D-S Evidence Theory Based Trust Detection Scheme in Wireless Sensor Networks

D-S Evidence Theory Based Trust Detection Scheme in Wireless Sensor Networks

Kai Yang (Department of Electronics Technology, Engineering University of Chinese Armed Police Force, Xi'an, China), Shuguang Liu (Department of Electronics Technology, Engineering University of Chinese Armed Police Force, Xi'an, China), Xiuguang Li (Department of Electronics Technology, Engineering University of Chinese Armed Police Force, Xi'an, China) and Xu An Wang (Department of Electronics Technology, Engineering University of Chinese Armed Police Force, Xi'an, China)
Copyright: © 2016 |Pages: 12
DOI: 10.4018/IJTHI.2016040104
OnDemand PDF Download:
$30.00
List Price: $37.50

Abstract

Wireless sensor networks (WSNs) is a key technology which is deployed under the assumption that participating sensors are cooperative in forwarding each other's packets. However, some nodes may behave selfishly by dropping other's packets or refusing to provide services in an attempt to maximize its throughput with minimum expense. In this paper, the authors present a novel detection scheme based on Dempster-Shafer (D-S) evidence theory in WSN to detect and isolate misbehavior sensors. However, when the scheme is operated, counter-intuitive results may appear. To overcome this problem, this paper improves the original D-S evidence theory, which defines a new variable to modify the collected evidence before combination and then combines these evidences according to Dempster combination rule. Simulation results show that this scheme can detect and isolate misbehavior sensors effectively and accurately, suppress nodes collusion and improve network performance. Compared with other existing detection scheme, this scheme has more security, robustness and accuracy.
Article Preview

Trust and reputation models in WSNs have attracted more and more attentions in recent years (Raje & Sakhare, 2014; Kumar, Titus & Thekkekara, 2012; Tk-Kim, 2008; Xia, Yu, Pan, Cheng & Sha, 2015; Singh & Verma, 2015; Sivakumar & Duraisamy, 2015; Wei, Qing & Nan, 2015; Chen, He, Liang & Chen, 2015; Tian, Liu & Chen, 2015; Abassi, 2015; Ahmed, Bakar, Channa & Khan, 2015; Musau, Wang & Abdullahi, 2014; Zhang & Gu, 2014).

In ATRM (Boukercha, Xua & K. El-Khatibb, 2007), which is a trust and reputation management scheme based on agent in WSNs, trust and reputation management is operated with title costs in local area, such as additional message and time delay. However, mobile agents must be started by trust entities. Chen et al. (H. Chen, G. Wu, H. F. Zhou & X. Gao, 2007) present a trust model based on agent in WSNs, which uses watchdog scheme to observe nodes behaviors and broadcast their trust evaluation. The DRBTS (Distributed Reputation-based Beacon Trust System) presented in (Srinivasan, Teitelbaum & Wu, 2006) is a scheme based on reputation. This scheme uses Beacon Nodes (BNs) to observe node. However, in order to trust the information from BNs, sensors have to obtain enough support from more than half neighbors. BTRM-WSNS [1] is a bio-inspired trust and reputation model in WSNs. Each node contains pheromone tracks leading to each neighbor nodes. In CONFIDANT (Buchegger & Boudec, 2002) each node monitors the behaviors of the next-hop neighbors. Both trust relationship and routing decisions are based on the experienced, the observed and the reported routing and forwarding behaviors of other nodes. SORI (Q. He, D. Wu & P. Khosla, 2004) scheme is proposed to encourage packet forwarding and punish selfish behaviors. The propagation of reputation guarantees its security by a one-way hash chain based authentication scheme. Watchdog and Pathrater (Zhong, Chen & Yang, 2003) are two extension versions of DSR.

Complete Article List

Search this Journal:
Reset
Open Access Articles: Forthcoming
Volume 14: 4 Issues (2018): 1 Released, 3 Forthcoming
Volume 13: 4 Issues (2017)
Volume 12: 4 Issues (2016)
Volume 11: 4 Issues (2015)
Volume 10: 4 Issues (2014)
Volume 9: 4 Issues (2013)
Volume 8: 4 Issues (2012)
Volume 7: 4 Issues (2011)
Volume 6: 4 Issues (2010)
Volume 5: 4 Issues (2009)
Volume 4: 4 Issues (2008)
Volume 3: 4 Issues (2007)
Volume 2: 4 Issues (2006)
Volume 1: 4 Issues (2005)
View Complete Journal Contents Listing