1. Proceedings
  2. I3M
  3. 2024
  4. FOODOPS
  5. 10.46354/i3m.2024.foodops.016

CoAP-based Digital Twin Modelling of Heterogeneous IoT Scenarios 

  • aLuca Davoli,
  • bHafiz Humza Mahmood Ramzan,
  • cLaura Belli,
  • dGianluigi Ferrari
  • a,b,c,d Internet of Things (IoT) Lab, Department of Engineering and Architecture, University of Parma, Parco Area delle Scienze 181/A, 43124 Parma, Italy
Cite as
Davoli L., Mahmood Ramzan H. H., Belli L., Ferrari G. (2024). CoAP-based Digital Twin Modelling of Heterogeneous IoT Scenarios. Proceedings of the 10th International Food & Operations Simulation Workshop (FOODOPS 2024), 016. DOI: https://doi.org/10.46354/i3m.2024.foodops.016
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Abstract

Modern societies nowadays require more and more abstraction efforts to hide the complexity of underlying systems and infrastructures. To this end, the concept of Digital Twin (DT) has recently emerged as a key enabler for the digital transformation of well-established architectures toward their virtual representation, opening to intelligent processing capabilities (e.g., monitoring, simulation, prediction, optimization). Aside from defining DTs to enhance these services, another key paradigm that is noteworthy of attention is the Internet of Things (IoT), enabling data and information collection through heterogeneous smart devices (often equipped with sensors and actuators). Thus, combining DTs and IoT together with the Constrained Application Protocol (CoAP) as communication protocol (with its native features), will allow to define scalable and lightweight replicas of real systems, and exploit key features (e.g., service and resource discovery) to provide end users with smart solutions. In this paper, a modelling paradigm for heterogeneous IoT scenarios, based on the definition of a DT for each entity involved in a specific context to be mapped, is detailed. This will allow to a-priori estimate the behaviour of an IoT ecosystem and provide well-known interaction endpoints to request data from/pushing information to the hidden lower layers of the same ecosystem. 

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