Energy-Efficient Distributed Spectrum Sensing for Cognitive Sensor Networks

Reliability and energy consumption in detection are key objectives for distributed spectrum sensing in cognitive sensor networks. In conventional distributed sensing approaches, although the detection performance improves with the number of radios, so does the network energy consumption. We consider a combined sleeping and censoring scheme as an energy efficient spectrum sensing technique for cognitive sensor networks. Our objective is to minimize the energy consumed in distributed sensing subject to constraints on the detection performance, by optimally choosing the sleeping and censoring design parameters. The constraint on the detection performance is given by a minimum target probability of detection and a maximum permissible probability of false alarm. Depending on the availability of prior knowledge about the probability of primary user presence, two cases are considered. The case where a priori knowledge is not available defines the blind setup; otherwise the setup is called knowledge-aided. By considering a sensor network based on IEEE 802.15.4/ZigBee radios, we show that significant energy savings can be achieved by the proposed scheme.