TT 09 - Real-time and networked embedded computing, industrial IoT technologies and applications

Track Chairs
Gaetano Patti, University of Catania, Italy

Focus

Real-time systems bring significant challenges and opportunities in embedded computing techniques to speed up industrial and commercial services. The emergence of Internet-of-Things (IoT) – a smart high-level concept of progressing the world through connecting and integrating all things into a huge network - has also been improving the communication efficiency for these services. A networked embedded computing has thus gradually been drawing developers’ attention to be widely applied in abundant applications. This track focuses on models, techniques, methods, analysis and applications that are related to real-time and embedded computing and IoT based networking and technologies.

Topics under this track include (but not limited to)
  • Innovations in real-time capable networks
  • Software Defined Networks (SDN) and Time-Sensitive Networking (TSN)
  • Deterministic IoT technologies
  • Hard Real-time SOA and RESTful industrial communication
  • Real-time issues of distributed control in industrial cyber-physical systems
  • Industrial IoT protocols (OPC UA, DDS, MQTT, AMQP, COAP, IEEE 11073 SDC, …)
  • Real-time analysis of location-based systems
  • Verification and validation of distributed embedded applications
  • Security verification of real-time (operating) systems and industrial automation systems
  • Factors influencing latencies in emerging industrial communication systems
  • Combining legacy real-time networks (e.g. CAN) with emerging real-time IP-based networks (TSN, SDN)
  • Blockchain and distributed ledger technologies in networked embedded systems and IoT applications
  • Deep learning technologies for distributed real-time embedded systems
  • Performance analysis (of distributed real-time systems)
  • Emerging real-time operating systems and real-time hypervisors
  • Hard real-time services in edge and cloud
  • Timing and resource analysis of (distributed) resource-constrained AI
  • 5G for industrial applications

Track Committee
  • Mohammad Ashjaei, Mälardalen University, Sweden
  • Moris Behnam, Mälardalen University, Sweden
  • Reinder Bril, Technische Universiteit Eindhoven (TU/e), Netherlands
  • Peter Danielis, Institute of Computer Science, University of Rostock, Germany
  • Cavalcanti Dave, Intel Corporation, USA
  • Antonio Espirito-Santo, University of Beira Interior, Portugal
  • Tullio Fachinetti, University of Pavia, Italy
  • Paolo Ferrari, University of Brescia, Italy
  • Rafia Inam, Ericsson Research, Ericsson AB, Sweden
  • Luca Leonardi, University of Catania,Italy
  • Changxin Liu, KTH, Sweden
  • Lucia Lo Bello, University of Catania, Italy
  • Luis Moutinho, University of Aveiro, Portugal
  • Roman Obermaisser, University of Siegen, Germany
  • Achim Rettberg, Hamm-Lippstadt, University of Applied Sciences, Germany
  • Stefano Rinaldi, University of Brescia, Italy
  • Chao Shen, Carleton University, Canada
  • Alexander Viehl, FZI Research Center for Information Technology, Germany
  • Feisheng Yang, Northwestern Polytechnical University, China
  • Kunwu Zhang, University of Victoria, Canada
  • Marisol García Valls, Universidad Politécnica de Valencia, Spain
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