Edge computing (EC) provides an efficient and low-latency computing architecture for mobile multimedia communications. Blockchain-enabled EC can offer enhanced security and data privacy protection in the system, whereas throughput remains a big concern for the blockchain. Sharding is a promising solution to increase the throughput at the cost of complex cross-shard transaction verification. The popular two-phase commit protocol (2PC) can ensure the consistency of cross-shard transaction processing. However, in the existing schemes based on 2PC, the number of intra-shard consensus invocations is proportional to the number of transactions, which imposes a great challenge on the system throughput and adaptivity improvement in sharding blockchains under dynamic transaction processing demands and capacities. In this article, we propose a transaction processing scheme based on 2PC, such that multiple transactions can be simultaneously processed in a batch during every execution of the consensus. Furthermore, we model the problem of transaction allocation to batches as a communication load balancing problem, aiming to balance the inter-shard communications within each batch under the shard processing capacity constraint. We also propose an effective Batch Transaction Processing algorithm (BTP) for the problem. Theoretical analysis proves that BTP is a 3-approximation algorithm for the communication load balancing problem. In the simulations and experiments on BlockEmulator, BTP respectively improves the system throughput and total transaction processing time by at least 29.41% and 22.64% over the state-of-the-art cross-shard transaction processing schemes, which demonstrates the superior adaptivity performance of BTP.

Sharding presents an enticing path toward improving blockchain scalability. However, the consensus mechanism within individual shards faces mounting security challenges due to the restricted number of consensus nodes and the reliance on conventional, unchanging nodes for consensus. Common strategies to enhance shard consensus security often involve increasing the number of consensus nodes per shard. While effective in bolstering security, this approach also leads to a notable rise in consensus delay within each shard, potentially offsetting the scalability advantages of sharding. Hence, it becomes imperative to strategically select nodes to form dedicated consensus groups for each shard. These groups should not only enhance shard consensus security but also do so without exacerbating consensus delay. In this article, we propose a novel consensus group selection based on transmission delay between nodes (CGSTD) to address this challenge, with the goal of minimizing the overall consensus delay across the system. CGSTD intelligently selects nodes from various shards to form distinct consensus groups for each shard, thereby enhancing shard security while maintaining optimal system-wide consensus efficiency. We conduct a rigorous theoretical analysis to evaluate the security properties of CGSTD and derive approximation ratios under various operational scenarios. Simulation results validate the superior performance of CGSTD compared to baseline algorithms, showcasing reductions in total consensus delay, mitigated increases in shard-specific delay, optimized block storage utilization per node, and streamlined participation of nodes in consensus groups.

Blockchain technology has garnered significant attention from academia and industry, with scalability remaining a key challenge. Sharding is a promising solution, dividing the blockchain into smaller partitions called shards, each processing a portion of the transactions to increase throughput. This approach is critical for enabling efficient Proof of Stake (PoS) consensus mechanisms, as demonstrated by the transition of Dogecoin to PoS, where sharding reduces the computational burden on validators and enhances scalability. However, sharding introduces high storage redundancy, as nodes in each shard must collectively maintain a copy of the entire blockchain, imposing substantial storage pressure. To address this, segments are introduced to divide the main chain into smaller parts distributed across nodes. Existing methods, however, randomly assign segments to nodes, resulting in high costs for node setup and segment queries. This paper investigates the optimal allocation of segments within shards to minimize these costs, proposing a Segment Allocation algorithm based on Cost Clustering (SACC). Theoretical analysis and simulations demonstrate that SACC achieves lower setup, query, and total costs while maintaining security and scalability, offering a more efficient solution for sharding-based PoS blockchains like Dogecoin.

Summary Vehicular edge computing (VEC) is envisioned as a promising approach to process explosive vehicle tasks, where vehicles can choose to upload tasks to nearby edge nodes for processing. However, since the communication between vehicles and edge nodes is via wireless network, which means the channel condition is complex. Moreover, in reality, the arrival time of each vehicle task is stochastic, so efficient communication methods should be designed for VEC. As one of the key communication technologies in 5G, non‐orthogonal multiple access (NOMA) can effectively increase the number of simultaneous transmission tasks and enhance transmission performance. In this article, we design a NOMA‐based task allocation scheme to improve the VEC system. We first establish the mathematical model and divide the allocation of tasks into two processes: the transmission process and the computation process. In the transmission process, we adopt the NOMA technique to upload the tasks in batches. In the computation process, we use a high response‐ratio strategy to determine the computation order. Then we define the optimization objective as maximizing task completion rate and minimizing task energy consumption, which is an integer nonlinear problem with lots of integer variables and cannot be solved directly. Through further analysis, we design a heuristics algorithm which we name as the AECO (average energy consumption optimization) algorithm. By using the AECO, we obtain the optimal allocation strategy by constantly adjusting the optimal variables. Simulation results demonstrate that our algorithm has a significant number of advantages.

With the booming of Internet of Things (IoT), a content provider (CP) traditionally supported by the storage resources of a network service provider (NSP) can provide content services through the resources of IoT devices to significantly reduce the service latency. The CP, NSP, and IoT devices constitute an edge caching system, and it is crucial to efficiently utilize the storage resources in the system. Most existing studies ignore the idle storage resources of IoT devices. The few studies that consider the storage resources of IoT devices either are based on the assumption of complete information, or only utilize the storage resources of part of the IoT devices. In this article, we propose a contract theory and Stackelberg game-based storage resource allocation method to effectively utilize the storage resources in edge caching systems under information asymmetry. The interaction between the CP and the IoT is formalized as a contract design problem, and the interaction between the NSP and the CP is formulated as a two-stage Stackelberg game with a single leader and a single follower. We analyze the constraints in contract design and the Nash equilibrium of the Stackelberg game. We also propose a golden section search-based optimal contract design and pricing (GSSCP) algorithm to obtain the optimal contract of the CP and optimal price of the NSP storage resources. Simulation results demonstrate that the proposed method can make effective use of the storage resources and improve the CP utility in edge caching systems under information asymmetry.

Sharding is a promising strategy to enhance blockchain scalability. However, the surge in transactions has led to heightened relations between nodes in the system, reflecting the volume of transactions between them. The increase in related nodes engaging in identical transactions across diverse shards leads to substantial cross-shard transactions, contributing to communication delays and impeding enhancements in throughput. Current methods typically employ greedy or heuristic approaches to organize nodes into shards, resulting in marginal reductions in the total relation between related nodes in different shards (i.e., the number of cross-shard transactions), while causing shard imbalance. Hence, there is a crucial need for periodic shard reorganization based on node relations to minimize the total relation between related nodes across different shards while ensuring shard balance. In this article, we investigate the reorganization of nodes into shards based on node relations in sharding blockchains, aiming to minimize the total relation between related nodes in different shards. We formulate the shard reorganization problem and introduce the shard reorganization algorithm based on the relation between nodes (SRRN) to address this issue. Theoretical analysis proves that SRRN is a$2\lambda M$-approximation algorithm, where$\lambda=({r_{\max}}/{r_{\min}})$, with$M$representing the number of shards, and$r_{\max}$and$r_{\min}$denoting the maximum and minimum nonzero relations between nodes, respectively. Simulation results demonstrate that SRRN outperforms baseline algorithms in terms of total relation, degree of relation reduction, differences in computing power between shards, cross-shard ratio, and throughput.

Several deep learning methods have emerged for fault diagnosis of industrial equipment in recent years. However, the realistic dataset is often much smaller than the benchmark diagnostic dataset due to the difficulty of fault data collection and labeling in realistic scenarios. Moreover, the collected fault data may come from various components with different fault categories. Therefore, existing deep-learning-based models have poor generalization capabilities for cases with only a few data when faced with new components. Herein, a triplet relation network (TRNet) is proposed for cross-component few-shot fault diagnosis by learning from several related meta-tasks iteratively. We construct a dual-channel feature embedding module with shared weights to extract fault features and a relation metric module to adaptively measure the feature similarity of sample pairs. Furthermore, in order to distinguish the most dissimilar samples in the same category (i.e., hard positive samples) and the most similar samples in different categories (i.e., hard negative samples), the hard sample recognition module is designed, combined with a triplet loss, to weaken the hard-to-discriminate feature of hard sample pairs, such that the TRNet are capable for task learning and feature learning to improve classification accuracy on target components. We conduct experiments on two publicly available datasets and one lab-built datasets. We validate the proposed method to classify with one, three, or five instances in each category of the target component. The results demonstrate that the fault diagnosis performance of our model is superior to the state-of-the-art methods.

Due to less degradation data and the inconsistent data distribution of different bearings, remaining useful life (RUL) prediction methods based on deep learning still do not yield satisfactory predictive results. Using RUL prediction model trained with one bearing sample but tested with another bearing sample is challenging. To solve this problem, in this paper a new deep transfer learning-based RUL prediction method (DTL-RULPM) is proposed. We adopt min-max normalization to normalize the original vibration data of bearing. A three-layer sparse autoencoder is designed to extract the deep features of the source domain. Random data with standard normal distribution is generated with the consistent dimension of the high-dimensional features of the source domain. Maximum mean discrepancy (MMD) is used to minimize the probability distribution distance between the features of the source domain and the randomly generated data, such that the model can learn domain-invariant features of different bearings. Then we adopt a bi-directional long and short-term memory (Bi-LSTM) network to predict the RUL of the bearing. We use the IEEE PHM Challenge 2012 dataset to verify the proposed method. The results demonstrate that the proposed method improves the RUL prediction accuracy and robustness of different bearings.

The compound fault signal of bearings is coupled and complex, thereby compound fault diagnosis is a difficult problem in bearing fault diagnosis. The existing deep learning models can extract fault features when there are a large number of labeled compound fault samples. In the industrial scenarios, collecting and labeling sufficient compound fault samples are unpractical. Using the model trained on single fault sample to identify unknown compound fault is challenging and innovative. To address this problem, we propose a Zero-shot Learning Compound Fault Diagnosis Model of bearing (ZLCFDM). First, we design a semantic encoding method to express the semantic vectors of single fault and compound fault according to the fault characteristics. Second, a convolutional neural network is designed to extract the time-frequency visual features of compound fault signal. Then we embed the semantic vector of the fault into the visual space of the fault data. The cosine distance is merged into K-nearest neighbor (KNN) to measure the distance between the visual features and the semantic vectors of the compound faults, such that the model can identify the categories of unknown compound faults. To validate the proposed method, we conduct experiments on self-built testbed. The results demonstrate that the identification accuracy of compound fault can reach 77.73% when the model trained without any compound fault samples. This is the first time to propose the compound fault diagnosis of bearing base on zero-shot learning.

Blockchain can provide a dependable environment for the Internet of Things (IoT), while the high computing power and energy required by blockchain hinder its applications in IoT. Offloading the computation at the resource-limited IoT devices to a cloud/edge computing service provider (CESP) is a feasible solution to the execution of computation-intensive blockchain tasks. The CESP provides computing resources to IoT users with a cloud and multiple edge servers that work collaboratively such that the users are able to perform mobile blockchain services. Resource allocation and pricing of computing resources at the cloud/edges have a significant impact on the revenues of CESP and users. Most of the existing works on the cooperative edge-cloud for computation offloading assumes that a user is mapped to a prespecified edge server or the cloud. However, the CESP may choose a server from either the edge servers or the cloud to run the offloaded tasks by jointly considering the cost and income of the service provisioning. In this article, we formulate a Stackelberg game with CESP as the leader and users as the followers for cloud/edge computing resource management. We prove the existence of Stackelberg equilibrium and analyze the equilibrium. We then model the resource allocation and pricing at the CESP as a mixed-integer programming problem (MIP) with the objective to optimize the CESP's revenue and propose an efficient iterative greedy-and-search-based resource allocation and pricing algorithm (IGS). The algorithm solves two subproblems comprising the CESP's revenue optimization problem: resource allocation under a given resource price and resource pricing based on a specified resource allocation scheme. The first subproblem evaluates where to execute the computing tasks via a greedy-and-search-based approach, whereas the second subproblem estimates the resource price through golden section search. We conduct experiments through simulations. Simulation results show that the proposed algorithm can effectively improve the revenue of both the CESP and the IoT terminals.

Internet users receive various online social networks (OSNs) services, however, providers of OSNs do not always provide users fine-grained privacy protection mechanisms with sufficient privacy protection for shared resources. In this paper, we propose a formal Group-Based Access Control (oGBAC) framework for preventing privacy disclosure when sharing information within or among groups in OSNs. Our framework extends the group-centric Secure Information Sharing (g-SIS) models by adapting the concept of the group to OSNs. We impose some restrictions to the group and information flow among groups to ensure that operations cannot incur privacy disclosure when sharing information among friends in OSNs. In view of characteristics of OSNs and the requirements of secure information flow, the oGBAC model also incorporates some ideas from the Attribute-Based Access Control (ABAC) to develop information flow based rules using relationship among attributes (such as tags, time and security levels) of objects and subjects in OSNs. Administration related rules and access related rules are designed for each access operation of group based OSNs' information sharing. The security of oGBAC model is analyzed using formal methods. To demonstrate the usability of the oGBAC model, we implement the model with the Comparative Attribute-Based Encryption (CCP-CABE), and analyze the security and efficiency of the implemented system to prove the effectiveness of the implemented system.

With the explosive growth in the scale of cloud computing infrastructures, reliability and energy efficiency have become important concerns considering the great complexity of cloud data centers. There is an urgent need for efficient task assignment that can dispatch tasks to appropriate cloud data center servers, which is critical to achieve reliability and energy efficiency in current cloud data centers. Most of the research on task assignment focuses on only one of the objectives of reliability and energy efficiency, while the two objectives are intrinsically conflicting with each other. In this paper, we deal with the problem of task assignment in data centers, with the objective of minimizing the energy consumption while providing failure tolerance to task execution failure. We propose a reliability-aware and energy-efficient task replica assignment algorithm based on running task replicas at a low speed and enabling multiple task replicas to share the same server resources. Each task in a job processed by the cloud computing platform has two instances: main task and task replica (shadow). Each main task runs on an individual server, and the task replica associated with the main task is assigned on a different server. The main tasks run at the full server speed, while the task replicas run at a lower rate than the main tasks. The task replicas can be mapped onto dedicated backup servers or be assigned to the servers on which the main tasks are running. Multiple task replicas can share the same server resources to reduce the number of servers required. We conduct experiments through simulations. Experimental results demonstrate that the proposed algorithm can effectively reduce the energy consumption, while achieving a good balance between the number of servers used and job completion time.

With the emergence and development of various computer technologies, many jobs processed in cloud computing systems consist of multiple associated tasks which follow the constraint of execution order. The task of each job can be assigned to different nodes for execution, and the relevant data are transmitted between nodes to complete the job processing. The computing or communication capabilities of each node may be different due to processor heterogeneity, and hence, a task scheduling algorithm is of great significance for job processing performance. An efficient task scheduling algorithm can make full use of resources and improve the performance of job processing. The performance of existing research on associated task scheduling for multiple jobs needs to be improved. Therefore, this paper studies the problem of multijob associated task scheduling with the goal of minimizing the jobs’ makespan. This paper proposes a task Duplication and Insertion algorithm based on List Scheduling (DILS) which incorporates dynamic finish time prediction, task replication, and task insertion. The algorithm dynamically schedules tasks by predicting the completion time of tasks according to the scheduling of previously scheduled tasks, replicates tasks on different nodes, reduces transmission time, and inserts tasks into idle time slots to speed up task execution. Experimental results demonstrate that our algorithm can effectively reduce the jobs’ makespan.

MapReduce jobs need to shuffle a large amount of data over the network between mapper and reducer nodes. The shuffle time accounts for a big part of the total running time of the MapReduce jobs. Therefore, optimizing the makespan of shuffle phase can greatly improve the performance of MapReduce jobs. A large fraction of production jobs in data centers are recurring with predictable characteristics, and the recurring jobs split the network into periodic busy and idle time slots, which allows us to better schedule the shuffle data in order to reduce the makespan of shuffle phase with the future predictable network status available. In this paper, we formulate the shuffle scheduling problem with the aim to minimize the makespan of MapReduce shuffle phase by leveraging the predictable periodic network status. We then propose a simple yet effective network-aware shuffle scheduling algorithm (NAS) to reduce the number of idle time slots required to transfer the shuffle data so as to reduce the shuffle makespan. We also prove that the proposed algorithm NAS is a 3/2-approximation algorithm to the shuffle scheduling problem when all the future idle time slots have the same duration. We finally conduct experiments through simulations. Experimental results demonstrate the proposed algorithm can effectively reduce the makespan of MapReduce shuffle phase and increase network utilization.

Cloud computing has emerged as an increasingly indispensable and highly demanded platform for various applications, as cloud computing allows for on demand resource provisioning and allocation. The associated tasks composing a job processed by cloud computing need to be executed under ordering constraints for correctness or consistency. The associated tasks are executed on different servers and communication is required to transfer the data between the servers, while the processing capacity of and the communication capacity between different components underlying the cloud computing platform may show great heterogeneity. Therefore, efficient scheduling for the associated tasks is critical for achieving high performance in cloud computing systems. In this paper, we tackle the problem of associated task scheduling for cloud computing with the aim to minimize the makespan of the job, when excessive diversities are present in the computing and communication components. We propose a Dynamic Priority List Scheduling (DPLS) algorithm based on dynamic task finish time prediction. The algorithm dynamically predicts the remaining execution time for each task to be scheduled according to the server allocation of the previously scheduled tasks, and decides the next task to be scheduled and the server allocation based on the previously task scheduling result. We conduct experiments through simulations on randomly generated associated tasks and real-world applications. Experimental results demonstrate that the proposed algorithm is promising.

Digital image steganalysis is the art of detecting the presence of information hiding in carrier images. When detecting recently developed adaptive image steganography methods, state-of-art steganalysis methods cannot achieve satisfactory detection accuracy, because the adaptive steganography methods can adaptively embed information into regions with rich textures via the guidance of distortion function and thus make the effective steganalysis features hard to be extracted. Inspired by the promising success which convolutional neural network (CNN) has achieved in the fields of digital image analysis, increasing researchers are devoted to designing CNN based steganalysis methods. But as for detecting adaptive steganography methods, the results achieved by CNN based methods are still far from expected. In this paper, we propose a hybrid approach by designing a region selection method and a new CNN framework. In order to make the CNN focus on the regions with complex textures, we design a region selection method by finding a region with the maximal sum of the embedding probabilities. To evolve more diverse and effective steganalysis features, we design a new CNN framework consisting of three separate subnets with independent structure and configuration parameters and then merge and split the three subnets repeatedly. Experimental results indicate that our approach can lead to performance improvement in detecting adaptive steganography.

Large-scale Internet applications provide service to end users with servers, which may be located at geographically distributed data centers. Users may require different delay constraints for different services. To meet the service delay requirements to end users, the data centers must provide enough server resources which incur a large amount of electricity and dollars cost. In this paper, we tackle the problem of minimizing electricity cost under diverse delay requirements of different services for different users in a multi-electricity-market environment. We propose two algorithms to reduce the electricity cost, taking into account the location diversity and the time diversity of electricity price. Our simulation results demonstrated that the proposed algorithms were effective in terms of the reduction of the electricity cost while satisfying the diverse delay constraints.