|Table of Contents|

Artificial immunitybased trust detection method for wireless sensor networks

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2014年03期
Page:
318-
Research Field:
Publishing date:

Info

Title:
Artificial immunitybased trust detection method for wireless sensor networks
Author(s):
Shen HaiboJiang HaitaoZhuang KechenZhang Hong
School of Computer Science and Engineering,NUST,Nanjing 210094,China
Keywords:
artificial immunitywireless sensor networkstrust detectionclusteringcluster head nodestrust valuesnegative selection algorithmdetectorsbase stationsmember nodes
PACS:
TP393
DOI:
-
Abstract:
Aiming at the problem that the network lifetimes of cluster head nodes are reduced because of a large number of computations to do in traditional trust evaluation models of wireless sensor networks(WSNs) based on clustering,an artificial immune method is proposed to evaluate the trust values of nodes in WSNs.Base stations use detectors generated by the negative selection algorithm for trust detection of nodes.Cluster head nodes send the trust properties of member nodes to base stations instead of aggregating the trust values of member nodes.The burdens on the cluster head nodes are cut and the lifetimes of WSNs are extended.The simulation results show that the method proposed here extends network lifetimes and has higher detection rates compared with existing methods.When the percentages of nonconfidence nodes are not more than 40%,the detection rates are above 90%.

References:

[1]Wong E Y C,Yeung H S C,Lau H Y K.Immunitybased hybrid evolutionary algorithm for multiobjective optimization in global container repositioning[J].Engineering Applications of Artificial Intelligence,2009,22(6):842-854.
[2]Zhou Dexin,Fan Zhicheng,Zhang Wenlin.Research on AMU fault detection algorithm based on immune danger theory[A].Proceedings of the 2011 Chinese Control and Decision Conference[C].Mianyang,China:IEEE,2011:1957-1961.
[3]Okamoto T,Ishida Y.Evaluations for immunitybased anomaly detection with dynamic updating of profiles[J].Artificial Life and Robotics,2010,15(2):225-228.
[4]廖俊,刘耀宗,姜海涛,等.基于人工免疫的MANET不端行为检测模型[J].南京理工大学学报,2011,35(5):652-658.
Liao Jun,Liu Yaozong,Jiang Haitao,et al.Artificial immunitybased misbehavior detection architecture for mobile ad hoc networks[J].Journal of Nanjing University of Science and Technology,2011,35(5):652-658.
[5]Shaikh R A,Jameel H,d’Auriol B J,et al.Groupbased trust management scheme for clustered wireless sensor networks[J].IEEE Transactions on Parallel and Distributed Systems,2009,20(11):1698-1712.
[6]Zhang Junqi,Shankaran R,Orgun M A,et al.A trust management architecture for hierarchical wireless sensor networks[A].Proceedings of 2010 IEEE 35th Conference on Local Computer Networks[C].Denver,USA:IEEE,2010:264-267.
[7]Zhou Youcai,Huang Tinglei,Wang Wei.A trust establishment scheme for clusterbased sensor networks[A].Proceedings of the 2009 5th International Conference on Wireless Communications,Networking and Mobile Computing[C].Beijing,China:IEEE,2009:1-4.
[8]Zhao Zhibin,Dong Xiaomei,Yao Lan,et al.Research on biological immunity principle based security model for wireless sensor network[A].Proceedings of the 2009 Chinese Control and Decision Conference[C].Guilin,China:IEEE,2009:4687-4691.
[9]Junbeom H,Younho L,Hyunsoo Y,et al.Trust evaluation model for wireless sensor networks[A].The 7th International Conference on Advanced Communication Technology,2005,ICACT 2005[C].Phoenix Park,GangwonDo,Korea:IEEE,2005:491-496.
[10]Hu Ronghua,Lou Peihuang,Zhao Peng.A novel approach of detector generation for realvalued negative selection algorithm[J].Applied Mechanics and Materials,2012,121-126:3736-3740.

Memo

Memo:
-
Last Update: 2014-06-30