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Mobile Information Systems
Volume 2016, Article ID 7628231, 15 pages
Research Article

LSOT: A Lightweight Self-Organized Trust Model in VANETs

1School of Computer Science and Technology, Xidian University, Xi’an 710071, China
2School of Cyber Engineering, Xidian University, Xi’an 710071, China
3School of Telecommunication Engineering, Xidian University, Xi’an 710071, China

Received 23 June 2016; Accepted 13 November 2016

Academic Editor: Elio Masciari

Copyright © 2016 Zhiquan Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


With the advances in automobile industry and wireless communication technology, Vehicular Ad hoc Networks (VANETs) have attracted the attention of a large number of researchers. Trust management plays an important role in VANETs. However, it is still at the preliminary stage and the existing trust models cannot entirely conform to the characteristics of VANETs. This work proposes a novel Lightweight Self-Organized Trust (LSOT) model which contains trust certificate-based and recommendation-based trust evaluations. Both the supernodes and trusted third parties are not needed in our model. In addition, we comprehensively consider three factor weights to ease the collusion attack in trust certificate-based trust evaluation, and we utilize the testing interaction method to build and maintain the trust network and propose a maximum local trust (MLT) algorithm to identify trustworthy recommenders in recommendation-based trust evaluation. Furthermore, a fully distributed VANET scenario is deployed based on the famous Advogato dataset and a series of simulations and analysis are conducted. The results illustrate that our LSOT model significantly outperforms the excellent experience-based trust (EBT) and Lightweight Cross-domain Trust (LCT) models in terms of evaluation performance and robustness against the collusion attack.