Deep Neural Network Routing with Dynamic Space Division for 3D UAV FANETs-Reference-Cited by-同舟云学术

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Deep Neural Network Routing with Dynamic Space Division for 3D UAV FANETs

Published:2022-03-07 Issue:3 Volume:125 Page:2003-2028
ISSN:0929-6212
Container-title:Wireless Personal Communications
language:en
Short-container-title:Wireless Pers Commun

Author:

Zhang HongGuang^ORCID,Wang Tao,Liu TingTing,Zhang QingSong,Liu YuanAn

Funder

National Natural Science Foundation of China

Research Initiative of Ideological and Political Theory Teachers

Hebei Key Laboratory of Safety Monitoring of Mining Equipment

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Computer Science Applications

Link

https://link.springer.com/content/pdf/10.1007/s11277-022-09602-4.pdf

Reference39 articles.

1. Bekmezci, I., Sahingoz, O. K., & Temel, Ş. (2013). Flying ad-hoc networks (FANETs): A survey. Ad Hoc Networks, 11(3), 1254–1270. https://doi.org/10.1016/j.adhoc.2012.12.004

2. Oubbati, O. S., Lakas, A., Zhou, F., Güneş, M., & Yagoubi, M. B. (2017). A survey on position-based routing protocols for Flying Ad hoc Networks (FANETs). Vehicular Communications, 10, 29–56. https://doi.org/10.1016/j.vehcom.2017.10.003

3. Chriki, A., Touati, H., Snoussi, H., & Kamoun, F. (2019). FANET: communication, mobility models and security issues. Computer Networks, 163, 106877. https://doi.org/10.1016/j.comnet.2019.106877

4. Hong, C., Zhang, Y., Xiong, Z., Xu, A., Chen, H., & Ding, W. (2018). FADS: Circular/spherical sector based forwarding area division and adaptive forwarding area selection routing protocol in WSNs. Ad Hoc Neworks, 70, 121–134. https://doi.org/10.1016/j.adhoc.2017.11.013

5. Senthil Kumar, V., & Prasanth, K. (2020). Weighted rendezvous planning on Q-Learning based adaptive zone partition with PSO based optimal path selection. Wireless Personal Communications, 110(1), 153–167. https://doi.org/10.1007/s11277-019-06717-z

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