  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.
