A Scalable Multi-Layer PBFT Consensus for Blockchain Li, Wenyu; Feng, Chenglin; Zhang, Lei ...
IEEE transactions on parallel and distributed systems,
2021-May-1, 2021-5-1, Letnik:
32, Številka:
5
Journal Article
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Practical Byzantine Fault Tolerance (PBFT) consensus mechanism shows a great potential to break the performance bottleneck of the Proof-of-Work (PoW)-based blockchain systems, which typically support ...only dozens of transactions per second and require minutes to hours for transaction confirmation. However, due to frequent inter-node communications, PBFT mechanism has a poor node scalability and thus it is typically adopted in small networks. To enable PBFT in large systems such as massive Internet of Things (IoT) ecosystems and blockchain, in this article, a scalable multi-layer PBFT-based consensus mechanism is proposed by hierarchically grouping nodes into different layers and limiting the communication within the group. We first propose an optimal double-layer PBFT and show that the communication complexity is significantly reduced. Specifically, we prove that when the nodes are evenly distributed within the sub-groups in the second layer, the communication complexity is minimized. The security threshold is analyzed based on faulty probability determined (FPD) and faulty number determined (FND) models, respectively. We also provide a practical protocol for the proposed double-layer PBFT system. Finally, the results are extended to arbitrary-layer PBFT systems with communication complexity and security analysis. Simulation results verify the effectiveness of the analytical results.
IoT plays a significant role in the growth of clinical data for identifying hazardous diseases and building drugs for patient diagnosis and medical care. Blockchain and federated learning are widely ...proposed in existing studies for localized data training and storage in a secure, decentralized environment. However, several studies rely on the federated averaging method in federated learning, which introduces high energy consumption during several training rounds. Frequent transactions for storing patient records in blockchain and smart contract processing present network congestion challenges. In this paper, we propose a privacy-preserving federated learning and scalable blockchain scheme. First, we present a satisfaction scoring method for model aggregation to improve energy efficiency during the federated learning process. Secondly, we design an Ethereum-based blockchain network with sidechains to process smart contract transactions separately and reduce computation overload in the blockchain mainchain. Evaluation of the proposed scheme is compared with the baseline federated average method and transaction processing of smart contracts with the Ethereum main chain. Results demonstrate reduced CPU consumption using the proposed model aggregation method, an improvement over the federated average method, and an improvement of 43.65 % in transaction processing speed using two sidechains ensuring side.
AgonOx and Providence worked with ScaleReady to enhance their manufacturing process for an IND filing. ScaleReady proposed a streamlined approach using the G-Rex500M-TF, GatheRex Liquid Handling ...&Cell Harvest Pump, and LOVO Cell Processing System.
ScaleReady assessed the team's existing process and aimed to maintain cell recovery, viability, and efficacy while closing critical stages. They conducted a demo, designed protocols, trained the team, and oversaw the initial engineering run. Subsequent runs validated the proposed process against historical data.
In an effort to improve the manufacturing process fortheir current ACT clinical trial, AgonOx and Providence approached ScaleReady to discuss options for closing key stages . ScaleReady outlined a process to close and streamline critical downstream processing steps using the G-Rex 500 MTF, the GatheRex Liquid Handling and Cell Harvest Pump, and the LOVO Cell Processing System.
During initial discussions with the AgonOx and Providence team, ScaleReady gained an understanding of their current manufacturing process and the need to close key stages while maintaining established levels of cell recovery, viability, and efficacy. The team at ScaleReady organized a demo where the G-Rex 500 MTF, the GatheRex, and the LOVO Cell Processing System could be used together to close the harvest, cell wash, and concentration process. A field application specialist from ScaleReady designed protocols to fit the use application trained the manufacturing team and guided the first engineering run. Additional engineering runs were performed using ScaleReady's proposed process to evaluate comparability with historical data.
The AgonOx and Providence team successfully completed three full-scale engineering runs to evaluate the proposed manufacturing process. The average recovery, viability, and efficacy of the cells were comparable to historical data. The overall processing time of the harvest, wash, and concentration stages of their manufacturing process decreased by half. Testing was completed within the 2-month timeline and culminated in the inclusion of the G-Rex500M-TF, GatheRex Liquid Handling &Cell Harvest Pump, and LOVO Cell Processing system into their process.
Processing times were halved while cell recovery, viability, and efficacy levels remained uncompromised.
•Block-DEF is proposed to solve big data and privacy challenges with a loose coupling design.•The blockchain bloat is avoided by combining a mixed blockchain structure with an O-NPBFT mechanism.•The ...traceability and privacy of evidence are balanced by using multi-signature schemes.
A secure digital evidence system should ensure that evidence cannot be tampered with and that private information cannot be leaked. Blockchain, a distributed tamper-resistant and privacy-preserving ledger, provides a promising solution for decentralized secure digital evidence systems. However, due to the huge number of digital evidences and the contradiction between the traceability and the privacy of evidence, blockchain faces big data and privacy challenges. To solve the above issues, we propose a secure digital evidence framework using blockchain (Block-DEF) with a loose coupling structure in which the evidence and the evidence information are maintained separately. Only the evidence information is stored in the blockchain, and the evidence is stored on a trusted storage platform. To avoid blockchain bloat, a lightweight blockchain combining a mixed block structure with an optimized name-based practical byzantine fault tolerance consensus mechanism is proposed. To support the traceability and the privacy of evidence, the multi-signature technique is adopted for evidence submission and retrieval. The analytical and experimental results show that Block-DEF is a scalable framework, it guarantees the integrity and validity of evidence, and balances privacy and traceability well.
This paper presents an efficient, highly responsive and highly repeatable MoS2/SiNWs heterostructure based photodetector. SiNWs samples were synthesized using metal assisted chemical etching and MoS2 ...nanoflakes were grown on SiNWs using wafer scalable processes. SiNWs, MoS2 and MoS2/SiNWs based devices were tested for photodetection performance in the visible region. The heterostructure interface between MoS2 and SiNWs helps to suppress dark current and enhance performance characteristics. The MoS2/SiNWs exhibits higher responsivity of 2.98 AW-1 at 450 nm illumination, which is 3 and 11 times higher than the SiNWs and MoS2 respectively. In addition, this heterojunction based device exhibits a higher detectivity of 2.7 × 1011 Jones and rise/fall time of 0.45/0.75 s. Moreover, the device fabrication method is simple and cost-effective. These results pave the way for the fabrication of scalable, highly repeatable and responsive miniaturized photodetectors based on MoS2/SiNWs for mass production.
•MoS2/SiNWs heterojunction based photodetector was constructed using a scalable fabrication process.•Photoresponsivity and detectivity of MoS2/SiNWs photodetector were enhanced in the visible region.•The rise/fall time of the device was estimated to be 0.45/0.75 s.•The MoS2/SiNWs heterojunction device shows highly repeatable results.
•There are considerable problems to overcome for 2D material commercialization.•Top-down synthesis approaches are most feasible for wider material production.•MXenes have the potential to transition ...from laboratory to industrial use.•Additional work needs conducted to correlate MXene processing to properties.•MXenes can be produced in large batches (>50 g) with no loss of properties.
Often described as “wonder materials,” two-dimensional (2D) materials have been touted as the next generation solution to many of the world’s problems, from energy storage to environmental remediation. However, despite the expectations and effort that the scientific community has placed on them, there are few examples of 2D materials moving from the laboratory to industrial use. The primary reason that most 2D materials are produced in very small quantities, are expensive or have scalability issues – it is infeasible to market materials for bulk applications when production batches are limited to subgram quantities. MXenes, the potentially largest class of 2D materials, comprised of transition metal carbides, nitrides, and carbonitrides, have extraordinary useful properties leading to potential applications in communication, energy technology, and several other fields. They are produced using a scalable selective etching approach, allowing them the potential to transition from laboratory use to wider industrial production. To accomplish this, however, significant work still needs conducted regarding the synthesis-structure-property relationship, on more diverse MXene structures and compositions, and optimization of processing. It is vital that the wider scientific community considers the feasibility of applied research prior to conducting it – if there is no potential for the application-focused work to be used, whether due to safety, material abundance, or simply cost – what benefit is gained?
Data analysis often entails a multitude of heterogeneous steps, from the application of various command line tools to the usage of scripting languages like R or Python for the generation of plots and ...tables. It is widely recognized that data analyses should ideally be conducted in a reproducible way. Reproducibility enables technical validation and regeneration of results on the original or even new data. However, reproducibility alone is by no means sufficient to deliver an analysis that is of lasting impact (i.e., sustainable) for the field, or even just one research group. We postulate that it is equally important to ensure adaptability and transparency. The former describes the ability to modify the analysis to answer extended or slightly different research questions. The latter describes the ability to understand the analysis in order to judge whether it is not only technically, but methodologically valid.
Here, we analyze the properties needed for a data analysis to become reproducible, adaptable, and transparent. We show how the popular workflow management system Snakemake can be used to guarantee this, and how it enables an ergonomic, combined, unified representation of all steps involved in data analysis, ranging from raw data processing, to quality control and fine-grained, interactive exploration and plotting of final results.
This paper applies an interlayer restoration deep neural network (IRDNN) for scalable high efficiency video coding (SHVC) to improve visual quality and coding efficiency. It is the first time to ...combine deep neural network (DNN) and SHVC. Considering the coding architecture of SHVC, we elaborate a multi-frame and multi-layer neural network to restore the interlayer of SHVC by utilizing both the adjacent reconstructed frames of the base layer (BL) and enhancement layer (EL). Moreover, we analyze the temporal motion relationship of frames in one layer and the compression degradation relationship of frames between different layers, and propose the synergistic mechanism of motion restoration and compression restoration in our IRDNN. The network can generate an interlayer with higher quality serving for the EL coding and thus enhance the coding efficiency. A large-scale and various-quality-degradation dataset is self-made for the task of interlayer restoration of SHVC. The experimental results show that with our implementation on SHVC, the EL Bj<inline-formula> <tex-math notation="LaTeX">\phi </tex-math></inline-formula>ntegaard delta bit-rate (BD-BR) reduction is 9.291% and 6.007% in signal-to-noise ratio scalability and spatial scalability, respectively. The code is available at https://github.com/icecherylXuli/IRDNN .
Blockchain technology is fast becoming the most transformative technology of recent times and has created hype and optimism, gaining much attention from the public and private sectors. It has been ...widely deployed in decentralized crypto currencies such as Bitcoin and Ethereum. Bitcoin is the success story of a public blockchain application that propelled intense research and development into blockchain technology. However, scalability remains a crucial challenge. Both Bitcoin and Ethereum are encountering low-efficiency issues with low throughput, high transaction latency, and huge energy consumption. The scalability issue in public Blockchains is hindering the provision of optimal solutions to businesses and industries. This paper presents a systematic literature review (SLR) on the public blockchain scalability issue and challenges. The scope of this SLR includes an in-depth investigation into the scalability problem of public blockchain, associated fundamental factors, and state-of-art solutions. This project managed to extract 121 primary papers from major scientific databases such as Scopus, IEEE explores, Science Direct, and Web of Science. The synthesis of these 121 articles revealed that scalability in public blockchain is not a singular term. A variety of factors are allied to it, with transaction throughput being the most discussed factor. In addition, other interdependent vita factors include storages, block size, number of nodes, energy consumption, latency, and cost. Generally, each term is somehow directly or indirectly reliant on the consensus model embraced by the blockchain nodes. It is also noticed that the contemporary available consensus models are not efficient in scalability and thus often fail to provide good QoS (throughput and latency) for practical industrial applications. Our findings exemplify that the Internet of Things (IoT) would be the leading application of blockchain in industries such as energy, finance, resource management, healthcare, education, and agriculture. These applications are, however, yet to achieve much-desired outcomes due to scalability issues. Moreover, Onchain and offchain are the two major categories of scalability solutions. Sagwit, block size expansion, sharding, and consensus mechanisms are examples of onchain solutions. Offchain, on the other hand, is a lighting network.
Temporal (or time-evolving) networks are commonly used to model complex systems and the evolution of their components throughout time. Although these networks can be analyzed by different means, ...visual analytics stands out as an effective way for a pre-analysis before doing quantitative/statistical analyses to identify patterns, anomalies, and other behaviors in the data, thus leading to new insights and better decision-making. However, the large number of nodes, edges, and/or timestamps in many real-world networks may lead to polluted layouts that make the analysis inefficient or even infeasible. In this paper, we propose LargeNetVis, a web-based visual analytics system designed to assist in analyzing small and large temporal networks. It successfully achieves this goal by leveraging three taxonomies focused on network communities to guide the visual exploration process. The system is composed of four interactive visual components: the first (Taxonomy Matrix) presents a summary of the network characteristics, the second (Global View) gives an overview of the network evolution, the third (a node-link diagram) enables community- and node-level structural analysis, and the fourth (a Temporal Activity Map - TAM) shows the community- and node-level activity under a temporal perspective. We demonstrate the usefulness and effectiveness of LargeNetVis through two usage scenarios and a user study with 14 participants.
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