An Efficient Collaborative Spectrum Sensing Method in Cognitive Radio Networks: Software-Defined Data Fusion Approach
Cognitive Radio (CR) technology has been suggested as a solution to the serious problem of spectrum scarcity in recent years. Cooperative Spectrum Sensing (CSS) is the key function to overcome the destructive effect of hidden station, multipath fading and shadowing problems. As many previous studies have shown, the trustworthiness of the CSS can be strictly degraded under Spectrum Sensing Data Falsification (SSDF) attack. In this paper, we introduce an important dynamic fusion rule called Software-Defined CSS (SD-CSS). The main contribution is to analyze the SSDF attack strategy against the CR network and apply the best fusion rule to increase the cooperative sensing performance. Two important SSDF attack parameters, attack strategy and attack ratio, are estimated and the obtained parameters are then used to choose an appropriate fusion rule to improve the CSS performance. The obtained results confirm considerable improvement in correct sensing ratio in massive attack.
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