State re-sharding can reduce cross-shard transaction ratios, which improves the scalability of blockchain systems. However, unavoidable cross-shard transactions and account-locking mechanisms can lead to security risks (read-write conflicts) and performance bottlenecks (low synchronization efficiency). Therefore, this paper proposes a secure and efficient read-write synchronization model for cross-shard transactions in blockchain state re-sharding via a lightweight Global State Tree ($\mathcal{GT}$). The model consists of intra-shard and inter-shard state consistency modules. The intra-shard module includes two methods: account state read-write and account record update. The former allows local shard committees to track account state changes and prevent the use of expired account states, while the latter incorporates account records within maximum latency into an account state data structure, thereby enhancing the traceability and verification efficiency of update history. In the inter-shard module, a transaction processing method with a global takeover mechanism is proposed during the re-sharding window. By using validated data in the$\mathcal{GT}$, the method achieves non-blocking global coordination and account reallocation. Experimental results demonstrate that the proposed model increases transaction throughput and reduces transaction latency compared to bases under Byzantine conditions.

Ethereum 2.0 (ETH2) marks a pivotal shift in blockchain technology, transitioning from a Proof-of-Work (PoW) to a Proof-of-Stake (PoS) consensus mechanism, with Gasper at its core. While this evolution promises enhanced scalability and energy efficiency, the performance of its block proposal stage is highly sensitive to network latency and system parameters, such as slot length. This sensitivity introduces a critical trade-off between throughput and security, measured by the probability of blockchain forking. This paper reveals that network latency is not just a passive risk but an exploitable attack surface. We introduce the "adaptive latency-driven equivocation attack", a novel adversarial strategy where an attacker deliberately creates forks while mimicking the behavior of a high-latency node, thus achieving plausible deniability. To formally analyze and quantify the impact of this threat, we develop a comprehensive theoretical model by using Markov chains to analyze the fork probability and throughput of the Gasper's block proposal mechanism under both honest and adversarial conditions. Through extensive simulations, we validate the accuracy of our model in both normal and bursty traffic conditions. Our findings provide a systematic methodology for optimizing system parameters to achieve a robust balance between performance and security, offering a foundational guide for configuring ETH2 networks against sophisticated, latency-based threats.

Quality of Service (QoS) prediction faces challenges from location-dependent variability and sparse user-service interactions. Existing methods often struggle to integrate location information (e.g., using fixed weights for spatial attributes) or learn representative features from sparse matrices. This paper proposes a method for Decoupling Location and Preference via a dual-branch architecture for robust QoS prediction under extreme sparsity, called DLP. It integrates location and preference features to address the challenges of sparsity and contextual variability. Unlike conventional single-stream or simple concatenation methods, DLP features a novel dual-branch architecture that decouples heterogeneous features and specializes in processing them: Location context and user-service preferences. The first branch, a location feature extraction network, processes user and service geographical and network information. It utilizes an attention mechanism to dynamically weight spatial attributes (instead of fixed weights) based on their actual impact on QoS and selects the most salient co-location features to model spatial interactions. The second branch, a preference feature extraction network, constructs high-dimensional feature representations from similarity-based user-service vectors derived from the sparse QoS matrix. It employs a multi-layer feature extraction block that hierarchically aggregates intermediate features to compensate for information loss during transformation, thereby capturing richer user/service preferences. Finally, a feature fusion prediction network integrates the learned location and preference features to generate accurate QoS predictions. Ablation studies and analysis validate that each component contributes significantly to performance gains. Extensive experiments on the WS-DREAM dataset show that DLP outperforms 22 baselines across 2.5%–20% sparsity, excelling in throughput prediction (achieving reductions up to 9.07% in Mean Absolute Error and 28.86% in Root Mean Squared Error at 2.5% sparsity) and validating its superior QoS prediction accuracy.

Federated Learning (FL) has emerged as a promising paradigm for secure data sharing in Industrial Internet of Things (IIoT), enabling collaborative model training without direct exchange of raw data. However, recent studies have shown that FL still suffers from privacy vulnerabilities, where adversaries can reconstruct sensitive information by analyzing shared model parameters. Although several privacy-preserving FL (PPFL) schemes have been proposed to address these challenges, they primarily focus on protecting local model privacy, with limited attention to protecting global model confidentiality during aggregation. Additionally, their reliance on centralized aggregation servers introduces risks of single points of failure. To address these challenges, we propose a novel privacy-preserving blockchain-based FL framework (PBFL) that integrates blockchain, homomorphic encryption (HE), and a single masking. Specifically, PBFL employs HE to enable secure model training within the ciphertext domain, ensuring global model confidentiality. The single masking technique allows clients to apply unique random masks to their encrypted local model updates, enabling secure aggregation while preserving local privacy. Additionally, PBFL leverages blockchain for decentralized aggregation and encrypted model storage, effectively mitigating the risks associated with centralized servers. Experimental results demonstrate that PBFL achieves comparable model accuracy to state-of-the-art solutions while providing enhanced privacy protection. Furthermore, even with a client dropout rate of up to 30%, PBFL outperforms other blockchain-based PPFL methods in terms of computational and communication efficiency.

The widespread adoption of WiFi has made throughput efficiency a critical concern in wireless networks. While Full-Duplex (FD) technology promises to double network capacity by enabling simultaneous transmission and reception, existing FD-WiFi designs primarily focus on the data transmission phase, leaving the fundamental inefficiencies in channel contention unaddressed. This paper presents CollFree, a novel WiFi protocol that exploits FD capabilities during both contention and data transmission phases. At its core, CollFree introduces a Slotwise Arbitration (SA) mechanism that enables each node to simultaneously transmit contention signals and sense channel status in each contention slot. This dual-mode operation significantly reduces contention time and facilitates collision-free data transmissions through a unique winner-determination process. We then develop theoretical models to analyze CollFree’s contention performance and throughput efficiency under both perfect and imperfect Clear Channel Assessment (CCA) conditions, providing guidelines for parameter optimization in practical deployments. Extensive simulations demonstrate that CollFree enhances throughput efficiency by over 20% compared to state-of-the-art FD-WiFi systems while maintaining distributed control and compatibility with current WiFi standards. These results suggest that CollFree represents a significant step toward realizing the full potential of FD technology in next-generation WiFi networks.

Cross-technology communication (CTC) enables direct communications among devices with heterogeneous wireless technologies, e.g., Bluetooth, WiFi, and ZigBee, thereby reducing the cost and complexity of their interconnections. Yet CTC is unreliable due to the technology heterogeneity, and most existing CTC designs do not provide acknowledgment (ACK) feedback to ensure reliable data transmission. Few ACK designs are only applicable to feedback for ZigBee-WiFi pair and vulnerable to sampling offsets that inherently exist in CTC. In this work, we propose a General and Offset-resistant Physical-layer ACK approach, called GOP-ACK, to support reliable communications. Its core idea lies in encoding ACK messages with offset-resistant signal that has two benefits: 1) it can be adapted to a wide range of CTC scenarios with minimal adjustment, and 2) it can be effortlessly and robustly detected even in the presence of sampling offsets. We offer practical guidelines to tackle key deployment challenges related to signal construction, efficient and robust transmission, and effective firmware module reuse, enabling the application of GOP-ACK to specific CTC scenarios. Based on them, we implement two designs: ZigBee-to-BLE and ZigBee-to-WiFi feedback, and propose a theoretical model to analyze their performance. We then conduct experiments and simulations to verify GOP-ACK’s feasibility and superiority over the state of the art, thereby enhancing the practicality of CTC greatly.

Practical Byzantine Fault Tolerance-based consensus mechanisms in consortium blockchains face challenges in scalability and communication efficiency. While recent approaches like HotStuff and Kauri have attempted to address these issues through star and tree communication structures, they still encounter limitations in security, communication costs, and node workload distribution. This article presents CTT, a novel consensus mechanism with a three-layer tree communication structure for consortium blockchains. CTT incorporates three key innovations: 1) A fixed three-layer architecture that reduces communication complexity between any two nodes toO(1), compared toO(logn) in existing tree-based approaches; 2) specialized role distribution among nodes at different layers to optimize workload and enhance system security; 3) an improved Borda counting method for efficient consensus node selection based on multiple attributes including verification rate, propagation rate, and storage space. The mechanism features dual middle-node communication paths with bottom nodes, providing enhanced fault tolerance and security compared to existing approaches. Experimental results demonstrate CTT's effectiveness in improving scalability and security while reducing communication overhead in consortium blockchain systems. The findings have the potential to significantly advance the performance and applicability of consortium blockchains in critical areas such as finance, supply chain, and healthcare.

Edge devices are positioned on the periphery of cloud environments, serving as agile, secure, and dependable service providers. Nevertheless, IoT edge devices grapple with distinct physical and communication vulnerabilities, such as message forgery and tampering. Thus, trust computing is indispensable for IoT edge devices. Existing trust computing methods confront certain challenges, such as overlooking the impact of fluctuating device resources and subjectivity in human-based weighting aggregation. This work designs a multi-source trust model rooted in IoT edge task collaboration. Edge devices undergo evaluation based on direct trust, indirect trust, and capability trust. This evaluation yields trust values for the edge devices, allowing for the identification of those with dynamic identities with malicious behavior. Subsequently, these malicious entities are excluded from the pool of edge devices. The proposed model employs an IoT consortium blockchain. This blockchain maintains records of interactions within edge task collaboration and documents feedback on trust concerning edge devices. The stored data is accessible to the public and can be verified by other devices, acting as a safeguard against tampering by malicious devices, thereby ensuring data security and privacy. Experimental results affirm that the proposed model exhibits superior computational efficiency and greater reliability when compared to existing methods.

Blockchain technology has garnered increasing attention from researchers. Because blockchain systems may contain malicious or spatially limited nodes that may delay block verification and reduce block transmission rate, this work proposes a block transmission model by designing and using special clusters. This work proposes the cluster formation and selection mechanisms. Nodes are grouped into clusters in a blockchain, and clusters with high fitness values are chosen to transmit blocks by calculating their trust values and block transmission rates. The proposed method is compared with the peers: Layer-Chain, BlockP2P-EP and RNS. According to experimental findings, the proposed method is superior to its peers regarding the time needed for block synchronization and transmission, block occupation storage ratio, transaction throughput, and block transmission success ratio.

Ensuring data integrity in edge computing environments presents significant challenges, primarily due to the distributed architecture of edge servers and the inherent risk of data corruption. Traditional edge data integrity (EDI) verification methods predominantly rely on sampling techniques, provide only probabilistic integrity assurances and often struggle with scalability and efficient corruption localization. To overcome these limitations, we introduce the deterministic integrity assurance and rapid corruption localization EDI (DL-EDI) verification scheme, a novel approach that combines extended Merkle grid (EM-Grid) with Boneh–Lynn–Shacham (BLS) signatures. DL-EDI leverages EM-Grid for comprehensive data integrity verification, ensuring deterministic integrity validation across all data blocks while facilitating rapid block corruption localization. Additionally, we incorporate a hierarchical signature aggregation method using BLS signatures to optimize verification efficiency and minimize communication and computational overhead. A thorough performance analysis of DL-EDI is conducted, evaluating its verification accuracy, communication and computational efficiency, and resilience against various security threats. Comparative experimental evaluations of DL-EDI against four established EDI schemes highlight its superior effectiveness and efficiency in addressing the challenges of EDI.

Node-assisted WiFi backscatter communication (NWB) is a promising technology that allows backscatter tags to communicate over long distances and achieve high throughput by using WiFi nodes as relays and enabling concurrent transmissions. However, NWB lacks an accurate theoretical model to evaluate and optimize its network performance, which is challenging to develop due to the location and contention randomness of both WiFi nodes and backscatter tags. Existing backscatter models that only account for one type of randomness are not suitable for NWB. To address this issue, we propose a novel stochastic geometry-based model that captures Location and Contention Randomness as well as the involved dependency and interference (named LoCoR). We use the Matérn hard-core point process and Matérn cluster process to model the repulsive and clustering attributes of the locations of WiFi nodes and backscatter tags, respectively. We also introduce a unified time unit to analyze the randomness and dependency of WiFi and backscatter contentions. Our model factors in various design parameters (e.g., the density and transmission power of tags) and can be used to evaluate their impacts on system throughput. We conduct extensive simulations to validate the accuracy of our model. With our accurate model, one can easily configure the optimal design parameters to maximize system throughput.

With the rapid development of service computing, the demand for service recommendation is increasing. Quality of Service (QoS) prediction has been one of the key challenges for service recommendation. Existing deep learning-based methods have been proposed for QoS prediction, but further improvement of their neural network structures is still needed to improve the prediction accuracy. This work introduces multi-stage multi-scale feature fusion with individual evaluations to a deep learning model for accurate QoS prediction. In our model, non-negative matrix factorization is used to extract three-scale (i.e., global, local, and individual) features; distance similarity is exploited to find similar users and services; a multi-stage deep neural network is designed to fuse multi-scale features, where individual evaluations are input to each stage to correct QoS features. Finally, our model is compared with often-cited prediction methods, and the experimental results show that it can more accurately predict QoS than its peers.Note to Practitioners—Accurate QoS prediction is very helpful to recommend the most suitable services to users among many similar services. Affected by the sparsity of historical data, the accuracy of existing QoS prediction methods is often limited. The multi-scale features of users and services can be used to improve prediction accuracy. This work proposes a new QoS prediction method to do so. Specifically, it first extracts global and individual features through non-negative matrix factorization and uses distance similarity to obtain local features. Then, it proposes a new deep neural network that fuses the extracted multi-scale features in each learning stage, thereby improving QoS prediction for services recommendation.

Blockchain as a ledger technology is attractive without the need for central servers. There are many types of blockchains in different fields, such as digital asset trading and payment, which have business interactions. In the fields, the digital asset and payment information on their blockchains need to be securely cooperative with each other. However, some business activities are on different blockchains, which have different consensus algorithms and network architectures, thus limiting the interoperability among these activities and making each blockchain an island. Cross-chain technology can connect different blockchains and realize the interoperability and sharing of information among them. This work designs a cross-chain digital asset system for secure trading and payment. It builds two parallel chains, i.e., digital asset chain (DAC) and payment chain (PC), and their functions are analyzed and designed. The cross-chain, i.e., relay chain (RC), is used to realize cross-chain interoperability, where the cross-chain message format and authority setting are designed to endow parallel chains with the ability to recognize. The decentralized characteristic of the RC allows cross-chain messages to be safely transmitted to ensure secure trading and payment. Through testing and analysis, the proposed system can provide more secure trading and payment than its peers.

Predicting quality of service (QoS) is an important issue in the field of service recommendation that has been widely studied in the past few years. Many current methods predict QoS values based on the historical invocation records of services, but most of them ignore the time-varying characteristics of these values. Capturing time-varying characteristics to ensure accurate prediction of QoS values has become a key problem in the area. To solve this problem, in this article, we first apply probabilistic matrix factorization in QoS time series of sparse QoS matrices to extract time-varying feature series of users and services. Then we construct a gated feature extraction network (GFEN) to compensate for feature loss due to matrix factorization and enrich the limited information due to the sparsity of QoS matrices, where the heart of GFEN is an enhanced gated recurrent unit (EGRU) and a generative adversarial network is proposed to train GFEN. Extensive experimental results show that the proposed model outperforms state-of-the-art methods in terms of QoS prediction accuracy.

Quality of Service (QoS) prediction is a crucial issue in service recommendation, which has been widely studied in the past few years. It faces several challenges, including improving QoS prediction accuracy. Can one extract and use deep features of users and services to improve it? This work answers this question by proposing a deep-learning model for service QoS prediction. In this model, a feature mapping and inference network is first designed to obtain high-dimensional feature matrices of users and services, which can enhance data flow information and reflect the deep relationships among users and services. Then, feature compensation blocks are designed to compensate for the possible loss of feature information in feature mapping and inference. Finally, a QoS prediction network is constructed to fuse the obtained feature matrices to predict QoS values. Experimental results show that the proposed method can achieve higher prediction accuracy than ten typical and representative methods, thus advancing the state of the art in QoS prediction.

Blockchain-enabled Internet of Things (IoT) has been receiving growing attention. However, IoT nodes are usually resources heterogeneous and subject to malicious attacks, such as the intentional delay of block verification and the transmission of invalid blocks. As a result, a random node-selection model for block transmission may lead to a low transmission rate and serious security risk. To solve the problem, this article proposes a rate-and-trust-based node selection model for block transmission. In our model, we calculate the block transmission rate of a node by the latency and connectivity among nodes and its trust value by its historical transmission and verification behaviors. On this basis, we propose a PageRank-based optimization algorithm for node selection that makes a tradeoff between the transmission rate and the security risk. Extensive experimental results show that the proposed model can achieve better performance than the state-of-the-art methods, including Bitcoin network, Ethereum network, and BlockP2P-EP protocol.

Sharding technology is a promising solution for improving the scalability of blockchain systems. However, it faces the problem of allocating suitable trusted nodes into separate shards to satisfy security and efficiency requirements. Existing blockchain sharding methods fail to consider shard trust difference, communication latency difference, and node count difference among shards. This tends to increase the risk of a blockchain failure. This work proposes a novel blockchain sharding model for node allocation by considering shard trust difference. Its key idea is to allocate nodes of different trust levels to suitable shards to make shards have almost the same trust, such that shards’ reliability increases and blockchain failure probability decreases. To reduce the communication delay among shards, this work considers the communication latency difference and node count difference among shards. It proposes a sharding algorithm to iteratively adjust node allocations such that an optimal or near-optimal node allocation set is obtained. Simulation results show that the proposed method can effectively improve shard security and the performance of blockchain sharding compared with two state-of-the-art methods, i.e., Monoxide and Rapidchain, in terms of throughput, latency, and blockchain failure probability.

With the continuous development of consortium blockchains, the storage overhead of blocks and storage load of nodes are increasing. The existing storage models consider either low-reliability issues or high storage overhead but not both. This work proposes a storage model that combines group-based block storage with block splitting and unit encoding. The former is used to improve the reliability of a block, while the latter is adopted to reduce the storage overhead and increase the reliability of a block in a consortium blockchain. Based on this model, this work designs a storage method to minimize the storage overhead of blocks and storage load of nodes, and maximize reliability of a consortium blockchain. Experimental results show that the proposed method outperforms Multi-layer Practical Byzantine Fault Tolerance, RapidChain, and Byzantine Fault Tolerance-Store in terms of storage overhead and reliability, thus greatly advancing the field of consortium blockchains.

Accurately predicting Quality of Service (QoS) is one of the main challenges in the area of service recommendation, and it has thus attracted much attention in recent years. This field exists many methods, most of which are inspired by collaborative filtering in service recommendation. They predict the missing QoS values of services by collecting the historical information of similar users/services, but their prediction accuracy needs further improvements. This work proposes user and service graphs are proposed for the first time in the field of QoS prediction by exploiting deep relationships among users and services. Based on the graphs, user/service feature vector sets are found via similar users/services. A two-stream deep learning-based prediction model is proposed for service QoS prediction. It has a deep convolutional neural network with two efficient deep convolutional units to deal with user/service feature vectors parallelly. Experiments are carried out to show that the proposed method can achieve better QoS prediction accuracy of services than the existing approaches such as non-negative matrix factorization, probabilistic matrix factorization, covering-based neighbor-hood-aware matrix factorization, neighbor integration deep matrix factorization, and several traditional methods.

Existing blind image quality assessment (BIQA) methods aim to extract distortion features by training deep learning models to predict quality scores. However, images suffer from various distortions. Training a single model is typically hard to handle distortion variation problems. To solve the problem, we propose a novel BIQA method to model the quality degradation process caused by image distortions. It consists of a generative adversarial network (GAN)-based quality perception network and a quality regression network. The GAN-based quality perception network is designed to simulate the process of distortion information introduced to images in both forward and reverse directions. The quality regression network extracts the learned hierarchical restoration features from the quality perception network to learn the relationship between features and quality scores. Experimental results on four representation IQA datasets show that the proposed method achieves comparable quality prediction performance with other state-of-the-art methods.

The storage overhead of blocks and storage load of nodes are increasing as consortium blockchains grow. The existing storage models consider either low-reliability issues or high storage overhead but not both. This work proposes a new storage model that combines group-based block storage with block splitting and unit encoding for the first time. The former is used to improve the reliability of a block, while the latter is adopted to reduce the storage overhead and increase the reliability of a block in a consortium blockchain. This work analyzes the storage overhead of blocks, storage load of nodes, and reliability of a consortium blockchain and designs a storage optimization method to reduce the storage overhead and improve the reliability of a blockchain system. Experimental results show that the proposed method outperforms Multi-layer Practical Byzantine Fault Tolerance, RapidChain, and Byzantine Fault Tolerance-Store in terms of storage overhead and reliability.

Abstract Nowadays, it is still challenging for the blind image quality assessment (BIQA) to accurately predict quality scores of distorted images, since distorted images have rich content information and complex distortions. To solve these problems, a content perception and distortion inference network for BIQA is proposed, which divides IQA task into content perception and distortion inference processes. Since humans try to understand image content before perceiving quality scores, a content feature extractor is designed to explore content information in an image to deal with the content variation problem. To handle the distortion diversity problem, a distortion feature extractor is proposed to capture distortion features in images. Because extracted content features and distortion ones have different characteristics, attention‐based fusion blocks to fuse multi‐scale content features and distortion ones as guidance to selectively enhance important features based on calculated weight scores are proposed. With fused features, a quality prediction module is designed to regress multi‐scale features to quality scores. Experiments are performed on six public IQA datasets, including LIVE, CSIQ, TID2013, LIVEC, KonIQ‐10k, and SPAQ. Experimental results show that the proposed method can effectively predict quality scores for both synthetically and authentically distorted images than its peers, including the state‐of‐the‐art methods.

With the fast boom of Internet of Medical Things (IoMT) devices and an increasing focus on personal health, personal health data are extensively collected by IoMT and stored as personal health records (PHRs). PHRs are frequently shared for accurate diagnosis, prognosis prediction, health advice consulting, etc. Since PHRs are highly private, the data-sharing process leads to wide-ranging concerns on privacy leakage and security compromise. Existing research has shown that the centralized systems, as the mainstream mode, are under the great risks. Motivated by this, we propose a consortium blockchain-based PHR management and sharing scheme, which is both security aware and privacy preserving. We adopt the interplanetary file system (IPFS) to store the PHR ciphertext of IoMT. Then, zero-knowledge proof can provide evidence for verifying keyword index authentication on blockchain. Moreover, the scheme jointly leverages modified attribute-based cryptographic primitives and tailor-made smart contracts to achieve secure search, privacy preservation, and personalized access control in IoMT scenarios. Security analysis is conducted to show that the designed protocols attain the expected design goals. This is followed by extensive evaluation results derived from real-world data sets, which demonstrate the superiority of the proposed scheme over current leading ones.

In a distributed heterogeneous fog environment, fog nodes may change their state at any time. Their reliability changes accordingly. A dynamic analysis of state changes can help one detect fault-tolerant fog nodes, which is conducive to promoting the reliability of fog services. This article proposes a fault-tolerant model based on a Markov chain for a fog system’s performance optimization. The real-time reliability of fog nodes is analyzed by using dynamic distributed parameters. Thus, the state transition process of fog nodes is modeled with a continuous-time Markov chain. The steady-state probability of a fog system is analyzed. Then, a fault-tolerant strategy and its algorithms are designed to select nodes with the minimum cost based on their steady-state probabilities. The proposed method can predict the number of faulty ones of a fog system via the steady-state probability. An intelligent optimization method called simulated annealing (ISA) is designed and used to select the most appropriate fog nodes to substitute faulty ones. The experimental results show that the method is feasible and effective for selecting the right fault-tolerant nodes according to different performance requirements. ISA can well outperform such methods as random selection, discrete differential evolution, and simulated annealing in terms of cost and time.

Current 3D target detection methods used in the field of autonomous driving generally have low real-time performance and insufficient target context feature to detect dynamic multi-target accurately. In order to solve these problems, a dynamic multi-target detection algorithm of voxel point cloud fusion based on PointRCNN is proposed, which adopts a two-stage detection structure. The first stage directly processes the point cloud to extract key point features and divides voxel space. A novel submanifold sparse convolution is used to extract voxel features. Then key point features and voxel features of the point cloud are merged to generate pre-selection boxes. In the second stage, reference points are set based on the voxel features. The features of key points around reference points are merged for the second time to achieve optimized detection boxes. Finally, for the problem of inconsistent confidence, a mandatory consistency loss function is proposed to improve the accuracy of the detection box. The proposed algorithm was compared with other algorithms in three different datasets, and further tested on a self-made dataset from an actual vehicle platform. Results showed that the proposed algorithm had higher accuracy, better robustness, stronger generalization ability for dynamic multi-target detection.

Transactions in blockchain networks can be transmitted through channels. Existing methods for channel transaction transmission depend on transaction routing information. If a node randomly chooses a channel to transmit a transaction, the transmission may be aborted due to insufficient funds (also called balance) or low transmission rate. To increase transmission success rate and reduce transmission delay across all transactions, this work proposes a transaction transmission model for blockchain channels based on non-cooperative game theory. The model considers the channel balance, states, and nodes’ channel preferences. Steady-state transmission rates of selected channels are analyzed. The ideal solution for the model is found.

Due to its salient features, such as immutability and auditability, blockchain is becoming more integrated into the Internet of Things (IoT) for enhancing security and developing a decentralized IoT framework. However, different IoT applications require different transaction processing performance, which brings challenges to the convergence of blockchains in IoT. Moreover, the membership of a distributed IoT system may fluctuate when an IoT device joins or leaves the system. The dynamic nature of IoT systems also introduces new challenges for device management. Accordingly, we propose an application-oriented block generation (AOBG) scheme for blockchain-enabled IoT with dynamic device management and conditional traceability. Specifically, we first construct a framework for a consortium blockchain-based IoT system, including structures for application-oriented transactions and blocks, and consensus mechanism. We present different miners, respectively, for processing urgent and ordinary transactions adaptively with applications. Then, an AOBG protocol is proposed for this framework based on group signature. The group signature is used to achieve anonymity, traceability, and nonframeability. Combining time-bound keys in group signature with node accounts in blockchain, the proposed scheme can realize efficient transaction verification, dynamic device management, conditional traceability with data security, and privacy preservation. Extensive experiments demonstrate high efficiency of the proposed scheme.

In past decades, we witnessed many applications and fast development of cloud computing technologies. Cloud faults are encountered in a cloud computing environment. They badly impact users and cause serious economic losses in business. As a vital technology, fault detection can guarantee a high reliability cloud environment. However, fault detection with a fixed detection cycle has defects and shortcomings. On the one hand, for the service with good performance, if a small cycle is set, it may need a lot of system overhead due to unnecessary over detection; on the other hand, for the service with poor performance, if a large cycle is set, it may result in the omission of faults which should be detected. To address these issues, in this paper, a fault detection model is proposed to improve the detection accuracy based on support vector machine and a decision tree. For abnormal samples, their abnormality is calculated by using the model. We design algorithms to adaptively and dynamically adjust cycles for fault detection. The cycle is shortened if a system experiences many faults, thus increasing fault detection success rate; it is lengthened if the system runs without any problem, thereby reducing much computational overhead. Experimental results show that the proposed method outperforms two classical methods, i.e., one based on self-organizing competitive neutral network and the other based on a probabilistic neural network.

In the last ten years, cloud services provided many applications in various areas. Most of them are hosted in a heterogeneous distributed large-scale cloud computing environment and face inherent uncertainty, unreliability, and malicious attacks that trouble both service users and providers. To solve the problems of malicious attacks (including solo and collusion deception ones) in a public cloud computing environment, we for the first time propose a double-blind anonymous evaluation-based trust model. Based on it, cloud service providers and users are anonymously matched according to user requirements. It can be used to effectively handle some malicious attacks that intend to distort trust evaluations. Providers may secretly hide gain-sharing information into service results and send the results to users to ask for higher trust evaluations than their deserved ones. This paper proposes to adopt checking nodes to help detect such behavior. It then conducts gain-loss analysis for providers who intend to perform provider-user collusion deception. The proposed trust model can be used to effectively help one recognize collusion deception behavior and allow policy-makers to set suitable loss to punish malicious providers. Consequently, provider-initiated collusion deception behavior can be greatly discouraged in public cloud computing systems. Simulation results show that the proposed method outperform two updated methods, i.e., one based on fail-stop signature and another based on fuzzy mathematics in terms of malicious node detection ratio and speed.

With the widespread adoption of cloud computing, large-scale online applications composed of services have been deployed in many critical areas. In order to ensure the performance of cloud applications, Quality of Service (QoS) is a key indicator commonly used for service selection and adaptation. Previous studies have proposed collaborative QoS prediction approaches to estimate personalized QoS values. However, collaborative QoS prediction encounters privacy problems in practice. As a result, privacy threat has become a key challenge to make QoS prediction approaches practical. In this paper, we proposed a privacy-preserving QoS prediction approach employing federated learning techniques to tackle this grand challenge. We further improve the prediction efficiency by reducing system overhead and make the federated privacy-preserving QoS prediction approach feasible. The proposed approach is evaluated on a large-scale real-world QoS dataset, and the experimental results confirm its effectiveness and efficiency.

The emergence of channel technology reduces the transaction verification time of blockchains. A channel with a stable state is helpful for completing transactions successfully. Under the assumption of node bounded rationality and replication dynamics of an evolutionary process, this paper presents a dynamic evolutionary game model based on node behaviors in blockchain channels. The model considers the cost of attack, attack success rate, defense, cooperation, and noncooperation strategies during the game process. The defense strategy can help nodes resist different attacks. Nodes can dynamically adjust their own strategies according to different behaviors of attackers to achieve effective defense. The experimental results show that the proposed method is better than a lightning network channel in terms of transaction success ratio.

With the widespread adoption of the fifth-generation (5G) cellular network and Mobile Edge Computing (MEC), numerous Internet of Things (IoT) applications are emerging in many critical areas. IoT applications are typically running on mobile devices to provide real-time interaction with users by connecting with smart IoT devices and remote cloud services. In order to ensure the performance of IoT applications, Quality of Service (QoS) is commonly used as a key metric for the selection and adaptation of high-quality services at runtime. Collaborative QoS prediction methods have been proposed in the literature to predict personalized QoS values, enabling QoS-based selection and adaptation. However, privacy issues in collaborative QoS prediction discourage users from collaborating by sharing data in practice. Furthermore, there are untrusted users in the unreliable MEC environment, which makes the prediction encounter serious reliability issues. As a result, privacy and reliability issues have become key challenges to make QoS prediction approaches feasible. In this paper, we proposed a credible and privacypreserving QoS prediction approach by leveraging federated learning techniques and developing reputation mechanisms to address this critical challenge. We evaluate the method on a large- scale real QoS dataset and the experimental results demonstrate the effectiveness and efficiency of the method.

A cloud computing paradigm has quickly developed and been applied widely for more than ten years. In a cloud data center, cloud service providers offer many kinds of cloud services, such as virtual machines (VMs), to users. How to achieve the optimized allocation of VMs for users to satisfy the requirements of both users and providers is an important problem. To make full use of VMs for providers and ensure low makespan of user tasks, we formulate an optimal allocation model of VMs and develop an improved differential evolution (IDE) method to solve this optimization problem, given a batch of user tasks. We compare the proposed method with several existing methods, such as round-robin (RR), min-min, and differential evolution. The experimental results show that it can more efficiently decrease the cost of cloud service providers while achieving lower makespan of user tasks than its three peers. Note to Practitioners-VM allocation is one of the challenging problems in cloud computing systems, especially when user task makespan and cost of cloud service providers have to be considered together. We propose an IDE approach to solve this problem. To show its performance, this article compares the commonly used methods, i.e., RR and min-min, as well as the classic differential evolution method. A cloud simulation platform called CloudSim is used to test these methods. The experimental results show that the proposed one can well outperform its compared ones, and its VM allocation results can achieve the highest satisfaction of both users and providers. The proposed method can be readily applicable to industrial cloud computing systems.

As a new promising distributed technology, blockchains have been widely applied since its inception. Its decentralization feature reduces the reliance on the trusted authorities and third parties. It can well solve the problem of data being tampered and increase data sharing. However, a blockchain system faces various security and trust issues, such as attacks against consensus mechanisms and propagation processes, which may make it store malicious information or delay data propagation. The work discusses the basic architecture of blockchains as well as its potential security and trust issues at data, network, consensus, smart contract, and application layers. Then, the related literature work is analyzed in terms of the issues at these layers. Some open issues are presented and discussed.

Fault detection is a crucial technology to improve the performance of cloud systems. Its fixed detection cycle tends to be problematic since it faces high overhead if a small detection cycle is used for well-performing services; while risks missing many faults if a large cycle is adopted for some poorly-performing services. To solve such problems, an algorithm for adaptively adjusting dynamic detection cycle is proposed to decrease the overhead and increase fault detection performance in a cloud environment. It shortens a detection cycle for cloud systems with large fault probability, thus boosting fault detection performance. Otherwise, it increases it, thus decreasing the overhead. The algorithm is based on the proposed detection model by using a decision tree and support vector machine to increase detection performance. Experimental results show that the method is feasible and effective in comparison with some representative methods.

To maximize task scheduling performance and minimize nonreasonable task allocation in clouds, this paper proposes a method based on a two-stage strategy. At the first stage, a job classifier motivated by a Bayes classifier's design principle is utilized to classify tasks based on historical scheduling data. A certain number of virtual machines (VMs) of different types are accordingly created. This can save time of creating VMs during task scheduling. At the second stage, tasks are matched with concrete VMs dynamically. Dynamic task scheduling algorithms are accordingly proposed. Experimental results show that they can effectively improve the cloud's scheduling performance and achieve the load balancing of cloud resources in comparison with existing methods. Note to Practitioners- Task scheduling is one of the challenging problems in cloud computing, especially when deadline and cost are considered. As an important actuator, virtual machines (VMs) play a vital role for cloud task scheduling. To meet task deadlines, one needs to save the time of creating VMs, task waiting time, and executing time. To minimize the task execution cost, one needs to schedule tasks onto their most suitable VMs for execution. We propose a cloud task scheduling framework based on a two-stage strategy to do so. It precreates VMs according to historical scheduling data, therefore saving time for tasks to wait for creating VMs. It matches tasks with their most suitable VMs dynamically, therefore saving their execution cost. Under the premise of meeting task deadlines, it minimizes the waiting time of VMs to schedule tasks, thus minimizing the cost to be paid by users who utilize VMs. The readily deployable algorithms are designed and illustrated to improve cloud task scheduling and execution results in comparison with those using traditional methods.

Cloud computing has become an important scientific computing and commercial application paradigm. Many computing resources and data exist in clouds, and face various trust issues. Malicious providers may provide poor services to users, while malicious users may give good providers unfaithful trust evaluations. Hence, it is important to detect these malicious nodes that can be providers and users. Current studies on trust management do not sufficiently address the issues of minimizing trust management overhead and maximizing the ability to detect malicious nodes. This paper proposes a new trust model and related algorithm to decrease trust management overhead and improve malicious node detection ability based on domain partition. Partitioning nodes into domains is helpful for decreasing the overhead of trust management in terms of trust storage and computation. Domain and cross-domain sliding-windows are proposed and utilized to store the most recent trust values. Then, an algorithm is designed to compute domain and cross-domain trust values for nodes, and a filter procedure is adopted to remove malicious trust evaluations and malicious nodes from a domain. Simulation results show that the proposed model and algorithm outperform two updated methods, i.e., one based on fuzzy mathematics and another based on authenticated trust and reputation calculation and management, in terms of speed and accuracy of trust computation and malicious node detection.

This paper investigates several speedup techniques for a multiobjective integer linear program (ILP) used to obtain an optimal Petri net supervisor with a compressed structure for automated manufacturing systems (AMSs). An optimal supervisor can be obtained by forbidding all first-met bad markings and no legal markings of a plant net via place invariants. An iterative method to perform lexicographic multiobjective ILP is proposed to design such supervisor with a simple structure in terms of the numbers of control places and added arcs. Instead of a single ILP, several much smaller ILPs are formulated in the iterative method, and they can be solved much faster. To further reduce the ILP solution time, an efficient redundancy identification method is used. Finally, some AMS examples are provided to demonstrate the proposed speedup techniques and approaches.

The number of competing stations has great influence on the performance of IEEE 802.11 MAC protocol based on the distributed coordination function (DCF), which utilizes Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA). Some researchers try to use performance modeling to analyze and optimize the protocol, but the strict assumptions of the modeling often lead to research results that could not be adaptive to the dynamic change of competing station number, which is extremely prevalent in today's IEEE 802.11 networks. Some other researchers try to use filters, based on accurate measurement, to estimate the number of competing stations, and improve system performance by dynamically tuning the protocol parameters. However, such mechanisms are too complex to apply in real environment. Base on our discovery, we propose a simple adaptive optimization mechanism, DOOR (Dynamic Optimization on Range), for the IEEE 802.11 DCF, which is based on the subrange of competing station number. The reason, principle and method for partitioning subranges are introduced. Moreover, the detailed system model and performance evaluation for the new mechanism are given. The elaborate numerical results show that this mechanism could achieve much higher throughput and shorter delay than the standard IEEE 802.11 DCF in almost all the different competing stations numbers.

The dynamic character of complex and variable network environment, the key problem, puzzles the corresponding protocols design for wireless LANs. However, the distributed coordination function (DCF) of IEEE 802.11 MAC protocol, which is carrier sense multiple access with collision avoidance (CSMA/CA) using constant parameters, could not perform well when network environment changes. Thus many researchers try to optimize IEEE 802.11 DCF. However early dynamic optimization mechanisms for the protocol mostly depend on measuring the number of "competing" stations accurately. The problem of them is that the algorithms are too complex to apply in reality. In our research, we find that system performance approaches optimization with the same protocol parameters, when the number of competing stations changes within a certain range dynamically. Therefore, we propose a self-adaptive optimization mechanism, DOOR (dynamic optimization on range), for the IEEE 802.11 DCF. DOOR uses filter to estimate the range of competing station number and adjusts the protocol parameters to optimize system performance effectively. The detailed analytical model and performance evaluation for the new mechanism are given. Moreover, the measurement method and parameters of filter are introduced. At last, the elaborate numerical results show that our mechanism could not only achieve much higher throughput and lower delay than the standard IEEE 802.11 DCF along with the change of competing stations, but also improve the stability of system performance based on reasonably partitioned ranges.