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Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To solve the problem that the edge devices deployed by different operators have huge investment and the cached contents are isolated and have difficulty in sharing information, this paper improved the architecture of an edge cache system based on consortium chain, so that the edge devices deployed by different operators could break the content isolation and achieve a wider range of content sharing. In order to improve the operator cache revenue while ensuring the user transmission quality and reducing the user transmission delay, this paper firstly analyzed the impact of different content delivery methods on user transmission delay and operator caching benefits in terms of cache popularity, content size, and edge device collaboration capabilities. Then, it established an optimization problem with the goal of maximizing the cache benefit and minimizing the transmission delay. Finally, to solve the proposed high-dimensional large-scale caching decision problem, it used asynchronous advantage actor-critic (A3C) based content caching algorithm to determine optimal placement of content. Simulation results show that the proposed cache strategy can effectively enhance the revenue of edge caching, reduce the delay of content delivery and improve the user experience. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the credibility of edge nodes in the Internet of Vehicles could not be guaranteed, this paper proposed a reputation-based task offloading and resource allocation model for the Internet of Vehicles, and used the reputation of edge nodes recorded on the blockchain to evaluate its credibility, so as to help the terminal devices select reliable edge nodes for task offloading. At the same time, this paper modeled the offloading strategy as the time delay and energy consumption minimization problem under the reputation constraint, and used the multi-agent deep deterministic policy gradient algorithm to solve the approximate optimal solution of the NP-hard problem. The edge server received rewards based on the completion of task offloading, and then updated the reputation recorded on the blockchain. Simulation experiments show that the proposed algorithm reduces in terms of time delay and energy consumption by 25.58% to 27.44% compared with the benchmark testing schemes. [ABSTRACT FROM AUTHOR]

This paper aimed to optimize the node selection problem in existing oracle machine schemes in the blockchain environment in order to improve the accuracy and reliability of oracle machine node selection. This paper introduced ORLM, a deep reinforcement learning based middleware for blockchain prophecy machine node selection. This middleware considered the consumption of multiple nodes under different service demands and modeled the reputation value of the prophecy machine nodes to sick the reputation value of the oracle machine data-providing nodes, thus avoiding the selection of nodes with malicious history as much as possible. With the deep reinforcement learning DQN algorithm, the middleware is able to optimize the process of selecting nodes for better node selection with security. The experimental results show that the oracle middleware is able to better satisfy the service requests of users. And it has high scalability and availability. It proves that the introduction of deep reinforcement learning to optimize the oracle node selection is a feasible direction. [ABSTRACT FROM AUTHOR]

Blockchain technology has great potentiality in the secure sharing of electronic health records. However, current solutions face issues such as large storage space and low consensus efficiency. This paper proposed a novel scheme based on blockchain and friend and relative nodes. Firstly, this paper adopted a distributed file system to store users’ complete electronic health records on offline servers, and constructed a set of user’s friend and relative nodes. Each user’s data was stored redundantly by its friend and relative nodes, and only the block’s header information was stored on the chain, reducing storage space. Then, this paper designed a data access mechanism based on multi-signature technology, using Shamir secret sharing mechanism to distribute user’s private keys to their relatives and friends. More than half of the relatives and friends could use their own private keys to reconstruct users’ private keys, solving data access problems in emergency situations. Finally, this paper proposed a credit based Byzantine fault-tolerant consensus mechanism, which used the top 10% nodes with credit values as the leader node set and utilized a random algorithm to generate the master node in the leader node set. And it improved consensus security. The experimental results show that compared with existing methods, the proposed scheme reduces the on-chain storage space by 1/7, improves data access efficiency by 15 times, and has higher consensus security while ensuring good latency and throughput. The proposed scheme can achieve secure and efficient sharing of electronic health records, providing a case for the application of blockchain in the medical field. [ABSTRACT FROM AUTHOR]

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Conventional centralized image classification methods faced challenges due to data privacy issues and limitations in computing resources, rendering them inadequate for practical applications. Existing federated learning frameworks relied on central servers, and there were security challenges such as single points of failure and data poisoning attacks. To address these issues, this paper proposed a novel image classification scheme that combined privacy-preserving federated learning and blockchain technology. The scheme achieved reliability and security in image classification tasks within a distributed environment. This approach trained the image classification model through federated learning and uploaded to the blockchain network for verification and consensus. During the classification phase, the model obtained the final classification result through weighted combination. Experimental results demonstrate that the proposed scheme ensures the accuracy of image classification while protecting user privacy. In conclusion, this paper provides an effective approach to address data privacy and security concerns in image classification, and presents a positive exploration towards improving classification accuracy. [ABSTRACT FROM AUTHOR]

Federated learning is a revolutionary deep learning model, and it enables users to train the global model cooperatively without exposing their private data. However, some malicious clients can lead to the risk of single point of failure and privacy disclosure, this poses a serious threat to the security of federated learning. In response to the above issues, based on the existing research, this article proposed a blockchain empowered multi edge federated learning model. Firstly, this paper proposed to use blockchain instead of central server to enhance the stability and reliability of model training process; Secondly, this paper proposed a consensus mechanism based on edge computing to achieve a more efficient consensus process; In addition, incorporating reputation assessment into the federated learning training process can transparently measure the contribution value of each participant and standardize the behavior of work nodes. Finally, comparative experiments show that the scheme can maintain high accuracy in the malicious environment, and can resist higher malicious ratio compared with the traditional federated learning algorithm. [ABSTRACT FROM AUTHOR]

Public chains face numerous problems of malicious transactions and illegal cryptographic activities because of their transparency and openness, which cause anomalous transactions in blockchains and cause serious damage to users’ assets and information security. To address the problem of blockchain abnormal transactions, this paper proposed a blockchain abnormal transaction detection method based on the fusion of GAT (Graph Attention Networks) and SVM (Support Vector Machine), which focuses on the features and connections of the local structure of the blockchain transaction graph neighbor nodes--GAS (Graph Attention networks and Support vector machine). In GAS, this paper utilize the Random Forest to evaluate the importance of transaction data features of nodes, and select the top 140important features in descending order. Then, combined with the features of neighboring nodes, it use GAT to update the features of the current node. The updated features serve as input to SVM for anomaly detection. Experimental results demonstrate that compared to non-integrated methods, GAS shows superior performance in detecting anomalies, with an accuracy rate of 98.11%, precision of 94.01%, and recall rate of 85.48%. [ABSTRACT FROM AUTHOR]

Applying blockchain technology to the cloud manufacturing platform (CMfg) endowed it with features such as decentralized resource management, distributed data storage, and platform robustness. However, during the implementation of service optimization composition, challenges are encountered, including the need for centralized resource scheduling, data entry dependence on the moment of block generation, and a lack of dynamic adaptability in the execution scheme. To address this, this paper proposed a solution in the form of cloud dual-chain oriented to service optimization composition. The cloud dualchain comprised a manufacturing service chain (MSC) and a process data chain (PDC). It segregated the management of service resources and composition scheme into distinct chains, allowing the CMfg to reconcile blockchain characteristics with real-world service collaboration needs. The manufacturing service within the cloud dual-chain was self-managed by the enterprise, and the MSC chain was formed by smart contracts based on task orders, balancing distributed and centralized management. The PDC, generated based on service composition, recorded process data in parallel. In case of service failure, a new service was invoked in the corresponding MSC node, updating the information in the PDC node to ensure dynamic adaptability of the execution plan. Experimental results demonstrate that cloud dual-chain facilitates flexible service management, distributed parallel entry of process data, and provides dynamic adaptability to execution scheme. Cloud dual-chain can effectively support the development of CMfg. [ABSTRACT FROM AUTHOR]

The consensus mechanism is a critical component of blockchain technology. To address the issue of delayed or inef- fective handling of malicious nodes in the DPoS consensus mechanism, this paper proposed an improvement plan for the DPOS consensus mechanism based on SVM (SVM-DPoS). This approach firstly established a node discrimination model based on SVM, which analyzed the behavior motivations of nodes through a well-trained model, allowing for the timely elimination of malicious nodes based on discrimination results. Furthermore, it optimized the traditional block production process based on a fixed negotiated block order, enhancing the block production efficiency and consequently improving the overall operational effi- ciency of the blockchain network. Experimental results on public datasets indicate that compared to existing methods, the im- proved consensus mechanism can quickly eliminate malicious nodes. Not only does it maintain system stability, but it also strengthens the defense against malicious activities, thus ensuring the normal operation of the blockchain network while enhan- cing the overall security of the consensus. [ABSTRACT FROM AUTHOR]

The task of fraud detection in blockchain requires a thorough exploration of the inherent temporal and spatial characteristics in historical transaction data. Existing fraud detection methods suffer from large prediction errors. To address this issue, this paper proposed a blockchain fraud detection method, named RGCN-SRG, based on reinforced graph convolutional network (RGCN) and spatiotemporal recurrent gate (SRG). Firstly, leveraging Bitcoin's historical transaction data for the construction of the transaction graph, the method used a reinforced graph convolutional network with different kernel sizes to comprehensively extract the graph's topology information and generate feature vectors. Additionally, considering the temporal characteristics of blockchain transactions, the method introduced a spatiotemporal recurrent gate structure that incorporated graph convolutional operations into the traditional gate structure to extract dependency information from multiple spatiotemporal dimensions of the transaction graph. Finally, it obtained the prediction results of money laundering detection through a linear layer and activation function. The proposed fraud detection method was evaluated by the constructed dataset. Compared with GCN, DEDGAT, EGT and GCN + MLP F, by the proposed method improves 18.4, 10.7, 9.2 and 4.9 percentage points, respectively; the precision improves 11.5, 11.2, 7.7 and 3.7 percentage points, respectively. [ABSTRACT FROM AUTHOR]

The advent of federated learning has unveiled a collaborative approach to learning across data islands, yet its scalability faces challenges from Non-IID local data at federated nodes and insufficient oversight, accountability, and privacy in centralized frameworks. To tackle these issues, this paper proposed a blockchain-based trustworthy slice aggregation (BBTSA) and federated attribution optimization method (FedAom). FedAom adopted attribution thinking, using the integrated gradients method for attribution to identify parameters influencing model decisions. It considered parameter sensitivity in a tiered approach, preserving and enhancing key knowledge learned by the global model during local updates. This effectively utilized shared data to mitigate the Non-IID problem. Concurrently, BBTSA employed blockchain to establish a decentralized framework, facilitating noise exchange between nodes instead of direct parameters, with a cooperative tree structure for obfuscation to boost privacy. Validation on diverse distributions across two datasets indicates FedAom notably improves stability and convergence speed over baseline methods in most instances. Simultaneously, BBTSA ensures client privacy without compromising model accuracy, guaranteeing training oversight and privacy protection. [ABSTRACT FROM AUTHOR]

Traditional centralized federated learning relies on a trusted central server for model aggregation, creating a vulnerability to single-point failures. In contrast, existing decentralized federated learning schemes elect a node temporarily in each iteration cycle to aggregate the model, but cannot ensure the complete trustworthiness of the elected node. To solve the aforementioned issues, this paper proposed a blockchain-based federated learning model aggregation approach that assigned the task of model aggregation to numerous miners instead of a single node. Miners proposed various candidate aggregation solutions and generated corresponding blocks, then it determined the main chain based on the highest accuracy chain principle to achieve consensus among nodes. Additionally, to counteract malicious training nodes, it introduced a training node selection mechanism based on staking training coins, allowing nodes to participate in training by staking training coins, with the system rewarding or penalizing them based on their contribution to the model. Simulation results demonstrate that with 10%, 20% and 30% malicious nodes in the system, the accuracy of this approach is respectively 8.64, 19.89, and 22.93 percent point higher than that of the federated averaging (FedAvg) scheme, and it also performs well in non-IID data training scenarios. In conclusion, this approach enhances the credibility of the federated learning aggregation process and ensures the effectiveness of federated learning training. [ABSTRACT FROM AUTHOR]

To make full use of heterogeneous nodes data from IIoT to train high-accuracy models while protecting data privacy, this paper proposed a privacy-preserving two-stage collaborative learning system based on blockchain. Firstly, it used a grouped federated learning framework to divide participating nodes into different groups based on their computing power. Each group trained a global model suitable for its computing power through federated learning. Secondly, it introduced split learning to enable nodes to collaborate with mobile edge computing servers to train a larger scale model, and used differential privacy technology to further protect data privacy. It stored trained models on the blockchain, and used the consensus algorithm of the blockchain to further prevent attacks from malicious nodes and protect the security of the model. Finally, to combine the advantages of multiple heterogeneous global models and further improve model accuracy, it used the feature extractor of each global model to extract features from user data, and used these features as training datasets to train a higher accuracy complex model. Experimental results show that the performance of system on Fashion-MNIST and CIFAR-10 datasets is better than the performance of traditional federated learning. It is suitable for obtaining high-accuracy models in IIoT scenarios. [ABSTRACT FROM AUTHOR]

To address the lack of transparency in traditional manufacturing supply chain management, the vulnerability to data tampering, and the insufficient traceability capabilities, which result in a lack of trust between supply chain organizations and increased opportunity costs, this paper designed the TMSCT framework by using blockchain technology. Firstly, it designed a master-slave chain collaboration mechanism to enable trustworthy collaboration while protecting privacy. Secondly, it implemented a hybrid on-chain and off-chain storage mode to alleviate storage pressure on distributed nodes. Finally, it constructed a B-Merkle tree based on Bloom filter and Merkle tree to quickly determine if the current block contained the target data. Experimental results demonstrate that the B-Merkle tree effectively reduces the average processing latency of query requests. The proposed framework outperforms existing solutions in terms of data existence verification, privacy protection, flexibility, and alleviating storage pressure on nodes. [ABSTRACT FROM AUTHOR]

This paper proposed a distributed video streaming fair scheduling strategy based on federated deep reinforcement learning to address the issues of unfair user QoE and low bandwidth utilization caused by uneven video bandwidth allocation in bottleneck links. This strategy dynamically generated bandwidth allocation weights based on the client's network status and the QoE level of each video stream. The congestion control algorithm at the server side allocated bandwidth to each video stream in the bottleneck link according to the computed weights, ensuring equitable transmission of video streams in the bottleneck link. Each video terminal operated a bandwidth allocation agent, and multiple agents train periodically using federated learning to facilitate rapid convergence of the agent models. The bandwidth allocation agents synchronized their training parameters through a consensus mechanism, enabling distributed aggregation of the agent model parameters while ensuring the security of parameter sharing. Experimental results demonstrate that the proposed strategy improves QoE fairness and overall QoE efficiency by 10% and 7%, respectively, compared to the latest solutions. This indicates that the proposed strategy has potential and effectiveness in addressing the uneven allocation of video stream bandwidth and improving user experience. [ABSTRACT FROM AUTHOR]

Existing blockchain content supervision schemes adopt ex-post governance methods, lack of pre-audit, and have problems of signature invalidity and low efficiency of multi-version block verification. To address these problems, firstly, this paper designed a dynamically adjustable and accountable data audit method to realize prior audit of blockchain transaction data. Secondly, it designed a redaction-controlled digital signature scheme to solve the problem of signature invalidity caused by editing operations. Finally, it designed a blockchain data consistency verification protocol to achieve efficient verification of query results. The results of security analysis and performance testing demonstrate the security and effectiveness of the scheme. In the case of controllable supervision, the scheme still maintains high efficiency in block generation and verification, and it provides a new solution for blockchain content supervision. [ABSTRACT FROM AUTHOR]

Considering that the problems of low efficiency and credibility in traditional honey traceability, this study constructed a solution that combines blockchain technology and InterPlanetary File System technology to establish a highly reliable honey traceability system after analyzing the honey traceability standards. Firstly, this study introduced a hierarchical storage strategy to enhance the efficiency of on-chain information and traceability; Subsequently, this approach employs a multi-chain architecture to isolate data. It utilizes a proxy re-encryption scheme based on external random oracles, whereby ciphertext re-encryption is executed through smart contracts. Simultaneously, message authentication codes are integrated to effectively address challenges such as low information credibility, forgery, and potential privacy data leaks. Lastly, this paper proposed a pre-commit mechanism to prevent inconsistencies in data caused by sudden loads, that is, the scheme of "high-traffic package pre-commit, low-traffic supplementary on-chain".To assess the feasibility of the proposed solution, simulations and tests were conducted using Apache Bench tools. The results demonstrate that the scheme effectively achieves the chaining of basic data, controllable supervision, and credible traceability. Furthermore, it fulfills the multi-level requirements of enterprises, regulatory authorities, and users. [ABSTRACT FROM AUTHOR]

The throughput of blockchain systems is severely insufficient, and the most effective solution to this problem is parallelization processing. The parallelization scheme is mainly a star architecture. Currently, the star architecture mostly uses account random sharding for node sharding in the system, and the system throughput of this sharding method is still insufficient. In response to this issue, this paper proposed a TKM sharding algorithm based on star structure, which improved the original K-means clustering algorithm and applied it to node sharding. The TKM sharding algorithm combined clustering algorithm with blockchain network sharding technology, allowing nodes to be sharded based on geographical location, greatly increasing the probability of transactions between neighboring nodes being intra chip transactions, thereby improving system throughput. At the same time, it introduced time stamps on the basis of the original algorithm to reduce attacks from malicious nodes. Simulation experiments show that this algorithm improves the maximum system throughput by 20% compared to traditional random sharding algorithms. Based on the above flux model, the optimal number of shards for the star blockchain system based on the TKM algorithm is obtained through experiments. [ABSTRACT FROM AUTHOR]

Aiming at the problems in the Practical Byzantine Fault Tolerance Algorithm (PBFT), such as large communication overhead, high delay and inability to reasonably divide according to the differences of scenarios and devices to meet the multi-scenario application of Internet of Things (IoT) in the integration of IoT and blockchain, this paper proposed an improved Practical Byzantine Fault Tolerance consensus algorithm based on Comprehensive Evaluation (CE-PBFT). Firstly, it conducted comprehensive evaluation of nodes based on the weighted performance and reputation value to screen out nodes that meet the needs of specific scenarios; after that, it conducted clustering based on the comprehensive evaluation of nodes to form a two-layer network architecture; finally, it divided the consensus process into sub-cluster consensus and main cluster consensus. The experimental results show that CE-PBFT has high fault tolerance and scenario adaptability, and when the number of scenario nodes reaches 100, it respectively has 93.9% and 87.8% performance optimisation over PBFT in terms of communication overhead and consensus delay. [ABSTRACT FROM AUTHOR]

With the continuous promotion of the national reform of the power market and the optimization of the power sales market, the microgrid will become an important carrier for the future development of electric energy and the reform of power grid operation and management. In order to improve the order matching efficiency of producers and consumers as well as economic benefits, this paper firstly proposed a secure transaction of microgrid electricity based on blockchain technology, which utilized dual chains to complete the authentication of the information of both parties in the transaction and proposed a pricing strategy based on the cooperative game of Shapley value allocation. Secondly, it proposed precise categorization of consumers participating in microgrid transactions to improve the matching efficiency of order transactions, and proposed a matching satisfaction function based on the total priority index in the order transaction stage to establish a satisfactory and stable bilateral matching model for microgrid transactions. Finally, the transaction parties complete the transaction settlement without a third-party trust organization through smart contracts. The results of the algorithm show that the microgrid transaction method based on blockchain technology and maximum satisfaction and stability can complete the direct transaction within the microgrid, which ensures the stability of the microgrid transaction as well as improves the satisfaction and matching stability of the two parties involved in the microgrid transaction. [ABSTRACT FROM AUTHOR]