Robust Spectrum Sensing with Crowd Sensors

This paper investigates the issue of cooperative spectrum sensing with a crowd of low-end personal spectrum sensors (such as smartphones, tablets, and in-vehicle sensors), where one critical challenge is the uncertainty of the quality of sensing data from crowd sensors that may be unreliable, untrustworthy, or even malicious. Moreover, due to either unexpected equipment failures or malicious behaviors, every crowd sensor could sporadically and randomly contribute abnormal data, which makes the existing defense schemes ineffective. To tackle these unique challenges, we propose a robust spectrum sensing scheme by developing a data cleansing framework, where the underutilization of licensed spectrum bands and the sparsity of nonzero abnormal data are jointly exploited to robustly cleanse out the potential nonzero abnormal data component from the original corrupted sensing data. Simulation results demonstrate that the proposed robust sensing scheme outperforms the state-of-art schemes under various abnormal data parameter configurations.