightweight consensus algorithm is introduced to dynamically elect aggregation nodes based on residual energy, reputation, and computing power, improving fault tolerance and reducing resource consumption. Based on such, a Top-kactive learning strategy is designed to select the most informative clients, balancing data utility and privacy protection. Security evaluation and analysis demonstrate that BAFLS effectively resists aggregation attacks and privacy inference. Experimentations on FMNIST, HAR, and ShipNetwork10 datasets show that BAFLS achieves up to 2.4% higher accuracy, reduces convergence rounds by up to 44%, and consistently lowers communication overhead compared to the baseline under various degrees of label and feature heterogeneity.

As a form of distributed machine learning, Federated learning allows users to complete training without sharing local data, thereby protecting user privacy to a certain extent. However, the gradients uploaded by users during the training process can still leak user privacy. Additionally, malicious or lazy cloud servers may tamper with or forge the aggregated results before returning them to users, causing significant losses to the entire training process. Existing solutions focus on security issues, but most privacy protection schemes based on complex cryptographic primitives require high computational power and communication bandwidth. Moreover, to verify the aggregated results, each user must compute proofs, which imposes an additional computational burden on users. Therefore, designing more efficient and lightweight solutions that ensure security while adapting to resource-constrained scenarios is necessary. An efficient group-based scheme for privacy preservation and verification outsourcing in federated learning, referred to as GPVO-FL, is introduced in this work. Specifically, we design a lightweight privacy protection mechanism based on group structure and masking techniques to protect user gradients. In addition, we design an outsourced verification mechanism that offloads the verification process to edge servers, thus reducing the computational burden on users. A detailed security and experimental analysis demonstrates the security and efficiency of our scheme.

User-generated multimodal data can provide powerful sentiment clues for sentiment analysis task. Existing works have aligned common sentiment features in different modalities through various multimodal fusion methods. However, these works have certain limitations: (1) Previous research works only align common sentiment features between image and text, without fully exploring interactions among these features, leading to suboptimal analysis results. (2) Redundant noise in image and text increases the risk of feature misalignment during cross-modal alignment. To address these issues, we propose a Multimodal Semantic Fusion Network (MSFN) to deeply explore the semantic relationship between image and text for Multimodal Sentiment Analysis (MSA). Specifically, we align image region and text word features related to sentiment by using a gated attention mechanism. Subsequently, we employ graph convolutional networks to model the interactions among these features to obtain explicit sentiment semantics. The proposed gated attention mechanism corrects potential feature misalignment during cross-modal alignment using a gating mechanism. Moreover, considering not all image–text pairs have explicit corresponding sentiment features, we integrate implicit sentiment semantics to our model for enhancing reliability in analysis. Experimental results on benchmark datasets demonstrate the effectiveness of our proposed model compared to baselines.

In recent years, the refinements in industrial processes and the increasing complexity of managing privacy-sensitive data from Industrial Internet of Things (IIoT) devices, have highlighted the critical need for secure, robust, and adaptive data management solutions. In this work, we propose a smart contract-assisted access control scheme for IIoT, which employs the Attribute-Based Access Control (ABAC) model to set access permissions for different industrial components. We defined a storage model and data format for private data through the design and deployment of smart contracts to manage system operations and access policies. In addition, the bloom filter component is deployed to optimize the efficiency of contract management and system performance. Experimental results show that in the real-world simulations, Hyper-IIoT shows well-controlled contract execution time, stable system throughput and fast consensus process, and is capable of handling high throughput and effective consensus in distributed systems even in large-scale request scenarios.

Recommendations for ride-hailing zones are crucial for matching drivers with passengers efficiently, improving mobility, and managing traffic effectively. However, current recommendation methods based on centralized machine learning suffer from privacy concerns due to the need for data sharing and aggregation. Furthermore, the lack of personalized results tailored to individual regions or drivers within a single architecture often leads to spatial supply imbalances in ride-hailing services. To address these challenges, we propose in this work federated geo-aware matrix factorization (FedGeoLMF), a framework for vehicular-cloud collaborative federated learning that identifies vehicles with similar passenger patterns, aiming to enhance the accuracy and efficiency of recommendations by improving local nodes for federated learning. At local nodes, federated matrix factorization algorithms based on historical trajectory data of moving vehicles and fused geographical information to achieve local learning and recommendations are designed. Additionally, we introduce a communication mechanism tailored for federated learning models exchanged between servers and mobile vehicles, employing the named data networking (NDN) protocol that streamlines parameter uploading and downloading processes, unveiling akin patterns within local federated matrix decomposition outcomes by harnessing the NDN routing algorithm, delivering personalized collaborative recommendations while upholding the confidentiality of user-sensitive data. Finally, we design an online recommendation method based on driver location and reachability matrices to recommend optimal ride-hailing zones for drivers in different locations. Experimental results demonstrate that the proposed method outperforms current baseline models in terms of accuracy and training efficiency and can efficiently provide personalized ride recommendations while ensuring privacy in the ride-hailing service domain.

Underwater acoustic Internet of Things Networks (UAIoTNs) play a crucial role in oceanographic and environmental monitoring, necessitating precise localization for optimal functionality. However, the underwater setting introduces significant challenges, encompassing the stratification effect arising from underwater heterogeneity, uncertainty in anchor positions due to currents, and variations in the signal transmission environment. These factors collectively impede the accurate estimation of location. Consequently, this paper addresses these challenges by analyzing and deriving a closed-form solution using a time-of-arrival (TOA)-based technique for 3D localization in UAIoTNs. The investigation establishes an underwater stratified propagation model, drawing inspiration from ray tracing theory and Snell’s law. Employing the Cramér-Rao lower bound (CRLB) framework, we explore scenarios both with and without considering perturbed anchors, utilizing the Banachiewicz-Schur theorem. To quantify the impact of the stratification effect and perturbed anchors on CRLB and mean square error (MSE), we further analyze and derive an MSE expression, employing Taylor-series linearization. Building on our analysis of the detrimental effects of stratification and inaccurate anchors, we introduce a multiple-weighted least squares (MWLS) algorithm to alleviate potential performance losses. This approach integrates a matrix operator in the update step, eliminating variable dependencies and resulting in a closed-form solution that circumvents the need for iterative processes. Our simulation results validate our analytical findings and demonstrate the effectiveness of the proposed method, showcasing improved localization accuracy across various scenarios when compared to state-of-the-art approaches.

In recent years, open mobile social networks focused on socializing and dating purposes have gained widespread popularity, such as Soul, Tinder, Momo, and Tantan, among several others. These applications permit users to post, comment, and send private messages to other users without their consent, making communication accessible. However, this low-entry communication approach has also increased malicious user attacks. We delve into a comprehensive analysis of malicious accounts in open socializing and dating applications, revealing that the existing methods overlook hidden malicious signals within the user text-related information, thus resulting in poor detection performance. For such, we propose GraphTAM, a novel graph- and text-based multihead attention fusion network model for detecting such malicious accounts, consisting of modules that effectively combine nontext-related and text-related information, enhancing the accuracy and performance of detecting malicious accounts. We employ graph convolutional networks (GCNs) for nontext-related information to extract advanced representations of users, incorporating their attribute and social relationship features. Regarding text-related information, we employ a multihead attention model to identify suspicious patterns in users’ posted articles, comments, and relevant behavioral statistics, so finally, we merge the advanced representations of nontext-related and text-related information using a multilayer perceptron to determine the maliciousness of an account. Data sets collected from SLink are utilized for the experimental evaluation and to compare the performance of the proposed model with the several state of the art algorithms. Experimental results show significant advantages in malicious account detection, where the F1 score achieves over 0.9, outperforming the existing methods that range between 0.6 and 0.85. Furthermore, the comparative experiments substantiate the critical role of text-related information in detecting malicious accounts in open socializing and dating applications.

With the increasing integration of location-based services (LBSs) into various societal domains, location privacy preservation has emerged as a pivotal concern. In most continuous LBS privacy protection approaches, the user needs to send a query to an untrusted location service provider (LSP) to request the corresponding query results, and these results are discarded immediately after being used. This leads to similar queries in the future having to be sent to the LSP again, which increases the risk of privacy leakage when facing the LSP. To solve these issues, caching techniques are typically used to provide answers to users’ future queries. However, minimizing the interaction with the LSP is a challenge. Here, we propose a trajectory privacy-preserving scheme based on a transition matrix and caching (TMC) scheme for continuous LBS in the Industrial Internet of Things (IIoT). It employs multilevel caching to reduce the risk of exposing sensitive information to untrusted entities. We designed a transition matrix to predict the user’s next query location and simplify the computation complexity. We designed a cloaking set generation algorithm by considering transition entropy, location prediction, data freshness, and cache contribution degree to enhance user location privacy and improve the cache hit rate. The security analysis demonstrates how the TMC scheme resists attacks from both internal and external entities and ensures robustness in trajectory privacy. The experimental results show that the proposed TMC scheme can provide a higher level of privacy protection and lower system overhead compared to several previous schemes.

Cloud computing and the Internet of Things (IoT) provide robust technological support for the development of image retrieval services. Specifically, images are highly sensitive and private data in e-healthcare and security surveillance. Existing retrieval schemes often do not strike a good balance between privacy protection and retrieval performance. It makes data vulnerable to illegal attacks and leads to high retrieval costs, thereby affecting the overall quality and usability of the system. To address these issues, this paper introduces a fine-grained secure approximate image retrieval (FSAIR) scheme for mobile cloud computing. Our approach implements a multi-verification architecture that provides precise identity control and an untraceable strategy. Furthermore, FSAIR constructs a flexible and secure hierarchical structure to support the efficient retrieval of large-scale high-dimensional data. By introducing Bloom filters to replace index nodes, FSAIR ensures system efficiency and employs bit-pattern encoding descriptors to search approximate data without violating privacy. We show that the proposed scheme protects data under different threat modes through security analysis. We also demonstrate the feasibility and superiority of the proposed solution through experimental evaluation, using the Caltech-256 dataset.

Biometric and password-based two-factor authentication has received attention from the community over the past decades because of its simplicity, portability, and robustness. In wireless body area networks (WBANs), dozens of authentication and key agreement schemes have been proposed. Despite well-studied security issues, preserving user privacy in these schemes is still challenging. In this work, we propose biometric-based authentication and key agreement (BAKA), a scheme based on a fuzzy extractor for WBAN, where a novel biometric and password-based authentication algorithm is proposed by using a fuzzy extractor to achieve anonymous identity authentication, a privacy-preserving key agreement algorithm for session key security, and finally, we deploy blockchain to record biometric information using its noncomparability and distributed storage to protect users’ privacy to a large extent. BAKA is secure as per formal security proof and informal security analysis, symmetric encryption is utilized to reduce computation overhead to improve the efficiency of BAKA, where security is not compromised. Extensive experiments to validate the performance of BAKA are performed, and the results demonstrate the security efficacy proposed.

With the vigorous development of the Internet of Things (IoT), mobile users need to access data from other domains in edge computing. To achieve secure data sharing, mobile users first need to be authenticated by servers from different domains and then negotiate session keys among them. However, traditional schemes cannot solve cross-domain identity authentication and key agreement problems well due to the limited computational resources of IoT devices. In this work, we propose a Blockchain-based Cross-domain Authentication scheme for Edge computing, namely BCAE. First, to achieve secure identity verification, we design a novel cross-domain mutual identity authentication algorithm based on digital certificates and digital signatures. Next, to improve efficiency, we utilize the blockchain to share information among different domains to reduce the computation overhead. To realize quick key agreement, we apply the elliptic curve cryptography technique to design a lightweight key agreement algorithm and obtain secure session keys. Extensive experiments conducted on an actual smart healthcare issue to validate the performance of BCAE and formal security analysis confirmed the potential of the proposed work.

The development of the Internet of Things (IoT), Big Data, and deep learning technologies has brought convenience to people’s lives. As personal privacy data protection laws and regulations tighten, the cost of acquiring high-quality annotated data from vast IoT datasets has significantly increased, resulting in prevalent issues such as data acquisition challenges and label noise in training data. In this work, we focus on the demand for privacy protection and trustworthy sharing of IoT data, and propose a method for addressing data bias in IoT through federated learning and blockchain by utilizing the theory of local intrinsic dimension (LID), incorporating committee consensus to achieve noise label identification and correction at the data level, reducing information loss in the training data. Additionally, it performs screening of low-quality local model updates at the model level, leveraging blockchain technology that addresses the single point of failure issues in traditional federated learning, ensuring the performance and security of the federated learning models. Analysis, proof of convergence, and experimentations on the proposed framework demonstrate good security and robustness in noisy environments, effectively addressing data bias in intelligent IoT settings. In scenarios with a noise level of 0.3, 0.6, and 0.9, the average model accuracy improved respectively by 7.75%, 7.30%, and 14.04% compared to FedAvg. Similarly, when compared to FedCorr, the average improvement in model accuracy is 5.19%, 3.63%, and 8.74% respectively. Moreover, the training time remains within an acceptable range for all cases.

Abstract Monocular depth estimation (MDE) has made great progress with the development of convolutional neural networks (CNNs). However, these approaches suffer from essential shortsightedness due to the utilization of insufficient feature-based reasoning. To this end, we propose an effective parallel CNNs and Transformer model for MDE via dual attention (PCTDepth). Specifically, we use two stream backbones to extract features, where ResNet and Swin Transformer are utilized to obtain local detail features and global long-range dependencies, respectively. Furthermore, a hierarchical fusion module (HFM) is designed to actively exchange beneficial information for the complementation of each representation during the intermediate fusion. Finally, a dual attention module is incorporated for each fused feature in the decoder stage to improve the accuracy of the model by enhancing inter-channel correlations and focusing on relevant spatial locations. Comprehensive experiments on the KITTI dataset demonstrate that the proposed model consistently outperforms the other state-of-the-art methods.

Deep-Learning-as-a-service (DLaaS) has received increasing attention due to its novelty as a diagram for deploying deep learning techniques. However, DLaaS faces performance and security issues that urgently need to be addressed. Given the limited computation resources and concern of benefits, Quality-of-Service (QoS) metrics should be revised to optimize the performance and reliability of distributed DLaaS systems. New users and services dynamically and continuously join and leave such a system, resulting in cold start issues, and additionally, the increasing demand for robust network connections requires the model to evaluate the uncertainty. To address such performance problems, we propose in this article a deep learning-based model called embedding enhanced probability neural network, in which information is extracted from inside the graph structure and then estimated the mean and variance values for the prediction distribution. The adversarial attack is a severe threat to model security under DLaaS. Due to such, the service recommender system's vulnerability is tackled, and adversarial training with uncertainty-aware loss to protect the model in noisy and adversarial environments is investigated and proposed. Extensive experiments on a large-scale real-world QoS dataset are conducted, and comprehensive analysis verifies the robustness and effectiveness of the proposed model.

Drug target interaction prediction is a crucial stage in drug discovery. However, brute-force search over a compound database is financially infeasible. We have witnessed the increasing measured drug-target interactions records in recent years, and the rich drug/protein-related information allows the usage of graph machine learning. Despite the advances in deep learning-enabled drug-target interaction, there are still open challenges: (1) rich and complex relationship between drugs and proteins can be explored; (2) the intermediate node is not calibrated in the heterogeneous graph. To tackle with above issues, this paper proposed a framework named DSG-DTI. Specifically, DSG-DTI has the heterogeneous graph autoencoder and heterogeneous attention network-based Matrix Completion. Our framework ensures that the known types of nodes (e.g., drug, target, side effects, diseases) are precisely embedded into high-dimensional space with our pretraining skills. Also, the attention-based heterogeneous graph-based matrix completion achieves highly competitive results via effective long-range dependencies extraction. We verify our model on two public benchmarks. The result of two publicly available benchmark application programs show that the proposed scheme effectively predicts drug-target interactions and can generalize to newly registered drugs and targets with slight performance degradation, outperforming the best accuracy compared with other baselines.

Model prediction based on machine learning is provided as a service in cloud environments, but how to verify that the model prediction service is entirely conducted becomes a critical challenge. Although zero-knowledge proof techniques potentially solve the integrity verification problem when applied to the prediction integrity of massive privacy-preserving Convolutional Neural Networks (CNNs), the significant proof burden results in low practicality. In this research, we present psvCNN (parallel splitting zero-knowledge technique for integrity verification). The psvCNN scheme effectively improves the utilization of computational resources in CNN prediction integrity, proving by an independent splitting design. Through a convolutional kernel-based model splitting design and an underlying zero-knowledge succinct non-interactive knowledge argument, our psvCNN develops parallelizable zero-knowledge proof circuits for CNN prediction. Furthermore, psvCNN presents an updated Freivalds algorithm for a faster integrity verification process. Experiments show that psvCNN is practical and efficient in terms of proof time and storage, generating a prediction integrity proof with a proof size of 1.2MB in 7.65s for the structurally complicated CNN model VGG16. psvCNN is 3765 times faster than the latest zk-SNARK-based non-interactive method vCNN, and 12 times faster than the latest sumcheck-based interactive technique zkCNN in terms of proving time.

Blockchain technology, with its unique decentralized and tamper-resistant features, is being utilized to address the issue of information silos in traditional electronic healthcare. However, as healthcare data sources become increasingly complex and numerous, the limited scalability and transaction throughput of traditional blockchains result in challenges such as slow processing efficiency and vulnerability to attacks in modern healthcare blockchain systems. To address these issues, we propose a Dynamic Security Resource Configuration scheme based on Medical Consortium Blockchain (MC-DSC). This scheme allows for dynamic blockchain configuration based on the varying urgency levels of data, enhancing data processing efficiency. It ensures the security of the data processing process through identity control and data encryption methods. Experimental results demonstrate that, compared to existing blockchain configuration algorithms (SsHealth and Medge-Chain), the proposed scheme achieves approximately a 15% performance improvement by dynamically configuring the blockchain for three data types (secure, urgent, and normal). Additionally, the security module accounts for only 7% of the total time overhead, efficiently safeguarding the security of healthcare data while effectively handling data with different urgency levels.

The visual information processing technology based on deep learning can play many important yet assistant roles for unmanned aerial vehicles (UAV) navigation in complex environments. Traditional centralized architectures usually rely on a cloud server to perform model inference tasks, which can lead to long communication latency. Using transfer learning to unload deep neural networks to the edge-fog collaborative networks has become a new paradigm for dealing with the conflicts between computing resources and communication latency. However, ensuring the security of edge-fog collaborative networks entity remains challenging. For such, we propose an anonymous authentication and group key agreement scheme for the UAV-enabled edge-fog collaborative networks, consisting of the UAV authentication protocol and the collaborative networks authentication protocol. Utilizing the AVISPA assessment tool and security analysis, the security requirements and functional features of the proposed scheme are demonstrated. From the performance results of the proposed scheme, we show that it is superior to existing authentication schemes and promising.

The payment channel network aims to solve the problems of long payment confirmation time and limited throughput in cryptocurrencies through off-chain payments. Hash Time-Lock Contract (HTLC) is an off-chain payment protocol that Lightning Network (LN) adopted. Unfortunately, when performing high-valued payments off-chain, due to the impact of payment channel capacity, it is often necessary to split a single payment, which increases the transaction fees and time. Therefore, we propose LightPay, an atomic off-chain multi-path payment protocol based on adapter signature and discrete logarithm problem. Among different conditions encoded in the multi-path contract, the multi-path transmission of a single high-valued payment can be realized under the premise of the unlinkability of partial payments. We construct an ideal functionality in the Universal Composability framework and demonstrate that LightPay UC-realizes it, thereby providing proof of its security and privacy. Experimental results indicate that the payment success rate of LightPay can be increased by 11.08% in 0.0025 BTC payments compared with the single-path payment protocol Multihop HTLC in LN. Additionally, compared with the multi-path payment protocol CryptoMaze, the communication overhead required by LightPay is reduced to about 55.6% on average in the simulated network. Overall, LightPay has advantages regarding payment success rate and overhead.

Traffic prediction is crucial for intelligent transportation systems, assisting in making travel decisions, minimizing traffic congestion, and improving traffic operation efficiency. Although effective, existing centralized traffic prediction methods have privacy leakage risks. Federated learning-based traffic prediction methods keep raw data local and train the global model in a distributed way, thus preserving data privacy. Nevertheless, the spatial correlations between local clients will be broken as data exchange between local clients is not allowed in federated learning, leading to missing spatial information and inferior prediction accuracy. To this end, we propose a federated graph neural network with spatial information completion (FedGCN) for privacy-preserving traffic prediction by adopting a federated learning scheme to protect confidentiality and presenting a mending graph convolutional neural network to mend the missing spatial information during capturing spatial dependency to improve prediction accuracy. To complete the missing spatial information efficiently and capture the client-specific spatial pattern, we design a personalized training scheme for the mending graph neural network, reducing communication overhead. The experiments on four public traffic datasets demonstrate that the proposed model outperforms the best baseline with a ratio of 3.82%, 1.82%, 2.13%, and 1.49% in terms of absolute mean error while preserving privacy.

Network operation and maintenance rely heavily on network traffic monitoring. Due to the measurement overhead reduction, lack of measurement infrastructure, and unexpected transmission error, network traffic monitoring systems suffer from incomplete observed data and high data sparsity problems. Recent studies model missing data recovery as a tensor completion task and show good performance. Although promising, the current tensor completion models adopted in network traffic data recovery lack an effective and efficient retraining scheme to adapt to newly arrived data while retaining historical information. To solve the problem, we propose LightNestle, a novel sequential tensor completion scheme based on meta-learning, which designs (1) an expressive neural network to transfer spatial knowledge from previous embeddings to current embeddings; (2) an attention-based module to transfer temporal patterns into current embeddings in linear complexity; and (3) meta-learning-based algorithms to iteratively recover missing data and update transfer modules to catch up with learned knowledge. We conduct extensive experiments on two real-world network traffic datasets to assess our performance. Results show that our proposed methods achieve both fast retraining and high recovery accuracy.

Summary Array‐unit dual‐usage register is a kind of register resource that can be read or written as a whole or individually. It is mainly configured in processors with SIMD processing units and provides register‐level speed data transfer between the scalar and vector processing units. To improve the efficiency of algorithms by using an array‐unit dual‐usage register, we investigate in this article the problem of adapting register allocation to code containing array‐unit dual‐usage register names. We propose a corresponding global register allocation method by combining the allocation of regular registers with array‐unit dual‐usage register, ensuring that the names of array‐unit dual‐usage register can be used in the input code of register allocation. Moreover, we present the processing framework of this method and the specific algorithms of some related vital aspects and demonstrate the working principles of the algorithms by an example. Experimental studies were conducted on a platform based on the FT‐M7002 DSP core, and showing that our register allocation method can effectively handle codes containing array‐unit dual‐usage register names and support relevant application algorithms to improve their data transfer scheme. For some typical algorithms with input matrix, substantial performance improvements of twofold or higher are achieved.

With the expansion of scale, the Internet of Things (IoT) suffers more and more security threats, and vulnerability and sensitivity to attacks are also increasing. As a distributed and secure network architecture, Blockchain is suitable for protecting the security and privacy of the IoT. In this article, we propose a secure smart blockchain IoT architecture based on Graph Neural Networks (GNN) named GTxChain, using a distributed intelligent prophecy machine to obtain off-chain data and construct the transaction data structure of the blockchain through the blockchain-directed acyclic graph (DAG). In the off-chain transaction and off-chain storage part, we use the lightning network, improved IPFS and GNN to obtain transaction information and continuously update the blockchain network and blockchain for transaction verification and other operations. GTxChain employs an IPFS storage architecture to enhance user privacy and reduce data processing time. Compared to other blockchain architectures, it improves by 10.51% and has better efficiency and stability in terms of Merkle-proof time overhead. Experimental results show that the GTxChain architecture can effectively ensure the IoT’s trustworthiness, security, and privacy (TSP).

Location-based services have become prevalent and the risk of location privacy leakage increases. Most existing schemes use third-party-based or third-party-free system architectures; the former suffers from a single point of failure (SPOF) and the latter experiences a heavy load on user terminal equipment and higher communication costs. Ensuring location privacy while lowering system overhead becomes a challenge. Many existing schemes fail to leverage the responses from an LBS server; such responses can be cached to answer subsequent queries. As a result, providing users with a relatively comprehensive level of location privacy protection is troublesome. In this article, we propose a caching-based dual${K}$-anonymous (CDKA) location privacy-preserving scheme in edge computing environments. Our scheme employs an edge server to intercedes between a user and LBS the server. We reduce the load on the user device by applying multilevel caching and to protect location privacy through dual anonymity. To ensure the location privacy in our construction, we set mobile clients and edge servers as anonymous. We use caching to lower the communication overhead and enforce the location privacy. The security analysis of our scheme supports its robustness against the edge server and the LBS server privacy offenses. We rely on the computation time, the communication cost, and the cache hit ratio to evaluate our work against existing constructions. The results are twofold: our work possesses a better response rate down to 15–32.6 ms and exhibits lower communication cost requirements down to 6.2–38.9 kB compared to existing works. Our scheme witnesses a higher cache hit ratio of up to 13.6% and 39.1% compared to the literature.

Sparse sensor placement strategies are applied to reconstruct a region’s full-state data conditioned to a limited number of sensors; particularly, crucial to ocean monitoring systems. In maritime systems, existing sparse sensor placement methods mainly consider the reconstruction error of data or rely on specific requirements. Considering how sensors acquire essential information for monitoring systems, the utilization of entropy from information theory becomes quite interesting. In this article, we show that entropy measurements on different quantities of information are sensitive to indicate the border areas, thus requiring a balance between the number of sensors needed and the amount of information collected by them in coastal areas. Due to such, we propose: 1) a novel sparse sensor placement strategy based on entropy, where the entropy measurements in temporal dimension are utilized for sample selection, so portions of samples selected are utilized for training data, significantly improving the training efficiency without sacrificing accuracy of subsequent data reconstruction. In the proposed strategy, 2) we use orthogonal triangle decomposition from linear algebra where a low-cost sensor is employed as pivot and in terms of spatial dimension, the entropy of each location is adopted as entropy weight to reconstruct full state data. Additionally, 3) the strategy employs a greedy algorithm of weighted column pivoting for the orthogonal triangle decomposition, which is designed to suit yet effectively seek additional information and minimal reconstruction error in each iteration processing step. Experimental results using sea surface temperature (SST) data show that the proposed strategy outperforms existing methods, acquiring more information, ensuring higher efficiency, and reducing costs while minimizing reconstruction errors.

Most devices in the Internet of Things (IoT) work on unsafe networks and are constrained by limited computing, power, and storage resources. Since the existing centralized signature schemes cannot address the challenges to security and efficiency in IoT identification, this article proposes IdenMultiSig, a decentralized multi-signature protocol that combines identity-based signature (IBS) with Schnorr scheme under discrete logarithms on elliptic curves. First, to solve the problem of offline or faulty devices under unstable networks, we introduce a novel improvement of the existing Schnorr scheme by introducing a threshold Merkle tree for the verification with only$m$valid signatures among$n$participants ($m$–$n$tree), while hiding the real identity to protect the data security and privacy of IoT nodes. Furthermore, to prevent dishonest or malicious behavior of the private key generator (PKG), a consortium blockchain is innovatively applied to replace the traditional PKG as a decentralized and trusted private key issuer. Finally, the proposed scheme is proven to be unforgeable against forgery signature attacks in the random oracle model (ROM) under the elliptic curve discrete logarithm (ECDL) assumption. Theoretical analysis and experimental results show that our scheme matches or outperforms existing research studies in privacy protection, offline device support, decentralized PKG, and provable security.

As part of Big Data trends, the ubiquitous use of the Internet of Things (IoT) in the industrial environment has generated a significant amount of network traffic. In this type of IoT industrial network where there is a large equipment heterogeneity, security is a fundamental issue; thus, it is very important to detect likely intrusion behaviors. Furthermore, since the proportion of labeled data records is small in the IoT environment, it is challenging to detect various attacks and intrusions accurately. This investigation builds a semisupervised ladder network model for intrusion detection in the Industrial IoT. This model considers the manifold distribution of high-dimensional data and incorporates a manifold regularization constraint in the decoder of the ladder network. Meanwhile, the feature propagation between layers is strengthened by adding more cross-layer connections in this model. On this basis, a random attention-based data fusion approach is proposed to generate global features for intrusion detection. The experiments on the CIC-IDS2018 dataset show that the proposed approach can recognize the intrusion with less false alarm rate, while model training is time efficient.

With the development of edge computing and 5G, the demand for resource-limited devices to execute computation-intensive tasks can be effectively alleviated. The research on computation offloading lays an essential foundation for realizing mobile edge computing, and deep reinforcement learning (DRL) has become an emerging technique to address the computation offloading problem. This article utilizes a DRL-based algorithm to design a decentralized computation offloading framework aimed at minimizing the computational cost. We employ a multiuser system model with a single-edge server suitable for industrial scenarios. Then, we propose a dual-critic deep deterministic policy gradient (DC-DDPG) algorithm based on the deep deterministic policy gradient (DDPG) algorithm to tackle computation offloading and resource allocation problems for all users. DC-DDPG adopts two critic nets in both the primary and target nets to fit the action value of two different optimization objectives, which expedites the convergence during the training process and reduces the computational cost of the edge computation system during operation. Compared with other DRL methods, such as deep Q-network and DDPG, numerical results demonstrate that the proposed DC-DDPG algorithm has a faster convergence speed and performs significantly better than other DRL-based algorithms in terms of system computational cost in computing-intensive tasks, which makes it more suitable for industrial intelligent manufacturing scenarios with large data volume.

Traffic flow forecasting is indispensable in today’s society and regarded as a key problem for Intelligent Transportation Systems (ITS), as emergency delays in vehicles can cause serious traffic security accidents. However, the complex dynamic spatial-temporal dependency and correlation between different locations on the road make it a challenging task for security in transportation. To date, most existing forecasting frames make use of graph convolution to model the dynamic spatial-temporal correlation of vehicle transportation data, ignoring semantic similarity between nodes and thus, resulting in accuracy degradation. In addition, traffic data does not strictly follow periodicity and hard to be captured. To solve the aforementioned challenging issues, we propose in this article CRFAST-GCN, a multi-branch spatial-temporal attention graph convolution network. First, we capture the multi-scale (e.g., hour, day, and week) long- short-term dependencies through three identical branches, then introduce conditional random field (CRF) enhanced graph convolution network to capture the semantic similarity globally, so then we exploit the attention mechanism to captures the periodicity. For model evaluation using two real-world datasets, performance analysis shows that the proposed CRFAST-GCN successfully handles the complex spatial-temporal dynamics effectively and achieves improvement over the baselines at 50% (maximum), outperforming other advanced existing methods.

With the prevalence of Intelligent Transportation Systems (ITS), massive sensors are deployed on roadside, vehicles, and infrastructures. One key challenge is imputing several different types of missing entries in spatial-temporal traffic data to meet the high-quality demand of data science applied in Cooperative-ITS (C-ITS) since accurate data recovery is critical to many downstream tasks in ITSs, such as traffic monitoring and decision making. For such, it is proposed in this article solutions to three kinds of data recovery tasks in a unified model via spatial-temporal aware Graph Neural Networks (GNNs), named Spatial-Temporal Aware Data Recovery Network (STAR), enabling a real-time and inductive inference. A residual gated temporal convolution network is designed to permit the proposed model to learn the temporal pattern from long sequences with masks and an adaptive memory-based attention model for utilizing implicit spatial correlation. To further exploit the generalization power of GNNs, a sampling-based method is adopted to train the proposed model to be robust and inductive for online servicing. Extensive numerical experiments on two real-world spatial-temporal traffic datasets are performed, and results show that the proposed STAR model consistently outperforms other baselines at 1.5-2.5 times on all kinds of imputation tasks. Moreover, STAR can support recovery data for 2 to 5 hours, with its performance barely unchanged, and has comparable performance in transfer learning and time-series forecast. Experimental results demonstrate that STAR provides adequate performance and rich features for multiple data recovery tasks under the C-ITS scenario.

Fake review detection has the characteristics of huge stream data processing scale, unlimited data increment, dynamic change, and so on. However, the existing fake review detection methods mainly target limited and static review data. In addition, deceptive fake reviews have always been a difficult point in fake review detection due to their hidden and diverse characteristics. To solve the above problems, this article proposes a fake review detection model based on sentiment intensity and PU learning (SIPUL), which can continuously learn the prediction model from the constantly arriving streaming data. First, when the streaming data arrive, the sentiment intensity is introduced to divide the reviews into different subsets (i.e., strong sentiment set and weak sentiment set). Then, the initial positive and negative samples are extracted from the subset using the marking mechanism of selection completely at random (SCAR) and Spy technology. Second, building a semi-supervised positive-unlabeled (PU) learning detector based on the initial sample to detect fake reviews in the data stream iteratively. According to the detection results, the data of initial samples and the PU learning detector are continuously updated. Finally, the old data are continually deleted according to the historical record points, so that the training sample data are within a manageable size and prevent overfitting. Experimental results show that the model can effectively detect fake reviews, especially deceptive reviews.

In Mobile Edge Computing (MEC) environment, service deployment for IoT application is a key issue that needs to be solved. Considering the knowledge of mobile users’ service requests and edge server’s processing capacity, the problem of microservice deployment in MEC environment is modelled as a non-linear optimization problem. An adaptive dynamic deployment optimization method called Adapt-SD has been proposed, which is based on Adam and weighted round-robin scheduling algorithm to solve this microservice deployment problem. In Adapt-SD, considering the hardware resource-constrained MEC environment, different numbers of microservice instances are deployed on different edge servers, and then microservice instances are invoked to achieve the minimum resource consumption cost while meeting user’s service access delay constraints. At the same time, Adapt-SD also ensures the work balance of edge servers. In this paper, real datasets from EUA in Australia and some synthetic datasets are utilized to measure the performance of Adapt-SD, which is compared with the existing microservice deployment algorithms. Experimental results show that Adapt-SD is superior to other representative deployment algorithms.

With the advances and innovations in digital technologies, blockchain has empowered advancements in communications and networking, promising to build trust and establish secure decentralized communications networks. Unfortunately, current personal data privacy protection schemes still suffer from explicit storage, lack of data ownership and implementation of fine-grained access control by users, and lack of transparency and auditability of data. In this article, we propose a personal data privacy protection scheme based on consortium blockchain that stores original data encrypted with an improved Paillier homomorphic encryption mechanism, namely PDPChain, where users realize fine-grained access control based on ciphertext policy attribute-based encryption (CP-ABE) on blockchain. In this scheme, consortium blockchain combines distributed private clusters to store the encrypted data, improving data transmission efficiency, and guaranteeing user privacy and security through off-chain storage and on-chain transmission synergy. In addition, it is more lightweight encryption and demarcation, ultimately protecting personal data privacy and providing a secure and trusted way to obtain information for data mining. For the performance testing, data in the form of files are used as an example, and the scheme is designed and simulated on Hyperledger Fabric and InterPlanetary File System. Experimental results show that the improved Paillier encryption mechanism reduces the overall encryption and decryption elapsed time by 25% and encryption elapsed time by 48%. Furthermore, the proposed CP-ABE access control method is adaptive to storing and sharing a massive amount of data. With the increase in the number of access control policies, the overall time-consuming of the scheme does not increase, and the time-consuming of decryption can also be stabilized at about 2 s.

Multi-modality complementary information brings new impetus and innovation to saliency object detection (SOD). However, most existing RGB-D SOD methods either indiscriminately handle RGB features and depth features or only take depth features as additional information of RGB subnet-work, ignoring the different roles of two modalities for SOD tasks. To tackle this issue, we propose a novel multi-modality diversity fusion network with SwinTransformer (M2DFNet) for RGB-D SOD from the perspective of the different status of multi-modality, which adequately explores the roles of RGB and depth modalities. To this end, a triple-diversity supervision mechanism (TDSM) and a diversity fusion module (DFM) are designed to parse the function of different modalities. Besides, we designed a dense decoder (DSD) to integrate multi-scale features and transfer gain information from top to bottom, which can improve the performance of SOD. Extensive experiments on five benchmark datasets demonstrate that the proposed M2DFNet outperforms 17 other state-of-the-art (SOTA) RGB-D SOD methods.

Multi-scale context is crucial for the accurate salient object detection (SOD) in the real-world scenes. Although current contextual information-based SOD methods have achieved great progress, they may fail to generate precise saliency maps due to their seldom considering the correlation of different scale context during the extraction process. To address these issues, we propose an Efficient Multi-Scale Context Exploration Network (EMCENet) for SOD. Specifically, a progressive multi-scale context extraction (PMCE) module is designed to progressively capture strongly correlated multi-scale context by using multi-receptive-field convolution operations. Afterwards, a hierarchical feature hybrid interaction (HFHI) module is introduced to generate powerful feature representations by adaptively aggregating multi-level features in a hybrid interaction strategy. Extensive experimental results on six public datasets demonstrate that the proposed EMCENet method without any post-processing performs favorably against 13 state-of-the-art SOD methods.

The wide deployment of Internet of Things (IoT) devices enables the controller to continuously collect massive volume data in automation systems, and makes it possible to make intelligent decisions based on machine learning techniques. In fact, data-driven intelligent automation systems have been common in the industrial community. Nevertheless, how to effectively store the collected stream data and ensure their integrity is still challenging. To this end, the notion of verifiable data streaming (VDS) protocol, which enables a client to outsource the stream data to an untrusted server in a verifiable manner, was introduced. However, we argue that existing VDS protocols based on the chameleon authentication tree (CAT) are inefficient in the data update, since the whole CAT must be updated accordingly to avoid acute exposure of chameleon hashing. Thus, they are infeasible for intelligent automation systems that need to frequently update data. In this article, we first introduce a new primitive called double-trapdoor chameleon hash tree (DCHT) based on the double-trapdoor chameleon hash families, where each leaf of DCHT is calculated and fixed by using a double-trapdoor chameleon hash family, making the entire DCHT always unchanged. Furthermore, we propose a novel VDS protocol based on the DCHT. Due to the distinctive properties of the underlying DCHT, the proposed VDS protocol has a constant update cost and more efficient than previous VDS protocols based on CAT. Besides, we prove that the proposed VDS protocol is secure in the standard model.

Satellite communication contributes tremendously to the Industrial Internet of Things (IIoT) with telecommunication efficiency and data accessibility at global-scale coverage. However, realizing proactive defense for satellite communication remains a challenge. In order to address such an issue, we first explore state-of-the-art proactive defense methods and, following next, a step forward on proposing an end hopping scheme based on fixed hopping timeslot and strict time synchronization strategy by utilizing moving target defense (MTD). In addition, we worked on a Proof of Concept (PoC) to evaluate the scheme’s theoretical protection performance for Distributed Denial of Service (DDoS). Experimental evaluation and analysis of the proposed scheme show that it is efficient and secure, as seen when the attack rate is 100 times/s, the response time of the hopping state is 69.98% shorter than that of the normal state, and when the attack rate is 1000 times/s, the response time of the hopping state is 90.15% shorter.

Blockchain technology is rapidly changing the transaction behavior and efficiency of businesses in recent years. Data privacy and system reliability are critical issues that is highly required to be addressed in Blockchain environments. However, anomaly intrusion poses a significant threat to a Blockchain, and therefore, it is proposed in this article a collaborative clustering-characteristic-based data fusion approach for intrusion detection in a Blockchain-based system, where a mathematical model of data fusion is designed and an AI model is used to train and analyze data clusters in Blockchain networks. The abnormal characteristics in a Blockchain data set are identified, a weighted combination is carried out, and the weighted coefficients among several nodes are obtained after multiple rounds of mutual competition among clustering nodes. When the weighted coefficient and a similarity matching relationship follow a standard pattern, an abnormal intrusion behavior is accurately and collaboratively detected. Experimental results show that the proposed algorithm has high recognition accuracy and promising performance in the real-time detection of attacks in a Blockchain.

Indoor localization has become an essential demand driven by indoor location-based services (ILBSs) for mobile users. With the rising of Internet of Things (IoT), heterogeneous smartphones and wearables have become ubiquitous. However, the ILBSs for heterogeneous IoT devices confront significant challenges, such as received signal strength (RSS) variances caused by hardware heterogeneity, multipath reflections from complex environments, and localization time restricted by computation resources. This article proposes EdgeLoc, a robust and real-time indoor localization system toward heterogeneous IoT devices to solve the above challenges. In particular, the RSS fingerprinting data of Wi-Fi is employed for localization and tackling the heterogeneity of IoT devices in twofold. First, feature-level and signal-level solutions are presented to address the random RSS variances. At the feature level, this work proposes a novel capsule neural network model to efficiently extract incremental features from RSS fingerprinting data. At the signal level, a multistep dataflow is further devised to process RSS fingerprints into image-like data, which utilizes the feature matrix to reduce absolute sensing errors introduced by hardware heterogeneity. Second, an edge-IoT framework is designed to utilize the edge server to train the deep learning model and further supports real-time localization for heterogeneous IoT devices. Extensive field experiments with over 33 600 data points are conducted to validate the effectiveness of EdgeLoc with a large-scale Wi-Fi fingerprint data set. The results show that EdgeLoc outperforms the state-of-the-art SAE-CNN method in localization accuracy by up to 14.4%, with an average error of 0.68 m and an average positioning time of 2.05 ms.

RGB-D Salient object detection (SOD) is a pixel-level dense prediction task, which can highlight the prominent object in the scene. Recently, Convolution Neural Network (CNN) is widely applied in SOD to generate multi-level features, which are complementary to each other. However, most methods ignore the unique characteristics of multi-level features (high-level and low-level features). Given the effective employment of multi-level features, we propose a novel multi-modality hierarchy-aware decision network (HDNet) by embedding a Swin Transformer as an encoder. The proposed HDNet contains three primary designs: (1) a Swin Transformer encoder is employed instead of a CNN to learn long-range dependencies; (2) a hierarchy-aware feature decision mechanism (HFDM) is proposed to exploit effective local detail cues of low-level features and global semantic information of high-level features, which consists of two sub-modules, namely low-hierarchy edge module (LEM) and high-hierarchy region module (HRM); (3) a decision-based fusion module (DFM) is designed to fuse RGB and depth features under the attribute of multi-level features generated from HFDM. Experiments on five public benchmarks verify that our framework has better performance than the other 18 state-of-the-art algorithms.

Considered as a promising fine-grained access control mechanism for data sharing without a centralized trusted third-party, the access policy in a plaintext form may reveal sensitive information in the traditional CP-ABE method. To address this issue, a hidden policy needs to be applied to the CP-ABE scheme, as the identity of a user cannot be accurately confirmed when the decryption key is leaked, so the malicious user is traced and revoked as demanded. In this article, a CP-ABE scheme that realizes revocation, white-box traceability, and the application of hidden policy is proposed, and such ciphertext is composed of two parts. One is related to the access policy encrypted by the attribute value, and only the attribute name is evident in the access policy. Another is related to the revocation information and updated when revoking, where the revocation information is generated by the binary tree related to users. The leaf node value of a binary tree in the decryption key is used to trace the malicious user. From experimental results, it is shown that the proposed scheme is proven to be IND-CPA secure under the chosen plaintext attacks and selective access policy based on the decisional q-BDHE assumption in the standard model, efficient, and promising.

Internet of Things (IoT) devices are widely considered in smart cities, intelligent medicine, and intelligent transportation, among other fields that facilitate people's lives, producing a large amount of private data. However, due to the mobility, limited performance, and distributed deployment of IoT, traditional access control methods cannot support the security of private data's access control process in current IoT environments. To address such problems, this article proposes an auditable access control model, based on an attribute-based access control model, and manages the access control policy for private data through the request record, the response record, and the access record stored in the blockchain network. Additionally, a Blockchain-based auditable access control system is also proposed based on the auditable access control model, ensuring private data security in IoT environments and realizing effective management and auditable access to these data. Experimental results show that the proposed system can maintain high throughput while ensuring private data security for real application scenarios in IoT environments.

In Industry 5.0, the massive number of Internet of Things devices have increasing demands for group communication with a high communication efficiency and low energy consumption. However, group communication meets continuously increasing security risk challenges. Existing authentication and group key agreement schemes have encountered many problems, such as lack of anonymity and untraceability. In this article, we propose an anonymous authentication and dynamic group key agreement scheme based on the Blockchain and token mechanism, where each group member can apply for a time-sensitive token during the first authentication and only needs to check the validity of the token in the subsequent authentication, reducing the computational and transmission costs considerably. The verification on the security of the proposed scheme is tackled through mathematical analysis and validated using ProVerif, and comparisons with existing schemes demonstrate that the proposed scheme reduces the security risks and each group member’s energy consumption.

The Industrial Internet of Things ( IIoT ) is designed to refine and optimize the process controls, thereby leveraging improvements in economic benefits, such as efficiency and productivity. However, the Radio Frequency Identification ( RFID ) technology in an IIoT environment has problems such as low security and high cost. To overcome such issues, a mutual authentication scheme that is suitable for RFID systems, wherein techniques in Deep Learning ( DL ) are incorporated onto the Arbiter Physical Unclonable Function ( APUF ) for the secured access authentication of the IC circuits on the IoT, is proposed. The design applies the APUF-MPUF mutual authentication structure obtained by DL to generate essential real-time authentication information, thereby taking advantage of the feature that the tag in the PUF circuit structure does not need to store any essential information and resolving the problem of key storage. The proposed scheme also uses a bitwise comparison method, which hides the PUF response information and effectively reduces the resource overhead of the system during the verification process, to verify the correctness of the two strings. Security analysis demonstrates that the proposed scheme has high robustness and security against different conventional attack methods, and the storage and communication costs are 95.7% and 42.0% lower than the existing schemes, respectively.

Representation learning techniques have been frequently applied in multimedia content analysis and retrieval. In this study, an efficient multimedia data clustering method is presented, which consists of two independent algorithms. First, we propose a new representation framework by incorporating sparse coding and manifold regularisation in an optimisation objective function, the cluster indicator matrix is estimated by introducing ℓ1 sparsity norm coarsely. Second, we refine the estimated cluster indicator matrix by performing spectral rotation such that an optimal assignment for clustering can be learned. Compared with existing methods, we have the following merits: our method takes into account the global matrix reconstruction information and locality manifold information simultaneously. Therefore, global and locality information both are respected. Additionally, theoretical justification about the novel representation method is presented in this study. Comprehensive experiments demonstrate the effectiveness and efficiency of our method in comparison with the state-of-the-art clustering methods on six real-world image datasets.

With the exponential increase in the number of transistors under fast-paced technology progress, the soft error induced reliability issue is becoming even more challenging in heterogeneous multicore processor design. As there are significant opportunities to mitigate the soft error impacts through heterogeneous multicore scheduling, we show in this paper that the correlation among multiple applications exhibits important reliability characteristics, by defining a new metric to measure the system-level vulnerability factor of multiple applications and an approximate estimator to evaluate the metric fast and accurately for effective scheduling decisions. To approach these issues, we propose CASH, a Correlation-Aware Scheduling strategy to optimise heterogeneous multicore system reliability. Comprehensive simulation results demonstrate that the proposed approach is promising, achieving up to 21.4% reliability improvement with only 3.6% performance degradation when compared with performance-oriented scheduling policy.

It is challenging to build domain-specific emotional lexicon for film reviews, due to its unique characteristics, such as massive data, endless new login words, and others. To improve the accuracy of film reviews classification, this article proposes a method for extending emotional lexicon based on word distance and point mutual information. First, using the improved K-means++ algorithm to cluster and select seed words with obvious emotional tendencies. Next, the Distance of Word and Point Mutual Information (DW-PMI) algorithm is presented to determine the emotional polarity of emotional words in the domain of film reviews. Four types of vocabulary, including degree adverb, negation, emoticon and emotion dictionary in the film reviews domain are added to the basic emotion dictionary to extend the film reviews emotional lexicon. From the experimental results, the expanded emotional lexicon of the Chinese film reviews can improve the accuracy and preciseness of the film reviews emotion analysis.

Most of the existing outsourced encrypted data schemes are retrieved based on the query keyword entered by authorised users.However, with the increase of the data scale in the cloud storage system, the retrieval efficiency of existing solutions has not been significantly improved.In this paper, a multi-keyword ranked search scheme for ciphertext based on mapping set matching (MSMR) is proposed, where (1) The private cloud server matches the keyword numbering set corresponding to the document index vector and the keyword numbering set corresponding to the query vector and sends the document identifier of the matching keyword numbering to the public cloud server.The public cloud server filters the documents irrelevant to the query request according to the document identifier corresponding to the matching keyword numbering, which effectively reduces the time spent in calculating the correlation score, and (2) the document index vector and query vector are segmented before encrypting them out, reducing the time to construct such vectors.Theoretical analysis shows that the proposed scheme is secure in the known ciphertext model.Experimental results confirm that whenever the data scale grows, the improvement of MSMR retrieval efficiency is more significant.

Service-oriented architecture (SOA) provides a flexible IT infrastructure in order to deal with global competition. However, real-world services change their architectures and functions frequently. This work uses data integration, information synthesis, and knowledge fusion to analyze SOA evolution and complexity. A quantificational approach is provided to estimate the change effects on a service-oriented system. We established a cloud service community to provide rich cloud services and a service mashup platform that connects API service providers and service users. The platform user can invoke all API services on the platform without jumping to the third-party website. The platform users can filter and accept the services recommended by the platform, and also freely compose the services which are real-world services. Data integration, information synthesis, and knowledge fusion are applied to mashups that users constructed, helping users avoid complexity and unreliability that is our objective. We also showed that our method achieved significant quantitative results.

Data credibility plays a key role in facilitating evidence-based decision making in organizations and governments (e.g., policy making). One of the key data sources is the Internet of Things (IoT) devices and systems, say within a fog environment. However, the increasing complexity and interconnectivity of such IoT and fog environments can result in security vulnerabilities (e.g., due to implementation errors or flaws in the underpinning devices or systems), which can be exploited to compromise the credibility of the data. Therefore, in this article, we propose a secure Blockchain-based scheme to guarantee the credibility of nodes and data and ensure data transmission security in the fog environment. We then demonstrate the feasibility of the proposed scheme using experiments.

With the rapid advancement of video and image processing technologies in the Internet of Things, it is urgent to address the issues in real-time performance, clarity, and reliability of image recognition technology for a monitoring system in foggy weather conditions. In this work, a fast defogging image recognition algorithm is proposed based on bilateral hybrid filtering. First, the mathematical model based on bilateral hybrid filtering is established. The dark channel is used for filtering and denoising the defogging image. Next, a bilateral hybrid filtering method is proposed by using a combination of guided filtering and median filtering, as it can effectively improve the robustness and transmittance of defogging images. On this basis, the proposed algorithm dramatically decreases the computation complexity of defogging image recognition and reduces the image execution time. Experimental results show that the defogging effect and speed are promising, with the image recognition rate reaching to 98.8% after defogging.

On behalf of the Program Committee of the 18th IEEE International Conference on Embedded and Ubiquitous Computing (IEEE EUC 2020), we would like to welcome you to join the conference in Guangzhou, China, December 29, 2020 - January 1, 2021.

With the fast advancements of electronic chip technologies in the Internet of Things (IoT), it is urgent to address the copyright protection issue of intellectual property (IP) circuit resources of the electronic devices in IoT environments. In this article, a fast deep-reinforcement-learning (DRL)-based detection algorithm for virtual IP watermarks is proposed by combining the technologies of mapping function and DRL to preprocess the ownership information of the IP circuit resource. The deep$Q$-learning (DQN) algorithm is used to generate the watermarked positions adaptively, making the watermarked positions secure yet close to the original design, turning the watermarked positions secure. An artificial neural network (ANN) algorithm is utilized for training the position distance characteristic vectors of the IP circuit, in which the characteristic function of the virtual position for IP watermark is generated after training. In IP ownership verification, the DRL model can quickly locate the range of virtual watermark positions. With the characteristic values of the virtual positions in each lookup table (LUT) area and surrounding areas, the mapping position relationship can be calculated in a supervised manner in the neural network, as the algorithm realizes the fast location of the real ownership information in an IP circuit. The experimental results show that the proposed algorithm can effectively improve the speed of watermark detection as also reducing the resource overhead. Besides, it also achieves excellent performance in security.

The massive redundant data storage and communication in network 4.0 environments have issues of low integrity, high cost, and easy tampering. To address these issues, in this article, a secure data storage and recovery scheme in the blockchain-based network is proposed by improving the decentration, tampering-proof, real-time monitoring, and management of storage systems, as such design supports the dynamic storage, fast repair, and update of distributed data in the data storage system of industrial nodes. A local regenerative code technology is used to repair and store data between failed nodes while ensuring the privacy of user data. That is, as the data stored are found to be damaged, multiple local repair groups constructed by vector code can simultaneously yet efficiently repair multiple distributed data storage nodes. Based on the unique chain storage structure, such as data consensus mechanism and smart contract, the storage structure of blockchain distributed coding not only quickly repair the nearby local regenerative codes in the blockchain but also reduce the resource overhead in the data storage process of industrial nodes. Experimental results show that the proposed scheme improves the repair rate of multinode data by 9% and data storage rate increased by 8.6%, indicating to be promising with good security and real-time performance.

Industrial networks are complex and diverse. Among existing intrusion prevention systems available, several of them have problems such as low detection accuracy rate, high false positive (FP) rate, and low real-time performance for impersonation attacks. To address such issues, it is proposed in this article an industrial network intrusion detection algorithm based on multifeature data clustering optimization model, where the weighted distances and security coefficients of data are classified based on the priority threshold of data attribute feature for each node in the network, given that the data modules in the industrial network environment are diverse and easy to diagnose, restore, and rebuild. The proposed algorithm can effectively improve the detection rate and real-time performance of detecting abnormal behavior for the multifeature data in industrial networks. The novel features are twofold, to rapidly select a node with high-security coefficient as the cluster center, and match the multifeature data around the center into a cluster. Experimental results show that the proposed algorithm has good superiority in terms of detection rate and time compared to other algorithms. In the industrial network, the detection accuracy of abnormal data reaches 97.8%, and the FP of detection is decreased by 8.8%.

Blockchain technology offers an intelligent amalgamation of distributed ledger, Peer-to-Peer (P2P), cryptography, and smart contracts to enable trustworthy applications without any third parties. Existing blockchain systems have successfully either resolved the scalability issue by advancing the distributed consensus protocols from the control plane, or complemented the security issue by updating the block structure and encryption algorithms from the data plane. Yet, we argue that the underlying P2P network plane remains as an important but unaddressed barrier for accelerating the overall blockchain system performance, which can be discussed from how fast and reliable the network is. In order to improve the blockchain network performance about enabling fast and reliable broadcast, we establish a trust-enhanced blockchain P2P topology which takes transmission rate and transmission reliability into consideration. Transmission rate reflects blockchain network speed to disseminate transactions and blocks, and transmission reliability reveals whether transmission rate changes drastically on unreliable network connection. This paper presents BlockP2P-EP, a novel trust-enhanced blockchain topology to accelerate transmission rate and meanwhile retain transmission reliability. BlockP2P-EP first operates the geographical proximity sensing clustering, which leverages K-Means algorithm for gathering proximity peer nodes into clusters. It follows by the hierarchical topological structure that ensures strong connectivity and small diameter based on node attribute classification. Then we propose establishing trust-enhanced network topology. On top of the trust-enhanced blockchain topology, BlockP2P-EP conducts the parallel spanning tree broadcast algorithm to enable fast data broadcast among nodes both intra- and inter- clusters. Finally, we adopt an effective node inactivation detection method to reduce network load. To verify the validity of BlockP2P-EP protocol, we carefully design and implement a blockchain network simulator. Evaluation results show that BlockP2P-EP can exhibit promising network performance in terms of transmission rate and transmission reliability compared to Bitcoin and Ethereum.

Quality of service (QoS) has been mostly applied to represent non-functional properties of Web services and differentiate those with the same functionality. How to accurately predict service QoS has become a key research topic. Researchers have employed neighborhood information into matrix factorization (MF) for service QoS prediction in recent years. However, they are restricted to traditional matrix factorization that may incur a couple of limitations. 1) Conventional MF for QoS prediction linearly combines the multiplication of the latent feature representation of users and services through inner product, failing to fully capture the implicit features of user and service. 2) Most of approaches integrate user or service neighborhood as heuristics into MF model, where either location context or historical invocation records are used to calculate similar users or services. Nevertheless, combining both of them together in a collaborative way is ignored for neighborhood selection that has yet to be properly explored. To deal with the challenges, we propose a novel approach for service QoS prediction called Neighborhood-integrated Deep Matrix Factorization (NDMF), which integrates user neighborhood selected by a collaborative way into an enhanced matrix factorization model via deep neural network (DNN). We implement a prototype system and conduct extensive experiments on public and real-world large Web service dataset with almost 2,000,000 service invocations called WS-DREAM which is widely used in service QoS prediction. The experimental results demonstrate that our proposed approach significantly outperforms state-of-the-art ones in terms of multiple evaluation metrics.

Summary Fog computing is a new computing paradigm that can provide flexible resources and services at the edge of network. It is an extension of cloud computing and usually cooperated with cloud computing. Therefore, end users, fog nodes, and cloud servers can form a three‐layer service model in practical application. In this model, they should have an agreement on a service contract, which contains every party's rights and obligations before the beginning of the service. However, due to lack of trust, it will suffer from some fairness problems during signing a service contract. Contract signing protocol allows two or more mutual distrust entities to sign a predefined digital contract in a fair and effective way. In this paper, we propose a fair three‐party contract signing protocol based on the primitive of blockchain, which can be applied to the scenario of fog computing. Our proposed construction allows the participants to sign a contract in a fair way without the involvement of an arbitrator. Moreover, the privacy of the contract content can be preserved on the public chain. Finally, we realize the proposed protocol through the private blockchain and provide the experimental simulation that analyzes the efficiency and effectiveness.

The I/O bottleneck has become an increasingly daunting challenge for big data analytics along with the explosive growth in data volume. Flash-based SSDs become promising to replace the hard disk drives. However, garbage collection (GC) operations in SSDs have a significant impact on the SSD performance, thus leading to performance variability in SSD-based RAIDs. To address this problem, we propose request redirection (RR) by exploiting the asymmetric read-write performance characteristics of SSDs and the hot-spare SSD in SSD-based RAIDs to alleviate the GC-induced performance variability. RR services the incoming read requests to the SSD currently in GC state by reconstructing the read data from other SSDs in the same stripe within SSD-based RAIDs. For the incoming write data to the SSD in the GC state, RR temporarily stores the write data on the hot-spare SSD and concurrently updates the corresponding parity in the SSD-based RAIDs. Extensive evaluations on the RR prototype show that the RR scheme significantly reduces the average response time and alleviates the performance variability, compared with the local GC and global GC schemes.

The previous blockchain data transmission techniques in industrial Internet of Things (IoT) have low security, high management cost of the trading center, and big difficulty in supervision. To address these issues, this paper proposes a secure FaBric blockchain-based data transmission technique for industrial IoT. This technique uses the blockchain-based dynamic secret sharing mechanism. A reliable trading center is realized using the power blockchain sharing model, which can also share power trading books. The power data consensus mechanism and dynamic linked storage are designed to realize the secure matching of the power data transmission. Experiments show that the optimized FaBric power data storage and transmission has high security and reliability. The proposed technique can improve the transmission rate and packet receiving rate by 12% and 13%, respectively. Moreover, the proposed technique has good superiority in sharing management and decentralization.

Recently, an increased amount of effort has been focused on optimizing the selection of routes for taxis, as part of the development of smart urban environments, and the increase of the accumulated trajectory data sets. One challenging issue is to match and recommend appropriate cruising routes to taxis, as most taxis cruise on streets aimlessly looking for passengers. Drivers encounter lots of difficulty in optimizing their cruise routes and hence increasing their incomes, and such inability not only decreases their profit but also increases the traffic load in urban cities. In this paper, the concept of urban traffic Coulomb's law is coined to model the relationship between taxis and passengers in urban cities, based on which a route recommendation scheme is proposed. Taxis and passengers are viewed as positive and negative charges. It first collects useful information such as the density of passengers and taxis from trajectories, then calculates the traffic forces for cruising taxis, based on which taxis are routed to optimal road segments to pick up desired passengers. Different from existing route recommendation methods, the relationship among taxis and passengers are fully taken into account in the proposed algorithm, e.g., the attractiveness between taxis and passengers, and the competition among taxis. Moreover, real-time dynamics and geodesic distances in road networks are also considered to make more accurate and effective route recommendations. Extensive experiments are conducted on the road network using the trajectories generated by approximately 5,000 taxis to verify the effectiveness, and evaluations demonstrate that the proposed method outperforms existing methods and can increase the drivers' income more than 8%.

Cloud-based storage is a service model for businesses and individual users that involves paid or free storage resources. This service model enables on-demand storage capacity and management to users anywhere via the Internet. Because most cloud storage is provided by third-party service providers, the trust required for the cloud storage providers and the shared multi-tenant environment present special challenges for data protection and access control. Attribute-based encryption (ABE) not only protects data secrecy, but also has ciphertexts or decryption keys associated with fine-grained access policies that are automatically enforced during the decryption process. This enforcement puts data access under control at each data item level. However, ABE schemes have practical limitations on dynamic user revocation. In this paper, we propose two generic user revocation systems for ABE with user privacy protection, user revocation via ciphertext re-encryption (UR-CRE) and user revocation via cloud storage providers (UR-CSP), which work with any type of ABE scheme to dynamically revoke users.

Over the last few years, a large number of Internet of Things (IoT) solutions have come to the IoT marketplace. Typically, each of these IoT solutions are designed to perform a single or minimal number of tasks (primary usage). We believe a significant amount of knowledge and insights are hidden in these data silos that can be used to improve our lives; such data include our behaviors, habits, preferences, life patterns, and resource consumption. To discover such knowledge, we need to acquire and analyze this data together in a large scale. To discover useful information and deriving conclusions toward supporting efficient and effective decision making, industrial IoT platform needs to support variety of different data analytics processes such as inspecting, cleaning, transforming, and modeling data, especially in big data context. IoT middleware platforms have been developed in both academic and industrial settings in order to facilitate IoT data management tasks including data analytics. However, engineering these general-purpose industrial-grade big data analytics platforms need to address many challenges. We have accepted six manuscripts out of 24 submissions for this special section (25% acceptance rate) after the strict peerreview processes. Each manuscript has been blindly reviewed by at least three external reviewers before the decisions were made. The papers are briefly summarized.

Recent implementations of heterogeneous multicore systems [central processing unit (CPU), graphics processing unit (GPU), and hybrid] address the issue of communication latency between CPU and GPU memory systems by merging these two, so that they can share the same memory address space. In recent years, the combination of the escalation in the number of cores with the rise in memory-intensive applications has significantly increased bandwidth (Bw) needs in both homogeneous and heterogeneous systems. Since tasks assigned to CPU and/or GPU cores will have different Bw demands, a two-tier memory system is needed. Hence, in this paper, Region-Aware Memory cONtroller (RAMON) is proposed as a configurable memory system where different address space regions are able to be dedicated to a different number of memory controllers (MCs), concurrently to supply different amounts of Bw to a different number of cores, providing different levels of memory parallelism. By having different address space regions-simply regions, each with a different number of MCs to match its Bw needs, memory interference per region is reduced. Our findings show that RAMON is promising and improves Bw by a factor of 9 times for CPU regions, 14.1 times for GPU regions, and 4.5 times for combined heterogeneous regions.

The increasingly severe network security situation brings unanticipated challenges to mobile networking. Traditional HMM (Hidden Markov Model) based algorithms for predicting the network security are not accurate, and to address this issue, a weighted HMM based algorithm is proposed to predict the security situation of the mobile network. The multiscale entropy is used to address the low speed of data training in mobile network, whereas the parameters of HMM situation transition matrix are also optimized. Moreover, the autocorrelation coefficient can reasonably use the association between the characteristics of the historical data to predict future security situation. Experimental analysis on DARPA2000 shows that the proposed algorithm is highly competitive, with good performance in prediction speed and accuracy when compared to existing design.

Molecular docking is an important method in computational drug discovery. In large-scale virtual screening, millions of small drug-like molecules (chemical compounds) are compared against a designated target protein (receptor). Depending on the utilized docking algorithm for screening, this can take several weeks on conventional HPC systems. However, for certain applications including large-scale screening tasks for newly emerging infectious diseases such high runtimes can be highly prohibitive. In this paper, we investigate how the massively parallel neo-heterogeneous architecture of Tianhe-2 Supercomputer consisting of thousands of nodes comprising CPUs and MIC coprocessors that can efficiently be used for virtual screening tasks. Our proposed approach is based on a coordinated parallel framework called mD3DOCKxb in which CPUs collaborate with MICs to achieve high hardware utilization. mD3DOCKxb comprises a novel efficient communication engine for dynamic task scheduling and load balancing between nodes in order to reduce communication and I/O latency. This results in a highly scalable implementation with parallel efficiency of over 84% (strong scaling) when executing on 8,000 Tianhe-2 nodes comprising 192,000 CPU cores and 1,368,000 MIC cores.

Recently, cloud storage has become quite attractive due to its elasticity, availability and scalability. However, the security issue has started to prevent public clouds from getting even more popular. Traditional encryption algorithms (both symmetric and asymmetric ones) fail to help achieve effective secure cloud storage due to their severe issues such as complex key management and heavy redundancy. Ciphertext-Policy Attribute Based Encryption (CP-ABE) scheme overcomes the aforementioned issues and provides fine-grained access control as well as deduplication features. CP-ABE has becomes a possible solution to cloud storage. However, its high complexity has prevented it from being widely adopted. This paper proposes a P-CP-ABE scheme to parallelize CP-ABE and port it to multi-core architecture machines. Major performance bottlenecks such as key management and encryption/decryption process are identified and accelerated. New AES encryption operation mode is adopted for further performance gains. Experimental results have demonstrated its effectiveness.

In this paper we illustrate a proposal with regard to providing learners adaptive micro learning experiences, which can be fulfilled within fragmented time pieces. The framework of our system is demonstrated, while it aims to deliver customized micro learning contents taking into account learners' specific demands, learning styles, preference and context.

As the size of high performance applications increases, four major challenges including heterogeneity, programmability, failure resilience, and energy efficiency have arisen in the underlying distributed systems. To tackle with all of them without sacrificing performance, traditional approaches in resource utilization, task scheduling and programming paradigm should be reconsidered. As Hadoop has handled data-intensive applications well in Clouds, GPU has demonstrated its acceleration effectiveness for computation-intensive ones. This paper intends to integrate Hadoop with CUDA to exploit both CPU and GPU resources. Hadoop will schedule MapReduce's Map and Reduce functions across multiple nodes, whereas CUDA code helps accelerate them further on local GPUs. All available heterogeneous computational power will be utilized. MapReduce in Hadoop will ease the programming task by hiding communication details. Hadoop Distributed File System will help achieve data-level fault resilience. GPU's energy efficiency characteristics help reduce the power consumption of the whole system. To achieve Hadoop and GPU integration, four approaches including Jcuda, JNI, Hadoop Streaming, and Hadoop Pipes, have been accomplished. Experimental results have demonstrated their effectiveness.

Fault-tolerance has huge impact on embedded safety-critical systems. As technology that assists to the development of such improvement, Safe Node Sequence Protocol (SNSP) is designed to make part of such impact. In this paper, we present a mechanism for fault-tolerance and recovery based on the Safe Node Sequence Protocol (SNSP) to strengthen the system robustness, from which the correctness of a fault-tolerant prototype system is analyzed and verified. In order to verify the correctness of more than thirty failure modes, we have partitioned the complete protocol state machine into several subsystems, followed to the injection of corresponding fault classes into dedicated independent models. Experiments demonstrate that this method effectively reduces the size of overall state space, and verification results indicate that the protocol is able to recover from the fault model in a fault-tolerant system and continue to operate as errors occur.

This paper introduces a prototype of Taiwan UniCloud, a community-driven hybrid cloud platform for academics in Taiwan. The goal is to leverage resources in multiple clouds among different organizations. Each self-managing cloud can join the UniCloud platform to share its resources and simultaneously benefit from other clouds with scale-out capabilities. Accordingly, resources are elastic and sharable with each other such as to afford unexpected resource demands to each cloud. The proposed platform provides a web portal to operate each cloud via a uniform user interface. The construction of virtual clusters with multi-core VMs is supplied for parallel and distributed processing models. An object-based storage system is also delivered to federate different storage providers. This paper not only presents the architectural design of Taiwan UniCloud, but also evaluates the performance to demonstrate the possibility of current implementation. Experimental results show the feasibility of the proposed platform as well as the benefit from the cloud federation.

In overlay‐based applications, multiple overlay networks are deployed to fulfill different service requirements. A multi‐overlay environment may exist in which a number of nodes simultaneously participate in the networks. When there are multiple overlay‐based applications running over a set of nodes, some of the nodes take extra effort to maintain multi‐overlay networks. Therefore, maintaining these co‐existing overlays incurs redundant maintenance overhead. This research presents a cooperative strategy for exploiting a master–slave model to handle the common overlay‐maintenance. The purpose is to eliminate the redundant maintenance overhead. To evaluate system performance, this study not only analyses various combinations of multiple overlays but also considers the effectiveness of the master selection approach. Experimental results demonstrated that the proposed cooperative strategy significantly decreases the redundant overlay‐maintenance overhead. In some cases, the overall reduction ratio of maintaining multiple overlays is as high as 60%.

Computation state construction is an indispensable step to achieve fault tolerance and computation mobility for scientific applications by saving and restoring the state during program execution. However, there is no effective state construction scheme yet due to the GPU's batch-mode execution manner as the GPU takes on a larger role in high performance computing. The GPU's complex memory hierarchy means the states are scattered in different memory locations that are difficult to fetch. Programs that are running in parallel make the states difficult to construct for each thread. The paper proposes an application-level computation state construction scheme to support GPU programs. A precompiler and run-time support module are developed to construct and save states in the CPU system memory dynamically. Memory blocks are registered, and new data structures are proposed to save and restore the computation states represented by variables and pointers in the GPU. Secondary storage can be utilized for scalability and long-term fault tolerance.

In most cases of safety-related systems, the network is an indispensable part. At this point, the system reliability is as important as the system communication quality. With the emergence of multi-core architectures, the first generation usually aims to provide reliable and deterministic computing resources. Therefore, with the boost requirement of reliability and throughput that cannot be satisfied by general single-core processors, the deployment of safety-related systems is transferred and processed multi-core environments. In this paper, we propose Reliable Communication Server on SPU (RCSoS), which is a server model for reliable communication utilizing SPU (Synergistic Processor Unit) in Cell/B.E (Cell Broadband Engine). It simulates SPU as a communication server and guarantees the reliability and determinacy by the isolation mode of SPU and contract model. We have implemented RCSoS in PlayStation 3, which dynamically adjust parameters, and inform applications on contract violations. Experiments show the performance of this model.

Checkpoint/restart has been an effective mechanism to achieve fault tolerance for many scientific applications. However, as GPU becomes a much bigger role in high performance computing, there is no effective checkpoint/restart scheme yet due to GPU's batch-mode execution manner. The paper proposes an application-level checkpoint/restart scheme to save and restore GPU computation states. A precompiler and run-time support module are developed to construct and save states in CPU system memory dynamically. Secondary storage can be utilized for scalability and long-term fault tolerance. CUDA applications with complicated memory use are support as well. Experimental results have demonstrated the effectiveness of the proposed scheme.

In this paper, we demonstrate a new motion in painting technique to allow users to change the dynamic texture used in a video background for special effect production. For instance, the dynamic texture of fire, smoke, water, cloud, and others can be edited through a series of automatic algorithms. Motion estimations of global and local textures are used. Video blending techniques are used in conjunction with a color balancing technique. The editing procedure will search for suitable patches in irregular shape blocks, to reproduce a realistic dynamic background, such as large waterfall, fire scene, or smoky background. The technique is suitable for making science fiction movies. We demonstrate the original and the falsified videos in our website at http://163.13.127.36/www/AINA12. Although video falsifying may create a moral problem, our intension is to create special effects in movie industry.

To support cloud storage effectively, a Distributed File System (DFS) should be well-rounded with excellent features in multiple major aspects and without significant drawbacks. The main design goals of a DFS in Clouds include security, reliability and scalability. Traditionally, cryptography, data duplication and powerful machines are common approaches to support DFS. However, the success of such a DFS will depend on cumbersome key management, large storage and costly infrastructure, respectively. This paper intends to revise Blakley's secret sharing and apply it to a DFS for both security and reliability without sacrificing the scalability in performance too much. A DFS is deployed with GPU (Graphics Processing Unit) as an acceleration option to tackle with scalability issue further. Experimental results have demonstrated the effectiveness of the new DFS.

Recently, more and more researches and applications exploit grid computing systems to deal with high performance computing. However, the mass data transmissions across different grid sites affect overall computing performance. Therefore, grid systems start to integrate with the P2P technology to support the high performance distributed computing. The new distributed computing system is named the P2P Grid computing system. Although the P2P Grid computing system combines the advantages of the grid computing system and the P2P technology, some issues are still needed to be solved. For example, the highly variable resource usage and the heterogeneity of resources could intensely affect the P2P Grid system performance. In this case, the computing performance depends on the resource management policy. Therefore, this study proposes a distributed dynamic load balance policy to manage resources more effectively and to further improve the resource utilization. The prototype is implemented on the sites of the Taiwan Uni Grid, and the P2P grid sites exchange information by JXTA advertisements. Experimental results show that the proposed algorithm could efficiently distribute the workload for execution, that is, it not only can minimize the job execution time, but also maximize the resource utilization.

Task consolidation is a way of maximizing cloud computing resource, which brings many benefits such as better use of resources, rationalization of maintenance, IT service customization, QoS and reliable services, etc. However, maximizing resource utilization does not mean efficient energy usage. Many literature show that energy consumption and resource utilization in clouds are highly coupled. Some research works aim to decrease resource utilization for saving energy while some try to find the balance between resource utilization and energy consumption. In this paper, an energy-aware task consolidation (ETC) technique is presented aims to optimize energy consumption of virtual clusters in cloud data center. Conforming most cloud systems, a 70% principle of CPU utilization is proposed to manage task consolidation among virtual clusters. The simulation results show that ETC can significantly reduce power consumption in managing task consolidation for cloud systems. Up to 17% improvement as compare to a recent work in [10] that aims to maximize resource utilization can be obtained.

The ubiquity of many-core architectures brings challenges in making scalable application software, changing dramatically from the way applications are traditionally developed. Optimization of programs for many-core platforms is a multifaceted problem, where system and architectural factors should be taken into consideration. In this paper, we attack the problem on the aspect of programming paradigm. We propose a hybrid producer-write plus consumer-read shared-memory programming paradigm for implementation of a master-worker video decoder on the TILE64 many-core platform. To evaluate the scalability and performance benefits of different programming paradigms, a Motion JPEG decoder is parallelized using master-worker structure and implemented with combinations of consumer-read programming and producer-write programming. Experimental results show that the proposed implementation obtained competitive performance speedup, scaling well with number of available cores and up to 4 times performance improvement over other implementations on the decoding of a 1080P video.

The robust availability of resources in distributed computing environments is a very important issue. In general, the resources are distributed among geographical distributed sites resulting in the higher failure probability. Therefore, this paper proposes a fault tolerance policy on dynamic load balancing in P2P grids to improve the dynamic availability of resources. This proposed policy duplicates jobs in different computing nodes to avoid job or hardware failure. In the meantime, the proposed fault tolerance policy also considers the load balancing among different computing nodes while keeping the stable job turnaround time. Therefore, the proposed policy could improve the system performance in a varying environment. Experimental results show that the proposed policy could achieve a better job completion rate as the failure rate increases.

With the rapid growth of network technologies, many new web services have been developed to provide various applications and computing functions. These services rely deeply on the internet. Therefore, packet classification is an important issue of network security that typically adopts a flexible packet filtering system to classify each processed packet. Traditional packet classification requires hung computing time to process large amount of internet packets. Hence, we propose a GPGPU-based parallel packet classification method to decrease the computational cost. We also evaluate the performance of the proposed method with implementation on various memory architectures of CUDA device. The experiment results demonstrate that the proposed method can achieve significant speed up over the sequential packet classification algorithms on single CPU.

Overlay networks are widely adopted in many distributed systems for efficient resource sharing. Recently, issues in overlay network have also been introduced into cloud systems, in order to organize thousands of virtualized resources. In parallel, the explosion of P2P applications introduces the multi-overlay environment in which a number of nodes simultaneously participate in multiple overlays. When multiple applications running over a large set of nodes, some of nodes may take repeated efforts to preserve multi-overlay networks. Therefore, maintaining these co-existing overlays brings the redundant maintenance overhead. This paper presents a cooperative strategy to analyze the overlay maintenance of multi-overlay environments and to elaborate multiple overlays for simplifying the overlay maintenance. The proposed strategy exploits the synergy of co-existing overlays to handle their common overlay-maintenance, so that the redundant maintenance overhead could be eliminated while keeping performance. To evaluate the system performance, this paper not only analyzes several overlays but also considers realistic multi-overlay environments by varying the intersection ratio of diverse overlays and the combination of multiple overlays. Experimental results show that the proposed cooperative strategy significantly decreases the redundant overlay-maintenance overhead, where the reduction ratio of maintaining multiple overlays is higher than 60 percent in some of cases.

In order to take advantage and profit with the popularization of digital music, companies started marketing licensed content on high-storage portable media players. The introduction of wireless technology in such players motivates new business opportunities where music distribution is ubiquitous. However, in such high-supply scenario, consumers may have difficulties to find interesting content. In such context, music recommender systems assist consumers in identifying their preferences and in supporting content searches. An important feature in such market is the low attention given to new music styles, what increases the promotion costs. In order to assist consumers who, positive or negatively, pay attention to such novelty factor, this work proposes a novel method to estimate music preference profiles based on acoustic similarity measures. Such profiles are learnt by an artificial neural network, named self-organizing novelty detection neural network architecture (SONDE), which classifies and quantifies the novelty level of music titles regarding the user profile. Based on novelty levels, we suggest a discount rate model to support promotion strategies. The proposed method is evaluated by simulating some scenarios.

As enterprises worldwide race to embrace real-time management to improve productivity, customer services and flexibility, large amount of resources have been invested in enterprise systems (ESs). As comprehensive feature of these modern systems, they utilize a n-tier client-server architecture that includes several application servers to serve users and host applications. The load and user distributions become a critical issue in performance tuning of these enterprise systems, as any other multi-server environments. This paper proposes an algorithm to distribute users by evoking similar transactions to same servers, which have limited buffer sizes. The number of transactions can be hosted in each server is constrained by the buffer size multiplied by a factor specified by system administrators. Based on user profiles, the algorithm return suggestions of user distributions, the number of servers needed, and similar user requests in each server. In addition, it discusses how to apply the knowledge of existing user patterns to distribute new users, who do not have enough entries in the profile and have no distribution suggestion during run-time.

Dynamic data redistribution is used to enhance data locality and algorithm performance by reducing interprocessor communication in many parallel scientific applications on distributed memory multicomputers. Since the redistribution is performed at runtime, there is a performance tradeoff between the efficiency of the new data decomposition for a subsequent phase of an algorithm and the cost of redistributing data among processors. In this paper, we present a processor replacement scheme to minimize the cost of interprocessor data exchange during runtime. The main idea of the proposed technique is to develop a replacement function for reordering logical processors in the destination phase. Based on the replacement function, a realigned sequence of destination processors can be derived and is then used to perform data decomposition in the receiving phase. Together with local matrix and compressed CRS vectors transposition schemes, the interprocessor communication can be eliminated during runtime. A significant improvement of this approach is that the realignment of data can be performed without interprocessor communication for special cases. The second contribution of the present technique is that the complicated communication sets generation could be simplified by applying local matrix transposition. Consequently, the indexing cost could be reduced significantly. The proposed techniques can be applied in both dense and sparse applications. A generalized symmetric redistribution algorithm is also presented in this work. To analyze the efficiency of the proposed technique, the theoretical analysis proves that up to (p-1)/p data transmission cost can be saved. For general cases, the symmetric redistribution algorithm saves 1/p communication overheads compared with the traditional method. Experimental results also show that the proposed techniques provide superior performance in most data redistribution instances.

This research paper discusses performance issues of cluster and grid computing platforms, and reasons to support the implementation of these computing infrastructures. A number of benchmark programs are executed in these computing systems, in order to perform performance analysis of experimental results. We are able to show that cluster platforms are excellent alternatives to access to supercomputing, due to its cost/performance, scalability and commodity components factors. In addition, we also show that grid technology is viable by increasing total system performance at no additional cost.

In this paper, we present visual tool for performance measurement and analysis of MPI parallel programs in cluster environments. Most of tools available today for cluster systems show solely system performance data (e.g., CPU load, memory usage, network bandwidth, machine-room temperature, server average load, among others), being more suitable for system administrators who maintain such system. The visual tool is designed to show performance data of all computer nodes involved in the execution of MPI parallel program, such as CPU load level and memory usage. Additionally, this tool is able to display comparative performance data charts of multiple executions of the application (instrumented with MPI interface) under development.

The low cost and wide availability of PC-based clusters have made them an excellent alternative to access supercomputing. However, while network of workstations may be readily available, there is an increasing need for performance tools that support these platforms, in order to achieve even higher performance. One of possible ways to increase performance is parallel program restructuring. It is introduced in this paper a toolkit to generate graphical charts for visualization of MPI parallel programs, reflecting to its execution over time, with the use of DP*Graph representation, parallel version of timing graph. In other words, parallel programs are shown through charts its sequential codes, dependencies and communication structures in a particular cluster system platform. Still in this paper, it is discussed the implementation of this toolkit and present some experimental results obtained.

In distributed shared memory (DSM) systems, it is the common need to access data in remote nodes. Thus, it induces to remote access performance latencies, which is the major factor of overhead for DSM systems. Prefetching strategies can improve these phenomena by reducing latencies, but it adds workload to home nodes. It is proposed in this paper a method to reduce overhead of home nodes, by providing an agent home to share the workload of home nodes, by distributing these workloads to other nodes, when sending data. The performance evaluation of proposed strategy is done by performing three well-known benchmark programs: NPB/IS, 3DFFT and Red-Black SOR. The experimental results show that our proposed agent home method achieves 8%- 40% of speedup against original JIAJIA.

In this paper, a parallel loop self-scheduling scheme for heterogeneous PC cluster systems is proposed. Though the proposed scheme does allow users to choose parameters before the execution initialization phase, there are still weaknesses that motivate us to go further with new improvements in that scheme. For instance, a decision on a fixed and monotonous parameter can easily lead to invalid schedule by using previous input information. Thus, it is proposed in this paper a new scheme, where the scheduling parameter can be adjusted dynamically and fit into most widely available computer systems, in order to provide higher overall performance.

Internet computing and grid technologies promise to change the way we tackle complex problems. They will enable large-scale aggregation and sharing of computational, data and other resources across institutional boundaries. As grid computing is becoming a reality, there is a need for managing and monitoring the available resources worldwide, as well as the need for conveying these resources to the everyday user. This paper describes a resource broker with its main function as to match the available resources to the user's needs. The use of the resource broker provides a uniform interface to access any of the available and appropriate resources using user's credentials. The resource broker runs on top of the Globus toolkit. Therefore, it provides security and current information about the available resources and serves as a link to the diverse systems available in the grid.

In this paper, the authors presented the grid-based decision tree architecture, with the intention of applying it to both parallel and sequential algorithms. Also, it is shown that, based on the scope and model of data mining applied in the grid environment as well as user equivalent perspective, grid roles can be categorized into three types. It was aimed, through these definitions, to help software developers define clear system processes and differentiate the application scope for software applications. To fulfill the architecture, an existing parallel decision tree algorithm was first applied (the SPRINT algorithm) to the grid environment. The performance and differences in many other areas are compared using datasets of different sizes. The experimental results will be used for future reference and further development.

Internet computing and Grid technologies promise to change the way we tackle complex problems. Harnessing these new technologies effectively, it will transform scientific disciplines ranging from highenergy physics to life sciences. This paper describes a metropolitan-scale Grid computing platform named TIGER Project (standing for Taichung Integrating Grid Environment and Resource), which basically interconnects universities and high schools’ cluster computing resources and sharing available resources among them, for investigations in system technologies and high performance applications. This novel project shows the viability of implementation of such project in a metropolitan city.