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

TL;DR

This paper introduces a novel trust evaluation method for wireless sensor networks using a state space model combined with an iterative particle filter, enabling efficient and scalable trust assessment.

Contribution

It develops a new state space trust model and an iterative particle filter algorithm for nonlinear trust evaluation in sensor networks, with linear complexity in state dimension.

Findings

The IPF algorithm performs well in simulations and real data.

The method is computationally efficient for high-dimensional trust evaluation.

The approach improves trust assessment accuracy in wireless sensor networks.

Abstract

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.