IEEE Working Group Artificial Intelligence of Things Group 

Overview: Artificial Intelligence (AI) and the Internet of Things (IoT) are two of the most rapidly evolving and interdependent fields of technology. The convergence of these two fields is leading to the creation of an emerging interdisciplinary field, dubbed the Artificial Intelligence of Things (AIoT). The AIoT represents the integration of AI technologies into IoT systems and devices, enabling them to interact with their environment in more sophisticated and intelligent ways. By combining IoT with AI, the data collected by distributed nodes can be utilized by applying AI techniques such as machine learning and deep learning. As a result, machine learning capabilities are moved closer to the data source.

Scope: The AIoT Working Group aims to explore the integration of artificial intelligence (AI) technologies into the Internet of Things (IoT) systems and devices, enabling them to interact with their environment in more sophisticated and intelligent ways. The working group will focus on the following areas:

• AIoT Architectures, Frameworks, and Algorithms: Developing novel AIoT architectures, frameworks, and algorithms for deploying state-of-the-art machine learning and deep learning algorithms on IoT and edge devices, realising the so-called Edge AI or Edge Intelligence;
• AIoT Applications: Identifying and exploring new AIoT applications, including healthcare, smart homes, industrial automation, transportation, and digital agriculture;
• Standards and Interoperability: Developing standards and protocols for AIoT systems and ensuring interoperability between different platforms and devices; and
• Ethics and Security: Addressing ethical and security concerns related to AIoT, such as privacy, transparency, and accountability.

• 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