Utilizing misleading information for cooperative spectrum sensing in cognitive radio networks

In cognitive radio networks, the radios continuously scan the radio spectrum and create a spectrum usage report. Due to channel uncertainty, there are inaccuracies in these reports. Oftentimes, the radios share and fuse the observed data in order to increase the accuracy of the spectrum usage. However, malicious nodes tend to send false information (i.e., attack) in order to mislead the construction of the spectrum usage report. In this paper, we use a trust model to evaluate the trustworthiness of every node and use the trust values to effectively fuse the information from all nodes. A node compares the information sent by a neighboring node with the predicted information. Based on the ratio of matches (or mismatches), the neighboring node is assigned a trust value. Then, we propose a log-weighted metric utilizing trust values to distinguish malicious nodes from others. Subsequently, we propose threshold based Selective Inversion (SI) fusion and Complete Inversion (CI) fusion to effectively combine not only the information sent by honest nodes but also utilize misleading information sent by malicious nodes. We also propose a combination of the two inversion schemes. We compare the performance of the inversion based fusion schemes with blind and trust-based fusions. Results reveal better performance for inversion based fusion schemes for various intensities of attack. We also conduct simulations to evaluate the optimal thresholds that are used for invoking the inversion based fusion schemes.