Robust cooperative spectrum sensing in cognitive radio blockchain network using SHA-3 algorithm - Evelyn Ezhilarasi I; J. Christopher Clement (Blockchain: Research and Applications)
Cognitive radio network (CRN) uses the available spectrum resources wisely. Spectrum sensing is the central element of a CRN. However, spectrum sensing is susceptible to multiple security breaches caused by malicious users (MUs). These attackers attempt to change the sensed result in order to decrease network performance. In our proposed approach, with the help of blockchain-based technology, the fusion center is able to detect and prevent such criminal activities. The method of our model makes use of blockchain-based MU detection with SHA-3 hashing and energy detection-based spectrum sensing. The detection strategy takes place in two stages: block updation phase and iron out phase. The simulation results of the proposed method demonstrate 3.125%, 6.5%, and 8.8% more detection probability at −5 dB signal-to-noise ratio (SNR) in the presence of MUs, when compared to other methods like equal gain combining (EGC), blockchain-based cooperative spectrum sensing (BCSS), and fault-tolerant cooperative spectrum sensing (FTCSS), respectively. Thus, the security of cognitive radio blockchain network is proved to be significantly improved.