Sensor Selection Based on Dempster-Shafer Evidence Theory under Collaborative Spectrum Sensing in Cognitive Radio Sensor Networks

Collaborative Spectrum Sensing (CSS) is considered as a powerful way to improve the utilization of the spectrum resources. However, the open characteristic of Cognitive Radio Sensor Networks (CRSNs) suffer various security threats, such as Spectrum Sensing Data Falsification (SSDF) attack, the defending of this kind of attack is a popular research topic in both academia and industry due to the demanding requirements of security. To enhance the performance of sensing, the schemes based on the Dempster-Shafer (D-S) Evidence Theory have been proposed. However, if we consider that the transmitted data will be increased with the sensing nodes increasing, the energy consumption of the whole networks should be reduced. In order to reduce the total energy consumption, Sensor Selection is one efficiency way to deal with the energy limitation. In this paper, we calculate the formulas of false alarm probability and miss detection probability under the D-S Evidence Theory and propose a new algorithm to find the minimum number of sensing nodes and the optimal threshold with the given security level based on D-S Evidence Theory to against SSDF attack.