State Space Model based Trust Evaluation over Wireless Sensor Networks: An Iterative Particle Filter Approach

State Space Model based Trust Evaluation over Wireless Sensor Networks: An Iterative Particle Filter Approach
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基于状态空间模型的无线传感器网络信任评估:一种迭代粒子滤波方法

DOI:
10.1049/joe.2016.0373
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发表时间:
2016-04
影响因子:
2.7
通讯作者:
Shi Cheng
Shi Cheng
中科院分区:
--
文献类型:
--
作者:
Bin Liu;Shi Cheng

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在本文中,我们提出了一种状态空间建模方法的信任评估在无线传感器网络。在我们的状态空间信任模型(SSTM)中,每个传感器节点都与一个信任度量相关联,该度量度量从该节点传输的数据在多大程度上更好地被服务器节点信任。给定SSTM,我们将信任评估问题转化为一个非线性状态过滤问题。为了估计基于SSTM的状态,提出了一种与粒子滤波器协同工作的逐分量迭代状态推断过程,因此所得到的算法被称为迭代粒子滤波器(IPF)。IPF算法的计算复杂度理论上与状态的维数线性相关。这个属性是理想的,特别是高维的信任评估和状态过滤问题。通过仿真和真实的数据分析,对算法的性能进行了评估。
In this paper we propose a state space modeling approach for trust evaluation in wireless sensor networks. In our state space trust model (SSTM), each sensor node is associated with a trust metric, which measures to what extent the data transmitted from this node would better be trusted by the server node. Given the SSTM, we translate the trust evaluation problem to be a nonlinear state filtering problem. To estimate the state based on the SSTM, a component-wise iterative state inference procedure is proposed to work in tandem with the particle filter, and thus the resulting algorithm is termed as iterative particle filter (IPF). The computational complexity of the IPF algorithm is theoretically linearly related with the dimension of the state. This property is desirable especially for high dimensional trust evaluation and state filtering problems. The performance of the proposed algorithm is evaluated by both simulations and real data analysis.
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