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Specifying autonomy in the Internet of Things: the autonomy model and notation

Author

Listed:
  • Christian Janiesch

    (Julius-Maximilians-Universität Würzburg)

  • Marcus Fischer

    (Julius-Maximilians-Universität Würzburg)

  • Axel Winkelmann

    (Julius-Maximilians-Universität Würzburg)

  • Valentin Nentwich

    (AUDI AG)

Abstract

Driven by digitization in society and industry, automating behavior in an autonomous way substantially alters industrial value chains in the smart service world. As processes are enhanced with sensor and actuator technology, they become digitally interconnected and merge into an Internet of Things (IoT) to form cyber-physical systems. Using these automated systems, enterprises can improve the performance and quality of their operations. However, currently it is neither feasible nor reasonable to equip any machine with full autonomy when networking with other machines or people. It is necessary to specify rules for machine behavior that also determine an adequate degree of autonomy to realize the potential benefits of the IoT. Yet, there is a lack of methodologies and guidelines to support the design and implementation of machines as explicit autonomous agents such that many designs only consider autonomy implicitly. To address this research gap, we perform a comprehensive literature review to extract 12 requirements for the design of autonomous agents in the IoT. We introduce a set of constitutive characteristics for agents and introduce a classification framework for interactions in multi-agent systems. We integrate our findings by developing a conceptual modeling language consisting of a meta model and a notation that facilitates the specification and design of autonomous agents within the IoT as well as CPS: the autonomy model and notation. We illustrate and discuss the approach and its limitations.

Suggested Citation

  • Christian Janiesch & Marcus Fischer & Axel Winkelmann & Valentin Nentwich, 2019. "Specifying autonomy in the Internet of Things: the autonomy model and notation," Information Systems and e-Business Management, Springer, vol. 17(1), pages 159-194, March.
    • Handle: RePEc:spr:infsem:v:17:y:2019:i:1:d:10.1007_s10257-018-0379-x
    DOI: 10.1007/s10257-018-0379-x
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    References listed on IDEAS

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    1. Rajagopal, 2014. "The Human Factors," Palgrave Macmillan Books, in: Architecting Enterprise, chapter 9, pages 225-249, Palgrave Macmillan.
    2. Daniel Beverungen & Martin Matzner & Christian Janiesch, 2017. "Information systems for smart services," Information Systems and e-Business Management, Springer, vol. 15(4), pages 781-787, November.
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    Cited by:

    1. Pascal Oliver Heßler & Jella Pfeiffer & Sebastian Hafenbrädl, 2022. "When Self-Humanization Leads to Algorithm Aversion," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 64(3), pages 275-292, June.
    2. Issa, Helmi & Jabbouri, Rachid & Palmer, Mark, 2022. "An artificial intelligence (AI)-readiness and adoption framework for AgriTech firms," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    3. Thomas Grisold & Christian Janiesch & Maximilian Röglinger & Moe Thandar Wynn, 2022. "Call for Papers, Issue 5/2024," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 64(6), pages 841-843, December.
    4. Issa, Helmi & Jabbouri, Rachid & Palmer, Mark, 2021. "An Artificial Intelligence (Ai)-Readiness and Adoption Framework for Agritech Firms," QBS Working Paper Series 271255, Queen's University Belfast, Queen's Business School.

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