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Call for papers - Integrating Large Models and Edge Sensing in Next Generation Networks

Guest Editors

Peiyan Yuan, PhD, Henan Normal University, China
Tarik Taleb, PhD, Ruhr University Bochum, Germany
Chenyang Wang, PhD, Shenzhen University, China
Chuan Sun, PhD, Nanyang Technological University, Singapore
Xiaoqiang Zhu, PhD, Beijing Jiaotong University, China
Xiaoyan Zhao, PhD, Henan Normal University, China

Submission Status: Open   |   Submission Deadline: 11 June 2025

EURASIP Journal on Wireless Communications and Networking is calling for submissions to our Collection on Integrating Large Models and Edge Sensing in Next Generation Networks. The rapid proliferation of IoT devices has led to an exponential increase in data generation at the network’s edge, necessitating efficient data extraction and utilization. This convergence enhances the capabilities of edge devices to process data locally and make intelligent decisions in real-time, reducing reliance on central servers and minimizing latency. By addressing the computational and energy constraints of edge devices, this integration promises more efficient, secure, and responsive networks. The topic is essential for developing solutions that maintain optimize network performance, paving the way for innovative applications in smart cities, healthcare, and other IoT domains. The special issue on "Integrating Large Models and Edge Sensing in next generation networks" provides a forum that brings together industry and academia, engineers and researchers to discuss up-to-date developments in integrating large models and edge sensing in next generation networks. 

The special issue invites submissions of unpublished works on (but not limited to) the following topics:
• Optimization of large models for edge sensing
• Multi-modal edge sensing and data fusion
• energy-efficient optimization for edge sensing applications
• Real-time edge sensing with large models
• Multi-modal data processing at the edge
• Semantic communication integrated with edge sensing
• Distributed training of large models for edge sensing
• Network architecture for large model and edge sensing integration
• Innovative network management for edge-large model deployment
• Deploying large models for edge sensing networking
• Distributed and power-efficient training of large models in edge sensing networks
• The role of semantic communications/large models in emerging 6G applications
• Edge-cloud systems for semantic communication Networks
• Signal processing for integrating semantic communications
• Information-centric and content-centric networks with large models
• Interdisciplinary research for integrating edge sensing/large models
• Information theory for integrating semantic communications, edge sensing and large models
• Personalization of FL with large models in cloud-edge networks
• Overcoming data interoperability challenges using large models in edge sensing

Article publication will be continuously, i.e. articles are published into the special issue one at a time as soon as they are ready. 


New Content ItemThis Collection supports and amplifies research related to SDG 9: Industry, Innovation & Infrastructure

Meet the Guest Editors

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Peiyan Yuan, PhD, Henan Normal University, China

Peiyan Yuan received the B.S., the M.S. and Ph.D. degrees from Henan Normal University, Xinxiang, China, Wuhan University of Technology, Wuhan, China, and Beijing University of Posts and Telecommunications, Beijing, China, respectively, all in computer science. He is a professor of Computer Science, Henan Normal University. He also worked as a post-doctoral researcher at the University of Texas at Dallas, USA. His research interests include future networks and distributed systems, and he authored or coauthored more than sixty papers and two books in these fields. He won the first national scholarship for Ph. D students from Ministry of Education of the P. R. China in 2012, and he received two Best Paper Awards from IEEE CSE in 2014 and CCF NCSC in 2022, respectively. He is a senior member of IEEE and CCF, and a member of ACM.

Tarik Taleb, PhD, Ruhr University Bochum, Germany

Tarik Taleb received the B.E. degree (with distinction) in information engineering and the M.Sc. and Ph.D. degrees in information sciences from Tohoku University, Sendai, Japan, in 2001, 2003, and 2005, respectively. He is currently a Full Professor at Ruhr University Bochum, Germany. He was a Professor with the Center of Wireless Communications (CWC), University of Oulu, Oulu, Finland. He is the founder of ICTFICIAL Oy, and the founder and the Director of the MOSA!C Lab, Espoo, Finland. From October 2014 to December 2021, he was an Associate Professor with the School of Electrical Engineering, Aalto University, Espoo, Finland. Prior to that, he was working as a Senior Researcher and a 3GPP Standards Expert with NEC Europe Ltd., Heidelberg, Germany. Before joining NEC and till March 2009, he worked as an Assistant Professor with the Graduate School of Information Sciences, Tohoku University, in a lab fully funded by KDDI. From 2005 to 2006, he was a Research Fellow with the Intelligent Cosmos Research Institute, Sendai. He has been directly engaged in the development and standardization of the Evolved Packet System as a member of the 3GPP System Architecture Working Group. His current research interests include AI-based network management, architectural enhancements to mobile core networks, network softwarization and slicing, mobile cloud networking, SDN/NFV, software-defined security, and mobile multimedia streaming.

Chenyang Wang, PhD, Shenzhen University, China

Chenyang Wang is currently a postal researcher in the College of Computer and Software Engineering, Shenzhen University, Guangdong, China, and he holds his PhD degree from the School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China. He also worked as a visiting researcher at the Aalto University in Finland. His research interests include large models, edge intelligence, and deep learning. He received the Best Student Paper Award of the 24th International Conference on Parallel and Distributed Systems by IEEE Computer Society in 2018. He also received the Best Paper Award from the IEEE International Conference on Communications in 2021. In 2022, he received the ``IEEE ComSoc Asia-Pacific Outstanding Paper Award".

Chuan Sun, PhD, Nanyang Technological University, Singapore

Chuan Sun received the B.S. degree from Wuhan University of Science and Technology, Wuhan, China, in 2017, his Ph.D. degree from the School of Big Data and Software Engineering, Chongqing University, Chongqing, China, in 2023. He is currently a research fellow at the College of Computing and Data Science, Nanyang Technological University, 50 Nanyang Avenue, Singapore. His current research interests include federated learning, reinforcement learning, and mobile edge computing. He has published more than 20 journal/conference papers in IEEE JSAC, TSC, IEEE Network, ICPADS and so on.

Xiaoqiang Zhu, PhD, Beijing Jiaotong University, China

Xiaoqiang Zhu received the Ph.D. degree in software engineering from Tianjin University, China, in 2022, and the M.S. degree in computer science from Dalian University of Technology, China, in 2018. He served as a joint Ph.D. student at ETH Zurich, Switzerland, supported by the China Scholarship Council in 2021. He is currently an Assistant Professor (Lecturer) with the School of Software Engineering, Beijing Jiaotong University, China. He has published scientific papers in international journals, such as IEEE COMST, TMC, TNSE, etc., and his research interests include the Internet of Things, machine learning, and privacy protection.

Xiaoyan Zhao, PhD, Henan Normal University, China

Xiaoyan Zhao is an Associate Professor of Computer Science at the Henan Normal University since 2015. She received the Ph.D. degree in Communication and Information System at the National Digital Switching System Engineering and Technological Center in 2018 and the M.S. degree in Communication and Information System from Wuhan University, China, in 2006, and B.S. degree in Electronic Science and Technology from Zhengzhou University, China, in 2003. She also worked as a visiting scholar at the University of Ottawa in Canada. She has published scientific papers in international journals and conferences, such as IoT, IEEE TVT, ICDCS, etc., and her research interests are in the sub-areas of Internet of Things, edge computing, and edge semantic communication.

About the Collection

EURASIP Journal on Wireless Communications and Networking is calling for submissions to our Special Issue on Integrating Large Models and Edge Sensing in Next Generation Networks. The rapid proliferation of IoT devices has led to an exponential increase in data generation at the network’s edge, necessitating efficient data extraction and utilization. This convergence enhances the capabilities of edge devices to process data locally and make intelligent decisions in real-time, reducing reliance on central servers and minimizing latency. By addressing the computational and energy constraints of edge devices, this integration promises more efficient, secure, and responsive networks. The topic is essential for developing solutions that maintain optimize network performance, paving the way for innovative applications in smart cities, healthcare, and other IoT domains. The special issue on "Integrating Large Models and Edge Sensing in next generation networks" provides a forum that brings together industry and academia, engineers and researchers to discuss up-to-date developments in integrating large models and edge sensing in next generation networks. 

The special issue invites submissions of unpublished works on (but not limited to) the following topics:
• Optimization of large models for edge sensing
• Multi-modal edge sensing and data fusion
• energy-efficient optimization for edge sensing applications
• Real-time edge sensing with large models
• Multi-modal data processing at the edge
• Semantic communication integrated with edge sensing
• Distributed training of large models for edge sensing
• Network architecture for large model and edge sensing integration
• Innovative network management for edge-large model deployment
• Deploying large models for edge sensing networking
• Distributed and power-efficient training of large models in edge sensing networks
• The role of semantic communications/large models in emerging 6G applications
• Edge-cloud systems for semantic communication Networks
• Signal processing for integrating semantic communications
• Information-centric and content-centric networks with large models
• Interdisciplinary research for integrating edge sensing/large models
• Information theory for integrating semantic communications, edge sensing and large models
• Personalization of FL with large models in cloud-edge networks
• Overcoming data interoperability challenges using large models in edge sensing

Article publication will be continuously, i.e. articles are published into the special issue one at a time as soon as they are ready. 

Image credit: © Tierney/stock.adobe.com

There are currently no articles in this collection.

Submission Guidelines

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This Collection welcomes submission of original Research Articles, Reviews, and Methodological Papers. Should you wish to submit a different article type, please read our submission guidelines to confirm that type is accepted by the journal. Articles for this Collection should be submitted via our submission system, EM (Editorial Manager).

When submitting your manuscript, please select the relevant collection "Integrating Large Models and Edge Sensing in Next Generation Networks" as a response to the question “Are you submitting this manuscript to a Special Issue?” under the Additional Information section. Authors should also express their interest in the Collection in their cover letter.

Articles will undergo the journal’s standard peer-review process and are subject to all of the journal’s standard policies. Articles will be added to the Collection as they are published.

The Editors have no competing interests with the submissions which they handle through the peer review process. The peer review of any submissions for which the Editors have competing interests is handled by another Editorial Board Member who has no competing interests.