Prediction Approach of Critical Node Based on Multiple Attribute Decision Making for Opportunistic Sensor Networks
Joint Authors
Liu, Linlan
Chen, Qifan
Yang, Zhiyong
Guo, Kai
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-04-12
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Predicting critical nodes of Opportunistic Sensor Network (OSN) can help us not only to improve network performance but also to decrease the cost in network maintenance.
However, existing ways of predicting critical nodes in static network are not suitable for OSN.
In this paper, the conceptions of critical nodes, region contribution, and cut-vertex in multiregion OSN are defined.
We propose an approach to predict critical node for OSN, which is based on multiple attribute decision making (MADM).
It takes RC to present the dependence of regions on Ferry nodes.
TOPSIS algorithm is employed to find out Ferry node with maximum comprehensive contribution, which is a critical node.
The experimental results show that, in different scenarios, this approach can predict the critical nodes of OSN better.
American Psychological Association (APA)
Chen, Qifan& Liu, Linlan& Yang, Zhiyong& Guo, Kai. 2016. Prediction Approach of Critical Node Based on Multiple Attribute Decision Making for Opportunistic Sensor Networks. Journal of Sensors،Vol. 2016, no. 2016, pp.1-8.
https://search.emarefa.net/detail/BIM-1110637
Modern Language Association (MLA)
Chen, Qifan…[et al.]. Prediction Approach of Critical Node Based on Multiple Attribute Decision Making for Opportunistic Sensor Networks. Journal of Sensors No. 2016 (2016), pp.1-8.
https://search.emarefa.net/detail/BIM-1110637
American Medical Association (AMA)
Chen, Qifan& Liu, Linlan& Yang, Zhiyong& Guo, Kai. Prediction Approach of Critical Node Based on Multiple Attribute Decision Making for Opportunistic Sensor Networks. Journal of Sensors. 2016. Vol. 2016, no. 2016, pp.1-8.
https://search.emarefa.net/detail/BIM-1110637
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1110637