Internationale Kooperationen

Internationale Netzwerke

IEEETrustworthy Internet of Things (TRUST-IoT)

In response to the increasing reliance on IoT devices and networks in sectors such as healthcare, transportation, smart cities, and industrial automation, the TRUST-IoT Working Group is committed to exploring the potential challenges and corresponding practical solutions that can enhance the reliability, security, trustworthiness, and transparency of Massive IoT systems. This Working Group leverages advanced specific AI technologies, comprising generative artificial intelligence (GenAI), Explainable AI (XAI), and Neuro-Symbolic AI (NeSy AI), to advance trustworthy by elucidating the reasoning behind predictions and decisions in IoT systems. The primary objectives are to drive algorithmic innovations to mitigate concerns regarding the reasoning and explainability behind different IoT functions and operations, thereby building trust within IoT ecosystems.

Scope: The TRUST-IoT Working Group focuses on addressing the various challenges associated with implementing and maintaining trustworthy IoT systems across diverse sectors. The scope of this Working Group includes, but is not limited to, the following areas:

  • Data Enhancement and Predictive Analytics: Using GenAI to create artificial data for training and testing IoT systems when real data is limited. Creating models to predict maintenance needs, potential failures, and performance issues in IoT systems.
  • Transparency in Decision-Making and Debugging: Developing XAI models to explain the decisions made by IoT systems, enhancing user trust and understanding. Furthermore, providing insights into the decision-making process and identifying areas for enhancement to improve IoT systems.
  • Advanced Reasoning and Interpretation: Using NeSy AI to better understand and interpret IoT data by combining pattern recognition with logical reasoning. In addition, designing models that merge neural network learning with structured knowledge to improve decision-making.
  • Trustworthy Massive Machine Type Communications (mMTCs) Powered by Open xG Technologies: Developing and deploying trustworthy mMTC solutions, leveraging the capabilities of Open xG technologies to support the massive connectivity requirements of IoT ecosystems. This includes ensuring reliable, secure, and efficient communication across a vast number of IoT nodes, thereby enabling scalable and resilient IoT networks.
  • Security and Trust Mechanisms: Implementing advanced security measures suitable for IoT environments. Addressing unique security challenges in IoT networks and creating frameworks to evaluate and enhance the transparency of IoT networks.

Allgemeine Kooperationsziele mit internationalen Partnern

  • Bildung internationaler Forschungsteams mit 3-4 Personen aus mindestens zwei Ländern
  • Veröffentlichung internationaler wissenschaftlicher Arbeiten
  • Initiierung internationaler Buchprojekte und Zeitschriftenredaktionen
  • Akquise internationaler Forschungsförderungen
  • Identifikation von Forschungslücken in den Bereichen Künstliche Intelligenz (KI), Cyber-Physische Systeme (CPS) und Internet der Dinge (IoT)
  • Unterstützung von Startup-Initiativen

Forschungsthemen

  • Maschinelles Lernen für Cyber-Physische Systeme (CPS)
  • IoT und moderne Kommunikationssysteme
  • Cybersicherheit für CPS
  • Drahtlose Kommunikation 6G
  • Maschinelles Lernen und Datenanalyse für IoT-Systeme
  • Signalverarbeitung für mehrdimensionale Sensorsignale
  • Process Mining für Sensorsignale
  • Intelligente Logistik und Mobilität
  • Niedrigkomplexe KI-Algorithmen für IoT-Endknoten
  • Codierung und verteiltes Lernen
  • Energieeffiziente Algorithmen für IoT-Endknoten
  • Lernen von Sensorinformationen basierend auf ratebegrenzten IoT-Systemen
  • Vor-Ort-Lernalgorithmen für datenschutzfreundliche KI-Systeme
  • Verteilte KI-basierte Angriffserkennung in IoT
  • Robustheits-/Sicherheitshandelsabgleich bei der Übertragung in IoT

Partner

Brasilien / England

  • Prof. Rodrigo De Lamare: PUC-Rio/ University of York

Frankreich

  • Dr. Ing Nadia Ndhaief: UP&S DITEX-Université de Lorraine

Deutschland

  • Prof. Anke Schmeink: RWTH Aachen University 

Kanada

  • Prof. Gunes Karabulut-Kurt.: École Polytechnique, Montreal, Canada

USA

  • Prof. Houbing Song: Embry-Riddle Aeronautical University, Daytona Beach, Florida, USA
  • Prof. Huihui Wang: St. Bonaventure University, NY
  • Prof. Wei Yu: Towson University, Maryland
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