Energy-efficient spectrum sensing for cognitive sensor networks

We consider a combined sleeping and censoring scheme for energy-efficient spectrum sensing in cognitive sensor networks. We analyze the detection performance of this scheme by theoretically deriving the global probabilities of detection and false-alarm. Our goal is to minimize the energy consumption incurred in distributed sensing, given constraints on the global probabilities of detection and false-alarm, by optimally designing the sleeping rate and the censoring thresholds. Using specific transceiver models for sensors based on IEEE 802.15.4/ZigBee, we show the energy savings achieved under an optimum choice of the design parameters.