Blockchain application in remote condition monitoring

Rahma Alzahrani, Simon Herko, John Easton

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

Through advanced sensor technologies, satellite-based authentication, and high bandwidth data networks, Remote Condition Monitoring (RCM) systems are now an essential 'Internet of Things' (IoT) resource for efficient operation of railway infrastructure. However, the full potential of this big data has yet to be realized. Data is currently collected and used in siloes, with limited visibility of all possible datasets for exploitation. The RSSB on behalf of the UK Rail Industry established a cross-industry research program, T1010, to build stronger cooperation between stakeholders in sharing RCM data. This research builds upon T1010, to explore the use of blockchain and smart contracts to automate, in an auditable and tamper-proof way, the commercial agreements and payment processes for data trading. By removing the limitations of paper-based agreements, our goal is to enable innovation in shared business processes and an IoT data marketplace. Building on existing smart contract-based schemes for trading and sharing IoT data over blockchain networks, this research identifies novel ways to enforce agreements and ensure fair cost attribution between parties, without a Trusted Third Party. The initial design of a blockchain-based framework is presented, oriented around the data provider, consumer, and smart contracts. Blockchain-hosted data access agreement and accounting models are specified in detail. The processors in the efficient permissioned blockchain platforms Hyperledger Fabric, Sawtooth, and Iroha have been analyzed for their suitability for implementation. We then outline our future work to evaluate and validate two industrial use cases: monitoring systems for unattended overhead line equipment and axle bearings.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Big Data (Big Data)
EditorsXintao Wu, Chris Jermaine, Li Xiong, Xiaohua Tony Hu, Olivera Kotevska, Siyuan Lu, Weijia Xu, Srinivas Aluru, Chengxiang Zhai, Eyhab Al-Masri, Zhiyuan Chen, Jeff Saltz
Pages2385-2394
Number of pages10
ISBN (Electronic)9781728162515
DOIs
Publication statusPublished - 19 Mar 2021
Event2020 IEEE International Conference on Big Data (Big Data) - Online
Duration: 10 Dec 202013 Dec 2020
https://bigdataieee.org/BigData2020/

Publication series

NameIEEE International Conference on Big Data

Conference

Conference2020 IEEE International Conference on Big Data (Big Data)
Period10/12/2013/12/20
Internet address

Bibliographical note

Funding Information:
ACKNOWLEDGMENT This project has received funding from the Shift2Rail Joint Undertaking (JU) under grant agreement No 826156. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and the Shift2Rail JU members other than the Union. The authors would further like to acknowledge Imam Abdulrahman Bin Faisal University and the Saudi Government for funding R.A.A., the first author.

Publisher Copyright:
© 2020 IEEE.

Keywords

  • Big data
  • Blockchain
  • Cost attribution
  • Process automation
  • Remote Condition Monitoring

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality

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