• International journal of advanced smart convergence
• /
• v.8 no.4
• /
• pp.169-178
• /
• 2019

Due to the characteristics of an ad hoc network without a control center, self-organization, and flexible topology, the trust evaluation of the nodes in the network is extremely difficult. Based on the analysis of ad hoc networks and the blockchain technology, a blockchain-based node-level trust evaluation model is proposed. The concepts of the node trust degree of the HASH list on the blockchain and the perfect reward and punishment mechanism are adopted to construct the node trust evaluation model of the ad hoc network. According to the needs of different applications the network security level can be dynamically adjusted through changes in the trust threshold. The simulation experiments demonstrate that ad-hoc on-demand distance vector(AODV) Routing protocol based on this model of multicast-AODV(MAODV) routing protocol shows a significant improvement in security compared with the traditional AODV and on-demand multipath distance vector(AOMDV) routing protocols.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.6
• /
• pp.3273-3308
• /
• 2017

Building appropriate trust evaluation models is an important research issue for security guarantee in social networks. Most of the existing works usually consider the trust values at the current time slot, and model trust as the stochastic variable. However, in fact, trust evolves over time, and trust is a stochastic process. In this paper, we propose a novel time-variant stochastic trust evaluation (TSTE) model, which models trust over time and captures trust evolution by a stochastic process. Based on the proposed model, we derive the time-variant bound of untrustworthy probability, which provides stochastic trust guarantee. On one hand, the time-variant trust level of each node can be measured by our model. Meanwhile, by tolerating nodes with relatively poor performance, our model can effectively improve the node resource utilization rate. Numerical simulations are conducted to verify the accuracy and consistency of the analytical bounds on distinguishing misbehaved nodes from normal ones. Moreover, simulation results on social network dataset show the tradeoff between trust level and resource utilization rate, and verify that the successful transmission rate can be improved by our model.