Energy-Efficient Primary User Tracking Using Genetic Algorithm in Cognitive Sensor Networks

This paper addresses the sensor selection problem for cooperative spectrum sensing and primary user tracking in cognitive radio sensor networks. An energy-efficient cooperative spectrum sensing is proposed which is based on the sensor selection with the constraints on the detection performance and the accuracy of the primary user localization. The problem is solved using the genetic algorithm (GA). Genetic algorithm is one of the nonlinear optimization methods and relatively better option thanks to its efficiency for large scale applications. GA defines the sensor in the form of chromosomes and genes and the user’s quality of service needs are given as input to the GA procedure. Simulation results indicate that the energy efficiency is improved while the location of the primary user is determined with high accuracy.