6G Communication and Edge Intelligence in Wireless Sensor Networks
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Communications".
Deadline for manuscript submissions: 31 December 2026 | Viewed by 6494
Special Issue Editors
Interests: wireless communications; next generation wireless networks; mobile edge computing; AI for wireless networks
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Edge computing has attracted extensive attention from both academia and industry in the 5G era, aiming to reduce local computing overheads and latency in uploading data to the cloud by leveraging the computing power of edge servers. In the upcoming 6G era, this approach will be further strengthened. With the development of AI applications, traditional edge computing tasks are evolving into services for model training and inference. One of the main issues to address in the future is how 6G networks can effectively support these services. In these services, devices cooperate with each other or edge servers by exchanging model parameters to complete model inference or training while simultaneously protecting data privacy and addressing the issue of the data island.
However, wireless networks have a significant impact on the performances of these services. On one hand, wireless resources are limited, and frequent model parameter transmissions incur considerable latency. On the other hand, wireless networks are unreliable, and errors in model parameter transmission can lead to model aggregation divergence. Therefore, it is crucial to design efficient communication and model training algorithms to improve the efficiency and accuracy of model training and inference.
This Special Issue will bring together researchers interested in this topic to collectively tackle the above challenges, which may include novel edge computing architectures, algorithms, applications, etc.
The list of possible topics includes, but is not limited to, the following:
- Efficient resource allocation algorithm for edge computing/learning;
- Strategies for heterogeneous distributed learning;
- Asynchronous federated/decentralized learning;
- Theoretical analysis and algorithms for time-varying distributed learning;
- Unreliable wireless distributed learning;
- Topology-aware distributed learning;
- Low latency edge collaborative inference;
- Novel edge computing architecture;
- Multi-model distributed learning over wireless networks;
- Personalized distributed learning over wireless networks;
- Privacy in wireless distributed learning;
- Applications of wireless distributed learning.
Prof. Dr. Guanding Yu
Dr. Shengli Liu
Guest Editors
Manuscript Submission Information
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Keywords
- edge computing
- edge AI
- federated learning
- decentralized learning
- learning latency
- constrained resource
- task-oriented communication
- 6G
- edge inference
- heterogeneous resource
- resource management
- asynchronous aggregation
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