Name
Abstract | ||
|---|---|---|
In this paper, we discuss the risk assessment of ad hoc networks, which have highly dynamic topology, open access of wireless channels, and vulnerable data communication. Conventional risk assessment methods are subjective and unreliable as some nodes reveal little information, and the quantity of samples is limited in ad hoc networks. To solve this problem, we propose a GRAP method, which includes grey relational projection (GRP), grey prediction, and grey decision making. Our scheme is designed to assess nodes' risk under limited circumstances such as small number of samples, incomplete information and lack of experience. Compared with principal component analysis, GRAP has demonstrated better performance and more flexible characteristics. To further the practicability of this method, we utilize a dynamic grey prediction, which shows high accuracy for decision making. In our scheme, four major nodes' attributes are selected, and the experiment results suggest that our model is more effective and efficient for risk assessment than principal component analysis in ad hoc networks. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.jpdc.2010.11.012 | J. Parallel Distrib. Comput. |
Keywords | Field | DocType |
risk assessment,principal component analysis,grey relational projection,dynamic grey prediction,grey decision,dynamic topology,grey prediction,grap method,grey risk assessment,incomplete information,ad hoc networks,conventional risk assessment method,ad hoc network | Small number,Data mining,Wireless,Computer science,Risk assessment,Communication channel,Artificial intelligence,Wireless ad hoc network,GRAP,Complete information,Principal component analysis,Machine learning | Journal |
Volume | Issue | ISSN |
71 | 9 | Journal of Parallel and Distributed Computing |
Citations | PageRank | References |
16 | 0.80 | 21 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cai Fu | 1 | 34 | 7.05 |
| Xiang Gao | 2 | 37 | 1.92 |
| Ming Liu | 3 | 377 | 44.40 |
| Xiaoyang Liu | 4 | 270 | 34.49 |
| Lansheng Han | 5 | 53 | 13.13 |
| Jing Chen | 6 | 285 | 60.83 |