Lifetime Maximization by Dynamic Threshold and Sensor Selection in Multichannel Cognitive Sensor Network

The tiny and lowcost sensors cannot simultaneously sense more than one channel since they do not have highspeed AnalogtoDigitalConvertors (ADCs) and highpower batteries. It is a critical problem when they are used for multichannel sensing in cognitive sensor networks (CSNs). One solution for this problem is that the sensors sense various channels at different sensing periods. Due to the energy limitation in these scenarios, the lifetime maximization will become an important issue. In this paper, maximizing the lifetime of a CSN is investigated by selecting both the cooperative sensors and their detector threshold, such that the desired detection performance constraints are satisfied. This is a NPcomplete problem, and obtaining the optimum solution needs exhaustive search with exponential complexity order. Here we have proposed two convexbased optimization algorithms with low order of complexity. First algorithm applies the known instantaneous SignaltoNoiseRatio (SNR) and obtains the proper detector thresholds by solving an equation for every channel. Investigation the effect of detector thresholds on the energy consumption, the false alarm probability and the detection probability shows that we can minimize the detector thresholds such that the detection constraints are met. In the second algorithm in order to reduce the complexity of the problem it is proposed the Bisection method for determining detector thresholds. Because knowing the instantaneous SNR is difficult, we have investigated the performance of the second algorithm by average value of SNR. Simulation results show that the proposed algorithms improve the performance of the network in case of lifetime and energy consumption.