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Cyber-Physical Resource Scheduling in the Context of Industrial Internet of Things Operations

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Service Orientation in Holonic and Multi-Agent Manufacturing (SOHOMA 2018)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 803))

Abstract

Industrial Internet of Things (IIoT) has become a broadly researched concept in the last several years. At its very foundation, the IIoT concept is the Internet-connection of Industrial Control Systems. This paper proposes a model for the manufacturing operations in an IIoT environment, where cyber-physical resources participate in a decentralized work scheduling process. Through the exchange of physical work-in-process and order information, the cyber-physical resources accomplish a common set of goals for the IIoT system, despite induced failures or delays at the processing resource and computational network levels. Holonic scheduling techniques are employed at the IIoT level, with part order, cyber-physical resource, and data network holon entities. The data network between remote processing locations is modelled as a software-defined network, with the control level working as the data network holon computational unit. A first cut, proof-of-concept, simulation was built to evaluate the capability of the system to offer effective decentralized scheduling and reconfigure the order sequences in the case of unexpected circumstances, such as cyber-physical resource processing delays, network cybersecurity attacks, or external order changes. It is expected that the combination of decentralized order scheduling and logically centralized order transport through Internet-based data networks will result in fast, reliable, and secure order processing and transmission across IIoT networks.

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Correspondence to Radu F. Babiceanu .

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Babiceanu, R.F., Seker, R. (2019). Cyber-Physical Resource Scheduling in the Context of Industrial Internet of Things Operations. In: Borangiu, T., Trentesaux, D., Thomas, A., Cavalieri, S. (eds) Service Orientation in Holonic and Multi-Agent Manufacturing. SOHOMA 2018. Studies in Computational Intelligence, vol 803. Springer, Cham. https://doi.org/10.1007/978-3-030-03003-2_31

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