6G assisted federated learning for continuous monitoring in wireless sensor network using game theory-Reference-Cited by-同舟云学术

6G assisted federated learning for continuous monitoring in wireless sensor network using game theory

Published:2023-02-16 Issue: Volume: Page:
ISSN:1022-0038
Container-title:Wireless Networks
language:en
Short-container-title:Wireless Netw

Author:

Phani Praveen S.,Ali Mohammed Hasan,Jarwar Muhammad Aslam,Prakash Chander,Reddy Chavva Ravi Kishore,Malliga L.,Chandru Vignesh C.

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Information Systems

Link

https://link.springer.com/content/pdf/10.1007/s11276-023-03249-0.pdf

Reference28 articles.

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2. Jadhav, S., & Jadhav, S. (2021). An organized study of congestion control approaches in wireless sensor networks. Future trends in 5G and 6G: Challenges, architecture, and applications (pp. 1–23). CRC Press.

3. Hui, Y., Cheng, N., Huang, Y., Chen, R., Xiao, X., Li, C., & Mao, G. (2021). Personalized vehicular edge computing in 6G. IEEE Network, 9, 5920–5931.

4. Jaiswal, K., & Anand, V. (2021). A Grey-Wolf-based Optimized Clustering approach to improve QoS in wireless sensor networks for IoT applications. Peer-to-Peer Networking and Applications 1–20.

5. Du, J., Jiang, C., Wang, J., Ren, Y., & Debbah, M. (2020). Machine learning for 6G wireless networks: Carrying forward enhanced bandwidth, massive access, and ultrareliable/low-latency service. IEEE Vehicular Technology Magazine, 15(4), 122–134.

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