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An experimental framework for investigating hashgraph algorithm transaction speed

Authors:

Daniel Hawthorne,

Katherine Duncan,

Aaron St. Leger,

Michael CollinsAuthors Info & Claims

BlockSys '19: Proceedings of the 2nd Workshop on Blockchain-enabled Networked Sensor

Pages 15 - 21

https://doi.org/10.1145/3362744.3363342

Published: 10 November 2019 Publication History

Abstract

Power grids around the world have experienced a growing number of malicious cyber attacks. This paper provides an overview of recent use of the Hyperledger Fabric distributed operating system to prototype use of a permissioned blockchain consensus algorithm to trust shared state estimation and control data and another effort to alter local sensor data to destroy the integrity of the shared data. The paper also provides justification for an experiment to prototype use of Babble, a peer-to-peer network plugin using the hashgraph consensus algorithm, to share the state estimation and control data through transactions recorded in a hashgraph. A key claim of the hashgraph documentation, which is unsubstantiated without a proper academic analysis, is that the algorithm is asynchronous Byzantine fault tolerance (ABFT). Also, while the Hyperledger Fabric implementation supports thousands of transactions per second, the hashgraph algorithm documentation claims orders of magnitude more. Our experiment seeks to measure the hashgraph transaction speed and determine its suitability for improving the resilience of wide area control of the smart grid. The previous resilience research of the Anomaly Detection of Cyber Physical Systems (ADCPS) team includes research into inadvertent cyber and physical failures as well as malicious attacks. We conclude with some speculations concerning the potential impact of fast, fair, and secure sharing of data across a network of blockchains potentially interfaced using hashgraph distributed ledger technology (DLT).

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Cited By

Song YXiong AWang DLi DLiu XShi HGuo Q(2024)A Double Layer Consensus Optimization Mechanism in DAG-Based Blockchain for Carbon TradingWireless Artificial Intelligent Computing Systems and Applications10.1007/978-3-031-71464-1_12(136-147)Online publication date: 13-Nov-2024
https://doi.org/10.1007/978-3-031-71464-1_12
Xiao LHan DLi DLiang WYang CLi KCastiglione A(2023)CTDM: cryptocurrency abnormal transaction detection method with spatio-temporal and global representationSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-023-08220-x27:16(11647-11660)Online publication date: 12-May-2023
https://dl.acm.org/doi/10.1007/s00500-023-08220-x
Li LShi PFu XZhang SZhong TChen M(2022)Asycome: A JointCloud Data Asynchronous Collaboration Mechanism Based on BlockchainBlockchain and Trustworthy Systems10.1007/978-981-16-7993-3_41(530-544)Online publication date: 1-Jan-2022
https://doi.org/10.1007/978-981-16-7993-3_41

Index Terms

An experimental framework for investigating hashgraph algorithm transaction speed
1. Applied computing
  1. Physical sciences and engineering
    1. Electronics
2. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Sensor networks
    2. Sensors and actuators
  2. Real-time systems
    1. Real-time system architecture

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cover image ACM Conferences

BlockSys '19: Proceedings of the 2nd Workshop on Blockchain-enabled Networked Sensor

November 2019

35 pages

ISBN:9781450370127

DOI:10.1145/3362744

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 10 November 2019

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Office of Naval Research

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SenSys '19

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SenSys '19: The 17th ACM Conference on Embedded Networked Sensor Systems

November 10, 2019

NY, New York, USA

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Cited By

Song YXiong AWang DLi DLiu XShi HGuo Q(2024)A Double Layer Consensus Optimization Mechanism in DAG-Based Blockchain for Carbon TradingWireless Artificial Intelligent Computing Systems and Applications10.1007/978-3-031-71464-1_12(136-147)Online publication date: 13-Nov-2024
https://doi.org/10.1007/978-3-031-71464-1_12
Xiao LHan DLi DLiang WYang CLi KCastiglione A(2023)CTDM: cryptocurrency abnormal transaction detection method with spatio-temporal and global representationSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-023-08220-x27:16(11647-11660)Online publication date: 12-May-2023
https://dl.acm.org/doi/10.1007/s00500-023-08220-x
Li LShi PFu XZhang SZhong TChen M(2022)Asycome: A JointCloud Data Asynchronous Collaboration Mechanism Based on BlockchainBlockchain and Trustworthy Systems10.1007/978-981-16-7993-3_41(530-544)Online publication date: 1-Jan-2022
https://doi.org/10.1007/978-981-16-7993-3_41

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