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Anomaly Detection in a Boxed Beef Supply Chain

Published:14 October 2021Publication History

ABSTRACT

An approach to simulating and analysing sensor events in a boxed beef supply chain is presented. The simulation component reflects our industrial partner’s transport routes and parameters under normal and abnormal conditions. The simulated transport events are fed into our situational awareness system for detecting temperature anomalies or potential box tampering. The situational awareness system features a logic-based modeling language and an inference engine that tolerates incomplete or erroneous observations. The paper describes the approach and experimental results in more detail.

References

  1. 2021. BeefLedger. https://beefledger.io/.Google ScholarGoogle Scholar
  2. A. Artikis, Anastasios Skarlatidis, François Portet, and G. Paliouras. 2012. Logic-based event recognition. Knowl. Eng. Rev. 27(2012), 469–506.Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Peter Baumgartner. 2020. Possible Models Computation and Revision – A Practical Approach. In International Joint Conference on Automated Reasoning(LNAI, Vol. 12166), N. Peltier and V. Sofronie-Stokkermans (Eds.). Springer International Publishing, Cham, 337–355. https://doi.org/10.1007/978-3-030-51074-9_19Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Peter Baumgartner. 2021. The Fusemate Logic Programming System (System Description). https://arxiv.org/abs/2103.01395Google ScholarGoogle Scholar
  5. Peter Baumgartner and Patrik Haslum. 2021. Situational Awareness for Industrial Operations. In Data and Decision Sciences in Action 2, Andreas T. Ernst, Simon Dunstall, Rodolfo García-Flores, Marthie Grobler, and David Marlow(Eds.). Springer International Publishing, Cham, 125–137. ASOR-2018.pdfGoogle ScholarGoogle Scholar
  6. Harald Beck, Minh Dao-Tran, and Thomas Eiter. 2018. LARS: A Logic-based framework for Analytic Reasoning over Streams. Artificial Intelligence 261 (08 2018), 16–70. https://doi.org/10.1016/j.artint.2018.04.003Google ScholarGoogle Scholar
  7. Wolfgang Faber. 2020. An Introduction to Answer Set Programming and Some of Its Extensions. In Reasoning Web. Declarative Artificial Intelligence - 16th International Summer School 2020, Oslo, Norway, June 24-26, 2020, Tutorial Lectures(Lecture Notes in Computer Science, Vol. 12258), Marco Manna and Andreas Pieris (Eds.). Springer, 149–185. https://doi.org/10.1007/978-3-030-60067-9_6Google ScholarGoogle Scholar
  8. David McKinna and Catherine Wall. 2020. Commercial application of supply chain integrity and shelf life systems. Technical Report. Meat and Livestock Australia Limited, NORTH SYDNEY NSW 2059. https://www.mla.com.au/research-and-development/reports/2020/commercial-application-of-supply-chain-integrity-and-shelf-life-systems/.Google ScholarGoogle Scholar
  9. A. Medvedev, A. Hassani, P. D. Haghighi, S. Ling, M. Indrawan-Santiago, A. Zaslavsky, U. Fastenrath, F. Mayer, P. P. Jayaraman, and N. Kolbe. 2018. Situation Modelling, Representation, and Querying in Context-as-a-Service IoT Platform. In 2018 Global Internet of Things Summit (GIoTS). 1–6. https://doi.org/10.1109/GIOTS.2018.8534571Google ScholarGoogle Scholar
  10. Scala [n.d.]. The Scala Programming Language. https://www.scala-lang.org.Google ScholarGoogle Scholar
  11. Murray Shanahan. 1999. The Event Calculus Explained. In Artificial Intelligence Today: Recent Trends and Developments, Michael J. Wooldridgeand Manuela Veloso (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 409–430.Google ScholarGoogle Scholar
  12. Dhananjay Singh, Gaurav Tripathi, and Antonio J. Jara. 2014. A Survey of Internet-of-Things: Future Vision, Architecture, Challenges and Services. In 2014 IEEE World Forum on Internet of Things, WF-IoT 2014. IEEE.Google ScholarGoogle ScholarCross RefCross Ref
  13. Monika Solanki and Christopher Brewster. 2014. Detecting EPCIS exceptions in linked traceability streams across supply chain business processes. In SEMANTICS. ACM, 24–31.Google ScholarGoogle Scholar

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  • Published in

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    ICCMS '21: Proceedings of the 13th International Conference on Computer Modeling and Simulation
    June 2021
    276 pages
    ISBN:9781450389792
    DOI:10.1145/3474963

    Copyright © 2021 ACM

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    • Published: 14 October 2021

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