PROJECT TITLE :
Energy-Efficient Sensor Selection for Cooperative Spectrum Sensing in the Lack or Partial Information
In this paper, we have a tendency to address an energy economical sensor choice method in wireless sensor networks for cooperative spectrum sensing when solely partial information regarding the sensors and first user is on the market. Since it is very tough to know signal-to-noise ratio for every node, we break down the node selection method as follows. Initial, there is not any information about the gap between the nodes and primary user. During this case, the likelihood density function of the received power from the first user is obtained. Based on the likelihood density operate of the received power, the common probability of detection is determined. According to the common probability of detection, the minimum number of nodes which should be distributed randomly to satisfy the common global likelihood of detection, is computed. Second, the location of the sensor nodes is assumed to be known whereas the distance between every node and primary user is not deterministic. Based on the convex optimization framework, we have a tendency to propose a numerical technique to get the priority of the sensors for spectrum sensing. The sensing nodes are selected to satisfy the average international chance of detection and minimize the energy consumption. Simulation results show that our theoretical solution is in keeping with our simulation results.
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