Credibility Propagation for Robust Data Aggregation in WSNs

Trust and reputation systems are widely employed in WSNs in order to help decision making processes by assessing trustworthiness of sensor nodes in a data aggregation process. However, in unattended and hostile environments, some sophisticated malicious attacks such as collusion attacks can distort the computed trust scores and lead to low quality or deceptive services as well as to undermine the aggregation results. Thus, taking into account the collusion attacks for developing a secure trust-based data aggregation in unattended environments has become an important research issue. In this paper, we address this problem by proposing a novel collaborativebased trust framework for WSNs, which is based on the introduced concept of credibility propagation. In this method, the trustworthiness of a sensor node is evaluated from the amount of credibility that such a node collects from other nodes. Moreover, we obtain the statistical parameters of sensors errors including sensors’ variances from such credibility values. Accordingly, we propose an iterative filtering algorithm to recursively compute the credibility and variance of all sensors. Following this algorithm, an estimate of the true value of the signal can be effectively obtained through the maximum likelihood estimation. Furthermore, we augment the proposed trust framework with a collusion detection and revocation method as well as data streaming algorithm. Extensive experiments across a wide variety of configurations over both real-world and synthetic datasets demonstrate the efficiency and effectiveness of our approach.