A soft cooperative spectrum sensing in the presence of most destructive smart PUEA using energy detector

Abstract

Summary Recently, the growth of Internet of Things (IoT) and its remarkable impacts on human well‐being life style are deniable. On the connectivity side, IoT is highly related to Wireless Sensor Network (WSN) concept. The key elements include the data, which is machine‐produced, specifically by sensors, and the data communication through connectivity technologies. On the security side, primary user emulation attack (PUE) is one of the well‐defined attacks in cognitive radio (CR)–based WSN. Here, we investigate a smart primary user emulation attacker that has the most destructive effect on the spectrum sensing unit of cognitive radio users. To deal with this attack, a soft cooperative spectrum sensing using an energy detector is proposed. In the proposed method, the values of sensing information of each secondary user are sent to a fusion center. Once the values are received, they will be combined with some appropriate coefficients in order to minimize spectrum sensing probability of error for a given probability of false alarm. The coefficients are the variables of a constrained optimization problem. Based on simulation results, our method has a lower error probability in spectrum sensing in comparison to hard combination schemes (eg, OR rule) and soft combination schemes (eg, CSINR method).

A throughput analysis of an energy-efficient spectrum sensing scheme for the cognitive radio-based Internet of things

“…With cooperative spectrum sensing, each CR-IoT user conducts local sensing independently and then sends the sensing results to the corresponding fusion center via a (noisefree or non-noise-free) control channel. The fusion center combines all sensing findings and makes a final global decision based on fusion rules [19][20][21][22]. The fusion rule can be categorized either as hard decision fusion rule or as soft decision fusion rule.…”

An integrated performance evaluation of ED‐based spectrum sensing over α−κ−μ and α−κ−μ‐Extreme fading channels

“…Nonetheless, the detection of availability of unused spectrum becomes a challenging task when the transmission channel is affected by moderate to severe levels of fading. The performance of ED and other spectrum sensing techniques over various types of multipath and composite fading channels and communication scenarios has been well documented in the literature . In particular, Dikmese et al explore the computational complexity of eigenvalue‐based spectrum sensing through power, inverse power, and efficient Cholesky methods.…”

“…The performance of ED and other spectrum sensing techniques over various types of multipath and composite fading channels and communication scenarios has been well documented in the literature. [3][4][5][6][7][8][9][10][11][12][13][14] In particular, Dikmese et al 5 explore the computational complexity of eigenvalue-based spectrum sensing through power, inverse power, and efficient Cholesky methods. In other work of Dikmese et al, 7 fast Fourier transform/filter bank-based method for spectrum analysis is considered.…”

One‐to‐Many Relationship Based Kullback Leibler Divergence against Malicious Users in Cooperative Spectrum Sensing

“…An extended sequential cooperative scheme that reduces the number of sensing reports is investigated in [29]. In the soft fusion combination schemes proposed in [30][31][32] sensing energies from different SUs are combined to take accurate decision about the PU spectrum holes. Similarly, in the hard fusion schemes SUs provide a hard binary decision to the FC to predict the licensed user activity in the spectrum [33][34][35].…”