Table of Content

30 July 2023, Volume 41 Issue 4

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Blockchain

Fair and Verifiable Voting Smart Contract Based on Blockchain

LIU Hong, ZHANG Jingyu, LEI Mengting, XIAO Yunpeng

2023, 41(4):  541-562.  doi:10.3969/j.issn.0255-8297.2023.04.001

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This paper proposes a blockchain-based encryption-proof scheme to address the fairness flaws and replay attacks in the equal voting mechanism. First, the voting process and rules are written into smart contracts, including time stamps and financial incentives, to ensure that voting takes place on time. It is stipulated that each voter is responsible for his address key generation. A Merkle tree based on the address public key is constructed to prove the legitimacy of the voter’s identity. Meanwhile, a random sequence is generated by hash to prevent repeat voting. Second, the blockchain bulletin board and Paillier algorithm encrypt and store votes to improve the encryption and decryption rate while overcoming the fairness defect. Finally, to ensure transaction legality and calculation accuracy, a zero-knowledge proof based on zk-SNARK is constructed based on the immutable characteristics of the blockchain. In this way, the real problem to be proved is transformed into a calculation problem with specific output, and the encryption algorithm is separated from the zero-knowledge proof circuit, so that the information of the verification data will not be disclosed. Theoretical analysis and experimental results show that the proposed scheme significantly improves the security and privacy of voting and has lower time and cost consumption.

Food Supply Chain Traceability System Based on Multi-blockchain

CAO Haohao, LIU Yang, LI Xiangyang, LIU Xinlei, WANG Yaoqi, ZHANG Yuan

2023, 41(4):  563-576.  doi:10.3969/j.issn.0255-8297.2023.04.002

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This paper proposes a multi-blockchain food supply chain traceability system (MBFST) based on the integration of consortium blockchain and private blockchain, to address challenges in food traceability. Enterprises carry out business cooperation through the consortium blockchain system, and record the trade circulation through the private blockchain system. The cross-chain interaction protocol between the private blockchain and the consortium blockchain is designed to implement the interaction and mapping according to the traceability ID. The system enables business cooperation and trade circulation recording through the consortium and private blockchain, respectively, with a cross-chain interaction protocol facilitating interaction and mapping based on traceability ID. Through data isolation mechanism like private data set, the protection of private data of enterprises is ensured. Experimental results show that the prototype system can achieve accurate traceability among enterprises in the food supply chain and ensure the whole process traceability both on-chain and off-chain. MBFST achieves a maximum TPS (transaction per second) of over 1000 and a TPS of over 200 for rich queries using traceability ID, with an average delay stable at 0.2～0.3 s. MBFST has superior query performance in high concurrency circumstances in terms of traceability, privacy and efficiency.

A PBFT Consensus Algorithm for Consortium Chain Optimization

WANG Weiyuan, BI Yuanwei, CHEN Xiaohan, LI Chuanbiao

2023, 41(4):  577-589.  doi:10.3969/j.issn.0255-8297.2023.04.003

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This paper proposes a decision tree Byzantine fault tolerance (DTBFT) algorithm to address the problems of communication overhead, unguaranteed node reputation, and inability to dynamically add or delete nodes in the PBFT algorithm for consortium chains. Firstly, according to the application scenario of the alliance chain, the consensus protocol of the PBFT algorithm is simplified, and the communication overhead is reduced. Secondly, a reputation score mechanism and a decision tree classification algorithm are introduced to improve security of the system. Finally, the selection range of master nodes is narrowed to high-level nodes with good node reputation to prevent frequent view switches. Experiments show that the DTBFT algorithm enhances throughput and algorithm security compared with PBFT.

Dynamic Spectrum Sharing Based on Blockchain Smart Contract

PAN Liang, CHEN Bin, DAI Mingjun, WANG Taotao, ZHANG Shengli

2023, 41(4):  590-600.  doi:10.3969/j.issn.0255-8297.2023.04.004

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In this paper, a blockchain smart contract-based smart contract is proposed to address the problems of data security threats, high management costs, and low scalability in centralized dynamic spectrum sharing. First, spectrum coupons are designed using the blockchain ERC4907 protocol to ensure the unique identification and leasing of spectrum resources. Second, the blockchain oracle machine was used to update the data of idle spectrum resources in real time to realize the upload of spectrum data. Finally, an auction algorithm based on smart contracts and Vue framework is used to auction the spectrum coupon lease term corresponding to the idle spectrum resources on the front-end page. Experimental results indicate that the proposed dynamic spectrum sharing scheme based on the blockchain smart contract ensures the users’ safe and effective dynamic spectrum sharing and is feasible.

Consortium Chain Multi-chain Collaboration Scheme for Complex Application Scenarios

ZHANG Yong, YAO Zhongyuan, WANG Chao, GUO Shangkun, GUO Xiaohan, SI Xueming

2023, 41(4):  601-613.  doi:10.3969/j.issn.0255-8297.2023.04.005

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To address the shortcomings of decentralized authority management, difficult business expansion, centralized reading and writing, and weak transactionality between multiple contracts in complex application scenarios, a consortium chain multi-chain collaboration scheme for complex application scenarios is proposed. This scheme uses a blockchain gateway to manage user access rights, provides an inter-chain routing mechanism for dynamic scaling of logical chains, implements a read-write separation mechanism to improve performance, and employs a TCC-based transaction scheme to support multicontract transactions. Experiments and analysis results show that the proposed scheme effectively improves transaction throughput, and has better support for business dynamic expansion and multi-contract transactionality.

Research on Multi-channel Sharding Technology for Hyperledger Fabric

LIU Yang, LIN Zhiyuan, ZHANG Yuxi, JIANG Lin, WU Yulin

2023, 41(4):  614-625.  doi:10.3969/j.issn.0255-8297.2023.04.006

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Blockchain is widely used in various fields, including the Internet of things and finance. Hyperledger Fabric is the one of the mainstream enterprise-level licensed blockchain systems, but its throughput limitation in high concurrency scenarios hinders wider application. Sharding is a solution to this problem, which can meet the goals of low latency and high throughput simultaneously. However, most existing sharding schemes are designed for non-licensed blockchain confidential currency only. In this paper, we propose a multi-channel interactive sharding scheme for the Hyperledger Fabric blockchain platform. First, the current transaction channel is dynamically copied and endorsed in parallel according to the sending rate of client transactions. Then, the transactions endorsed by the copied channel are emerged at the sorting node to generate new blocks. Finally, the new blocks are distributed to each node in parallel in multiple channels and integrated in the main ledger to ensure the consistency of the ledger between peer nodes and update the world state.

Communication Engineering

Large-Scale Device Access Algorithm for 5G Cellular Internet of Things

ZHANG Ziyang, ZHAO Junhui, MA Xiaoting

2023, 41(4):  626-635.  doi:10.3969/j.issn.0255-8297.2023.04.007

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With the era of interconnected physical devices coming, massive machine type communication (mMTC) is one of the main application scenarios of 5G network. In 5G mMTC scenario, a large number of devices requests to access the network at the same time, causing network congestion and performance degradation. To solve this problem, a device access algorithm based on the analysis of cellular internet of things (IoT) terminal data is proposed. By considering the relevant information of devices and network resources, devices are clustered and mapped to the idle network resource blocks, achieving the efficient access of cellular IoT devices. The proposed method provides an effective access network for 5G cellular IoT devices under mMTC scenarios.

Outage Probability of Multiuser Cooperative Wireless Networks with Cochannel Interferers

HUANG Haiyan, SHI Yujie, ZHANG Xuejun, WANG Chunli, LI Xinying

2023, 41(4):  636-645.  doi:10.3969/j.issn.0255-8297.2023.04.008

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By jointly optimizing cooperative diversity and multiuser diversity, an optimal “source-relay” selection scheme is proposed for multi-source and multi-relay wireless cooperative networks with cochannel interference. The source node with the largest SINR is selected to broadcast signals to multiple relay nodes based on the signals received between multiple source nodes and destination nodes. The relay node then forwards the decoded source node signals to the destination node using the selective cooperative decoding and forwarding protocol, and the destination node combines the signals received by the two hops in the cooperative transmission using the maximum ratio combining (MRC). An exact expression for outage probability in Rayleigh fading environments is derived, taking into account the correlation between SINR caused by multiple cochannel interferences. Experimental results demonstrate that increasing the number of source and relay nodes can mitigate the effects of cochannel interference, reducing the outage probability and improving overall system performance while achieving diversity gain.

Encrypted Traffic Classification Algorithm Based on VPN Channel

WEI Jieling, MA Xiuli, JIN Yanliang, WANG Rui

2023, 41(4):  646-656.  doi:10.3969/j.issn.0255-8297.2023.04.009

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This paper proposes a new encrypted traffic classification algorithm based on a variant ResNet18 network to improve network management and strengthen network security supervision. A three-channel image construction is designed to address the strong encryption and high opacity characteristics of traffic in virtual private network (VPN) channels. The proposed method successfully identifies different apps’ traffic in different VPN channels, as validated using popular apps’ traffic collected from real VPN channels. The algorithm achieves 98.1% and 96.0% classification accuracy on public and self-collected datasets, respectively. Experimental results demonstrate the algorithm’s universality and practical value.

Gaussian Mixture Model Convolution Neural Network Based on Imbalanced Problem

XU Hong, JIAO Guie, ZHANG Wenjun

2023, 41(4):  657-668.  doi:10.3969/j.issn.0255-8297.2023.04.010

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Imbalanced data classification is a challenging task in big data mining. The distribution of imbalanced data seriously affects the classification performance of models, especially for minority classes. In this paper, an expectation-maximum weighted resampling (EMWRS) algorithm and weighted cross entropy Loss (WCELoss) function are proposed to improve the classification performance of imbalanced data. The proposed approach utilizes a Gaussian mixture model to preprocess the data and employs weighted sampling and cost-sensitive learning to construct a convolutional neural network model. The constructed convolutional neural network is evaluated using F1 and G-mean as indicators, and compared with various classic algorithms such as SMOTE (synthetic minor over sampling technique) and ADASYN (adaptive synthetic sampling) on the adult datasets of UCI (university of California irvine). The experimental results demonstrate that the proposed model outperforms ADASYN and other classical algorithms in terms of F1 and G-mean on UCI adult datasets, which indicates that the proposed model effectively enhances the accuracy of minority classification.

Signal and Information Processing

Environmental Sound Classification Method Based on Color Channel Feature Fusion

DONG Shaojiang, XIA Zhengfu, FANG Nengwei, XING Bin, HU Xiaolin

2023, 41(4):  669-681.  doi:10.3969/j.issn.0255-8297.2023.04.011

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To address low classification accuracy in traditional neural networks processing complex environmental sounds, an environment sound classification method based on color channel feature fusion is proposed. Firstly, three acoustic features are extracted from the raw audio data, namely log-Mel Spectrogram (LMS), Mel-scale frequency cepstral Coefficients (MFCC) and energy spectrum (ES). Then, the above three features are used as RGB color channel components respectively for feature fusion to form a more representative spectrogram, which contributes to representing the environmental sound comprehensively. Subsequently, in order to avoid the poor generalization ability of the trained model due to the small number of datasets, the pre-trained network VGG-16 is trained by fine-tuning method. Finally, the effectiveness of the proposed method is verified on two widely used environmental sound classification datasets and audios collected in real scenarios, and compared with other models in terms of accuracy. The results show that the accuracy of the proposed method on ESC-10 and ESC-50 datasets can reach 88.2% and 65.2% respectively, improving the classification performance of audios collected in real scenarios.

Image Publication Algorithm of Service Robot Based on Differential Privacy Protection

JIAO Hejun, ZHOU Wanchun, SHI Jinfa, LIU Shengyuan

2023, 41(4):  682-691.  doi:10.3969/j.issn.0255-8297.2023.04.012

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To protect the sensitive personal information in service robots, this paper proposes an image publishing algorithm for service robots based on the combination of discrete cosine transform and differential privacy. Firstly, the image is compressed by discrete cosine transform. In order to balance the correlation noise error and reconstruction error, a coefficient selection method based on random gradient descent algorithm is introduced. Then, appropriate coefficients are selected in the corresponding coefficient space to compress the image, and Laplace noise is added to the coefficient space. Finally, based on four real indoor image data sets, wavelet packet transform and least squares support vector machine classification techniques are used to measure the accuracy of the algorithm. The experimental results demonstrate the robustness of the proposed algorithm.

Computer Science and Applications

Pork Price Prediction Model Based on VMD-BO-BILSTM

HU Chun'an, JIANG Wei

2023, 41(4):  692-704.  doi:10.3969/j.issn.0255-8297.2023.04.013

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Based on the nonlinear and fluctuating characteristics of pork price, this paper proposes a pork price prediction approach using variational modal decomposition (VMD) and Bayesian optimization-based bidirectional long short-term memory (BiLSTM). VMD decomposes the data into subsequences with simple fluctuations, which are then used in BiLSTM. Bayesian optimization is adopted to optimize the number of neurons, learning rate, and batch size of the first and second hidden layers of the BiLSTM network model. Experimental results show that the proposed VMD-BO-BiLSTM method outperforms traditional single LSTM and BiLSTM models in terms of mean absolute error, root mean square error, mean absolute percentage error, and determination coefficient. It has higher accuracy and applicability for pork price prediction.

Implementing a FAST Decoder with Low Latency and Low Jitter

ZHANG Xihuang, DING Nan, CHAI Zhilei, FENG Yifei, YE Junchao

2023, 41(4):  705-717.  doi:10.3969/j.issn.0255-8297.2023.04.014

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In order to solve the problems of high delay of pure software decoding, long development period of FPGA (field programmable gate array) hardware decoding and difficult update of financial FAST (financial information exchange adapted for streaming) protocol, a hardware decoding mode based on OpenCL and HLS was proposed. By optimizing the marking, segmentation, merging, and decoding modules of FAST data decoding through pipelining, parallel operations are performed on segmentation and field decoding. The input and output of the data are changed to a streaming interface to reduce I/O port latency, and the segmentation and mapping of the array segmentation are carried out to achieve low latency and low jitter in the decoding process. Experimental results show that compared with pure software decoding, the processing speed of the proposed decoder is improved by 11 times, the decoding delay is shortened to 1/6, and the jitter amplitude is controlled within 10ns. Compared with the traditional HDL custom FPGA hardware development, the proposed approach improves development efficiency by 3～4 times, thus better meeting the needs of product updates.

Control and System

Control Method of SMA Wire Constant Output Force Based on Neural Network PID

WANG Ben, Lü Peilun, WANG Yangwei

2023, 41(4):  718-726.  doi:10.3969/j.issn.0255-8297.2023.04.015

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To realize the constant output force control of shape memory alloys (SMA) wire, a constant output force control method based on particle swarm optimization neural network proportion integral differential (PID) is proposed. This paper examines the relationship between SMA wire self-perceived resistance value, output force, and temperature, with SMA wire resistance used as system feedback. Particle swarm algorithm is then applied to optimize the parameters of PID, achieving accurate control of constant output force of SMA wire, thereby reducing overshoot and improving response speed compared to traditional PID control.