Proceedings of the International Conference on Communication and Electronic Information Engineering (CEIE 2016)

Study on Combat Network Self-Recovery Mechanism Based on Load Distribution

Authors
Mingxing Zhang, Guangquan Cheng, Shuifa Mei, Yanghe Feng
Corresponding Author
Mingxing Zhang
Available Online October 2016.
DOI
https://doi.org/10.2991/ceie-16.2017.100How to use a DOI?
Keywords
Target Network; Self-recovery Mechanism; Load Distribution
Abstract
Under the combat-targeted network layer description, the paper establishes layered network model for combat-targeted networks so as to facilitate the study on its self-recovery mechanism. Furthermore, it puts forward the optimal self-recovery strategy of the early warning network layer based on the load redistribution theory. Finally, simulation analysis is performed on the feasibility of the previously studied early warning network layer self-recovery mechanism via the simulated attack. The result shows that the target network's self-recovery mechanism can better utilize the combat capability of the remaining nodes while maintaining greater early warning capacity and minimizing the delay in each node type in the network.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Volume Title
Proceedings of the International Conference on Communication and Electronic Information Engineering (CEIE 2016)
Series
Advances in Engineering Research
Publication Date
October 2016
ISBN
978-94-6252-312-8
ISSN
2352-5401
DOI
https://doi.org/10.2991/ceie-16.2017.100How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Mingxing Zhang
AU  - Guangquan Cheng
AU  - Shuifa Mei
AU  - Yanghe Feng
PY  - 2016/10
DA  - 2016/10
TI  - Study on Combat Network Self-Recovery Mechanism Based on Load Distribution
BT  - Proceedings of the International Conference on Communication and Electronic Information Engineering (CEIE 2016)
PB  - Atlantis Press
SP  - 770
EP  - 777
SN  - 2352-5401
UR  - https://doi.org/10.2991/ceie-16.2017.100
DO  - https://doi.org/10.2991/ceie-16.2017.100
ID  - Zhang2016/10
ER  -