  The primary user emulation attack (PUEA) is one of the common threats in
cognitive radio networks (CRNs), in this problem, an attacker mimics the
Primary User (PU) signal to deceive other secondary users (SUs) to make them
leave the white spaces (free spaces) in the spectrum assigned by the PU. In
this paper, the PUEA is detected and localized using the
Time-Difference-Of-Arrival (TDOA) localization technique. Particle Swarm
Optimization (PSO) algorithms are proposed to solve the cost function of TDOA
measurements. The PSO variants are developed by changing the parameters of the
standard PSO such as inertia weight and acceleration constants. These
approaches are presented and compared with the standard PSO in terms of
convergence speed and processing time. This paper presents the first study of
designing a PSO algorithm suitable for the localization problem and will be
considered as a good guidance for applying the optimization algorithms in
wireless positioning techniques. Mean square error (MSE) and cumulative
distribution function (CDF) are used as the evaluation metrics to measure the
accuracy of the proposed algorithms. Simulation results show that the proposed
PSO approaches provide higher accuracy and faster convergence than the standard
PSO and the Taylor series estimation (TSE).
