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Xiaoqiang Di
Changchun University of Science and Technology
Corresponding Author
ORCiD: https://orcid.org/0000-0001-9432-4564
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Temporal satellite networks can accurately describe the dynamic process of satellite networks by considering the interaction relationship and interaction sequence between satellite nodes. In addition, the measurement of node importance in satellite networks plays a crucial role in understanding the structure and function of the network. The classical supra-adjacency matrix (SAM) temporal model identifies the key nodes in the temporal network to some extent, which ignores the differences of inter-layer connectivity relationships leading to the inability to reflect the dynamic variations of satellite nodes. Therefore, the evaluation method based on time slot correlation is proposed to measure the importance of satellite nodes in this paper. Firstly, the correlation coefficient of time slot nodes is defined to measure the coupling relationship of adjacent time slots. Secondly, the dynamic supra-adjacency matrix (DSAM) temporal network model is proposed considering the correlation between adjacent time slots and the characteristics of link time. Finally, the node importance ranking results in each time slot and a global perspective are obtained by utilizing the eigenvector centrality. Through experimental simulations of the Iridium and Orbcomm constellations, it is demonstrated that the DSAM method has a more accurate recognition rate and is more stable than the SAM method.
Keywords
Temporal satellite network, Time slot coefficient, Supra-adjacency matrix, Eigenvector centrality, Key nodes
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This preprint is under consideration at EURASIP Journal on Wireless Communications and Networking. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
Xiaoqiang Di
Changchun University of Science and Technology
Corresponding Author
ORCiD: https://orcid.org/0000-0001-9432-4564
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 09 Jun, 2021
No community comments so far
Editorial decision: Major revision
On 01 Aug, 2021
Review #1 received
Received 27 Jul, 2021
Review #2 received
Received 19 Jul, 2021
Review #3 received
Received 19 Jul, 2021
Reviewer #3 agreed
On 04 Jul, 2021
Reviewer #2 agreed
On 04 Jul, 2021
Reviewer #1 agreed
On 30 Jun, 2021
Reviewers invited
Invitations sent on 30 Jun, 2021
Reviews received
Received 30 Jun, 2021
First submitted
On 02 Jun, 2021
Editor assigned
On 01 Jun, 2021
Submission checks complete
On 01 Jun, 2021
Editor invited
On 01 Jun, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Temporal satellite networks can accurately describe the dynamic process of satellite networks by considering the interaction relationship and interaction sequence between satellite nodes. In addition, the measurement of node importance in satellite networks plays a crucial role in understanding the structure and function of the network. The classical supra-adjacency matrix (SAM) temporal model identifies the key nodes in the temporal network to some extent, which ignores the differences of inter-layer connectivity relationships leading to the inability to reflect the dynamic variations of satellite nodes. Therefore, the evaluation method based on time slot correlation is proposed to measure the importance of satellite nodes in this paper. Firstly, the correlation coefficient of time slot nodes is defined to measure the coupling relationship of adjacent time slots. Secondly, the dynamic supra-adjacency matrix (DSAM) temporal network model is proposed considering the correlation between adjacent time slots and the characteristics of link time. Finally, the node importance ranking results in each time slot and a global perspective are obtained by utilizing the eigenvector centrality. Through experimental simulations of the Iridium and Orbcomm constellations, it is demonstrated that the DSAM method has a more accurate recognition rate and is more stable than the SAM method.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
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