Dynamic witness selection for trustworthy distributed cooperative sensing in cognitive radio networks
Cooperative spectrum sensing by secondary user (SU) nodes in cognitive radio networks (CRNs) is a promising approach to increase the spectrum access efficiency and overall network performance. However, unreliable sensing results or malicious behaviors from cooperator SU nodes can be very disruptive and reduce the network performance. Trust and reputation modeling has been identified as one of the potential solutions to address this problem, but the current centralized trust evaluation approach in CRN lacks scalability. Although some decentralized trust models have been proposed in CRN, without proper protection mechanisms, they are vulnerable to collusive behaviors by the witness SU nodes when they share testimonies about the trustworthiness of neighboring SU nodes. In this paper, we propose a clustering based witness selection method to address this problem. By dividing the witness SU nodes testimonies about the trustworthiness of neighboring SU nodes into clusters, the proposed method helps SU nodes to select which witness's opinion to trust mode in the future. The proposed method has been studied using extensive computer simulation and has demonstrated good robustness against common collusive attacks.