SG-PBFT: A secure and highly efficient distributed blockchain PBFT consensus algorithm for intelligent Internet of vehicles

Xu, Guangquan; Bai, Hongpeng; Xing, Junliang; Luo, Tao; Xiong, Neal N.; Cheng, Xiaochun; Liu, Shaoying; Zheng, Xi

doi:10.1016/j.jpdc.2022.01.029

Cited by 107 publications

(32 citation statements)

References 59 publications

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“…As technology evolves, data security is facing more and more attacks [14,15]. The current data privacy protection technologies for wearable devices are mainly based on cryptography and perturbation [16].…”

Section: Related Workmentioning

confidence: 99%

“…If the derivatives of adjacent data points are opposite, and the time interval (t int ) between the former point and the starting point (i.e., t cur and t sta respectively) is greater than α, the former is kept and the latter is compared with the next data point. Repeat the previous steps until the list1 is traversed, and the retained points together with the starting and ending points are the salient points, which are stored in list2 (lines [10][11][12][13][14][15][16][17][18][19][20][21][22].…”

Section: Plos Onementioning

confidence: 99%

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Local differential privacy protection for wearable device data

Wang

et al. 2022

PLoS ONE

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Personal data collected by wearable devices contains rich privacy. It is important to realize the personal privacy protection for user data without affecting the data collection of wearable device services. In order to protect users’ personal privacy, a collection scheme based on local differential privacy is proposed for the collected single attribute numerical stream data. At first, the stream data points collected by the wearable device are censored to identify the salient points, and the adaptive Laplacian mechanism is used to add noise to these salient points according to the assigned privacy budget; then the collector reconstructs and fits the stream data curve to the noise-added salient points, so as to protect the personal privacy of the data. This scheme is experimented on the heart rate dataset, and the results show that when the privacy budget is 0.5 (i.e., at higher privacy protection strength), the mean relative error is 0.12, which is 57.78% lower than the scheme of Kim et al. With the satisfaction of user privacy protection, the usability of mean value estimation of wearable device stream data is improved.

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Section: Related Workmentioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

Local differential privacy protection for wearable device data

Wang

et al. 2022

PLoS ONE

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“…In the improved algorithm, consensus nodes are grouped by type to form a node structure with the same characteristics, which is more conducive to data storage and node management 14,15,20 . Also, dividing a large number of nodes into multiple consensus sets can reduce the size of nodes and simplify the complexity of consensus.…”

Section: Typesmentioning

confidence: 99%

An improved PBFT consensus algorithm based on grouping and credit grading

Liu

Wang

2022

Preprint

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To address the problems of high communication complexity, the random selection of master nodes, and limited supported network size of the Practical Byzantine Fault Tolerance (PBFT) consensus algorithm for consortium chains, an improved Byzantine Fault Tolerance (CBFT) algorithm based on grouping and credit hierarchy is proposed to optimize the nodes of large-scale consortium chains structure. First, the network nodes are divided into different consensus sets according to their response speed to the management nodes, and the consensus is carried out inside and outside the group respectively; based on this, a credit grading mechanism is proposed and a credit calculation formula is introduced to select the management nodes; finally, a simulation and performance testing system based on this improved scheme is built. The experimental results show that CBFT has less communication overhead, shorter latency, and higher throughput than PBFT, and it is more obvious with the increase in the number of nodes, which meets the needs of large consortium chains.

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“…To validate the performance of MSACLPSO, the PSO, BLPSO, CLPSO, CPMPSO, IJAYA, GOTLBO, SATLBO, DE/BBO, DBBO, STLBO, WOA, CWOA, LWOA, GWO, EGWO, WDO, DE, JADE, and MPPCEDE [ 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 ] algorithms were used for comparison. The parameter values of MSACLPSO were the same as given in Section 5.2 .…”

Section: Case Analysismentioning

confidence: 99%

A Multi-Strategy Adaptive Comprehensive Learning PSO Algorithm and Its Application

Zhang

Song

2022

Entropy

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In this paper, a multi-strategy adaptive comprehensive learning particle swarm optimization algorithm is proposed by introducing the comprehensive learning, multi-population parallel, and parameter adaptation. In the proposed algorithm, a multi-population parallel strategy is designed to improve population diversity and accelerate convergence. The population particle exchange and mutation are realized to ensure information sharing among the particles. Then, the global optimal value is added to velocity update to design a new velocity update strategy for improving the local search ability. The comprehensive learning strategy is employed to construct learning samples, so as to effectively promote the information exchange and avoid falling into local extrema. By linearly changing the learning factors, a new factor adjustment strategy is developed to enhance the global search ability, and a new adaptive inertia weight-adjustment strategy based on an S-shaped decreasing function is developed to balance the search ability. Finally, some benchmark functions and the parameter optimization of photovoltaics are selected. The proposed algorithm obtains the best performance on 6 out of 10 functions. The results show that the proposed algorithm has greatly improved diversity, solution accuracy, and search ability compared with some variants of particle swarm optimization and other algorithms. It provides a more effective parameter combination for the complex engineering problem of photovoltaics, so as to improve the energy conversion efficiency.

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SG-PBFT: A secure and highly efficient distributed blockchain PBFT consensus algorithm for intelligent Internet of vehicles

Cited by 107 publications

References 59 publications

Local differential privacy protection for wearable device data

Local differential privacy protection for wearable device data

An improved PBFT consensus algorithm based on grouping and credit grading

A Multi-Strategy Adaptive Comprehensive Learning PSO Algorithm and Its Application

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