Energy-Efficient Sensor Selection for Cooperative Spectrum Sensing in the Lack or Partial Information

In this paper, we address an energy efficient sensor selection method in wireless sensor networks for cooperative spectrum sensing when only partial information about the sensors and primary user is available. Since it is very difficult to know signal-to-noise ratio for each node, we break down the node selection process as follows. First, there is not any information about the distance between the nodes and primary user. In this case, the probability density function of the received power from the primary user is obtained. Based on the probability density function of the received power, the average probability of detection is determined. According to the average probability of detection, the minimum number of nodes which should be distributed randomly to satisfy the average global probability of detection, is computed. Second, the location of the sensor nodes is assumed to be known while the distance between each node and primary user is not deterministic. Based on the convex optimization framework, we propose a numerical method to obtain the priority of the sensors for spectrum sensing. The sensing nodes are selected to satisfy the average global probability of detection and minimize the energy consumption. Simulation results show that our theoretical solution is in line with our simulation results.