Industrial Internet

The Industrial Internet (IIoT) aims to connect and integrate industrial devices and systems for enhanced efficiency, automation, and predictive maintenance. Current research heavily focuses on addressing challenges like data privacy and resource limitations through techniques such as federated learning, reinforcement learning (particularly multi-agent and deep reinforcement learning), and graph neural networks for optimized resource allocation, anomaly detection, and soft sensing. These advancements are significantly impacting manufacturing and other industries by improving operational efficiency, enabling predictive maintenance, and facilitating the development of more intelligent and responsive systems.

Papers

April 20, 2022

Representation Learning for Content-Sensitive Anomaly Detection in Industrial Networks
Fabian Kopp
Anomaly Detection Representation Learning Supervised Autoencoder Network Intrusion Detection Industrial Internet

February 3, 2022

Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoT
Zonghang Li, Yihong He, Hongfang Yu, Jiawen Kang, Xiaoping Li, Zenglin Xu, Dusit Niyato
Federated Learning Hierarchical Federated Learning Industrial Internet Industrial Data

February 1, 2022

Learning to Speak on Behalf of a Group: Medium Access Control for Sending a Shared Message
Shaan ul Haque, Siddharth Chandak, Federico Chiariotti, Deniz Gunduz, Petar Popovski
LeArning Abstract Multi Armed Bandit Group Setting Random Access Industrial Internet Medium Access Control

Industrial Internet

Papers

Representation Learning for Content-Sensitive Anomaly Detection in Industrial Networks

Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoT

Learning to Speak on Behalf of a Group: Medium Access Control for Sending a Shared Message