A Probability Maximizing Approach for Spectrum Sensing in Cognitive Wireless Sensor Networks

In order to meet the increasing demand for frequency resources for sensors, spectrum sensing has been proposed to improve the utilization of spectrum resources in cognitive wireless sensor networks (CWSNs). Therefore, spectrum sensing-enabled sensors can detect spectrum resources that are not used by the primary users (PUs) in CWSNs. To accurately detect the state of the PU signal and avoid harmful interference in a very short period, we propose a spectrum sensing problem in a CWSN model and evaluate the local spectrum sensing performance of the sensor. Furthermore, the tradeoff problem between detection delay and false alarm probability is also proposed and analyzed. In addition, we propose a probability maximizing (PM) approach to estimate the change point according to previous sensing information and maximize the stopping sensing probability to find the optimal sensing time. Finally, simulation results show that compared to the traditional convex optimization approach, the proposed PM exhibits outstanding performance in terms of the global false alarm probability, while achieving significant throughput in a shorter sensing time.