Nature Scientific Reports
npj Wireless Technology

Collection

Lightweight Edge AI for Industrial IoT (Light eAI-IIoT)

The Industrial Internet of Things (IIoT) is transforming modern industries by enabling intelligent, data driven operations at the edge of networks. As these systems grow in scale and complexity, optimizing the energy consumption of Artificial Intelligence (AI) and Machine Learning (ML) techniques becomes essential for sustainable and reliable performance. In particular, data collected by IIoT devices enables predictive maintenance to automate fault detection and diagnosis, improve quality control, enhance worker safety, and unlock supply chain traceability.

As AI and ML become increasingly integrated into IIoT systems, new deployment strategies are emerging to address the stringent constraints of industrial environments.

Nature Collection Presentation
Submission Deadline:
January 29, 2027
Submit Now

About This Collection

This journal special issue especially focuses on the potential and challenges of exploiting Lightweight AI at the edge of networks, including at the IIoT devices themselves. In particular, the issue will explore the synergy of AI compression techniques and wireless communications/sensing, and investigate the potential trade-offs over multiple dimensions such as low AI carbon footprint and excellent wireless communications/sensing performances, with the goal of enabling future IIoT and smart factory applications.

We encourage submissions that explore novel algorithms, hardware architectures, and system designs that enable efficient, low-power sensing and analysis at the edge. Interdisciplinary work that combines insights from wireless technology, signal processing, and embedded AI is particularly welcome.

Topics of Interest

  • Energy efficient wireless communications/sensing for IIoT
  • Edge AI and ML-aided wireless communications and networking
  • Tiny ML for wireless communications/sensing
  • AI compression for wireless communications/sensing
  • Edge AI network architectures for IIoT
  • Low-power acoustic sensing networks
QR Code to Nature Collection

Scan to access the collection

Visit Nature Collection
Editorial Team

Guest Editors

MK

Megumi Kaneko

PhD, HDR

National Institute of Informatics & The University of Tokyo, Japan

Guest Editor
OB

Olivier Berder

PhD

Univ Rennes, CNRS, IRISA, France

Guest Editor
RG

Robin Gerzaguet

PhD

Univ Rennes, CNRS, IRISA, France

Guest Editor
JW

Julien Weber

MEng

Wavely, France

Guest Editor
KK

Kenichi Kawamura

MD

NTT, Inc., Japan

Guest Editor

We welcome submissions from researchers worldwide working on energy efficient wireless communication/sensing for IIoT and edge AI applications.