GRAP: Grey risk assessment based on projection in ad hoc networks

Authors:
Cai Fu;Xiang Gao;Ming Liu;Xiaoyang Liu;Lansheng Han;Jing Chen
Affiliations:
School of Computer Science and Technology, Huazhong University of Science and Technology, WuHan City, China;Deparment of Management Science, City University of Hong Kong, Hong Kong;School of Computer Science and Technology, Huazhong University of Science and Technology, WuHan City, China;Department of Computer Science and Engineering, Shanghai Jiao Tong University, ShangHai City, China;School of Computer Science and Technology, Huazhong University of Science and Technology, WuHan City, China;School of Computer, Wuhan University, WuHan City, China
Venue:
Journal of Parallel and Distributed Computing
Year:
2011

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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.