基于灰色理论的无线传感器网络信任模型

针对无线传感器网络(WSN)中通信节点精确评估的问题,提出了一种基于灰色理论的信任模型(GTTM)。该模型充分监测节点行为,构造样本矩阵,以灰色关联思想计算推荐节点的权重,以灰色聚类思想计算节点的信任值。仿真实验表明,与经典的基于信誉的信任管理框架(RFSN)模型比较,GTTM网络中通信节点的信任值收敛更加平缓,能够抵御恶意推荐,及时降低不可信节点的信任值,在网络遭受攻击时仍能获得较高的交易成功率;与基于Bayes估计的信任计算模(TCM-BE)比较,即使在推荐样本较少的情况下,GTTM仍能保持较低的恶意节点误报率。实验结果表明,所提模型能够准确评估节点的可信度,保证网络的可靠运行。

Trust model for wireless sensor network based on grey theory

Focusing on the issue of accurate assessment of the communication nodes in Wireless Sensor Network (WSN), a Grey Theory-based Trust Model named GTTM was proposed. The proposed model fully monitored the behaviors of nodes in network, builded sample matrix, and computed the weights of the recommending nodes by grey relational method, and computed nodes' trust values through the clustering algorithm of grey theory. The simulation experiments showed that compared with the classic Reputation-based Framework for Sensor Networks (RFSN) model, the convergence of the trust value of nodes in GTTM network is more gentle; GTTM could resist malicious recommendation and reduce the values of untrusted nodes in a timely manner, could still get a high rate of successful trading when the network suffered attacks. Compared with the Trust Computation Model based on Bayes Estimation (TCM-BE) model based on Bayes estimation, even under the circumstances of less recommended samples, GTTM still could keep low rate of false positive malicious nodes. The experimental results show that the GTTM can evaluate the trust values of nodes more accurately and ensure the reliable operation of the network.