Cooperative Spectrum Sensing Research Of Cognitive Radio Sensor Networks

In cognitive radio sensor networks, as most sensor nodes are powered by battery, energy and spectrum resources are both scarce resources, which thus need to be utilized efficiently. In cooperative spectrum sensing, more nodes involved leads to more extra communication and energy consumption. Therefore, sensing node selection and power control is of important significance for cognitive radio sensor network deployment and application.In cooperative spectrum sensing, the reporting channel may suffer severe interference, due to its unreliability as conventional wireless channels, which directly pose an impact on the accuracy of spectrum sensing. In this paper, we consider the unreliability of the sensing result transmission, and to make spectrum sensing more energy efficient, we select a proper portion of nodes to participate in spectrum sensing while leaving the other nodes asleep to save energy. Moreover, to further reduce energy consumption we will control the transmit power for the sensing nodes. We modeled the problem as a mixed discrete and continuous variable optimization one. A modified particle swarm algorithm (CBPSO) based on BPSO is proposed, which can adjust the sensing node transmit power while selecting nodes for spectrum sensing. Simulation results show that the total energy consumption was reduced significantly compared with BPSO and other existing sensing nodes selection algorithms.Spectrum sensing based on spectrum database can reduce the computational burden and energy consumption of sensor nodes effectively, which is coincident with cognitive radio spectrum sensing technology development trends, and the method is also suitable for wireless sensor networks. However, the premise of the research and application of this method is to build a spectrum database, which means we must collect enough information to spectrum firstly. This paper is to design and implement a Wi-Fi spectrum data acquisition system. According to the spectral acquisition scheme before, and combined with smart mobile phone flexible function, system spectrum acquisition should be carried out from the three dimensions of space, time and frequency. For space dimension, to get the geographical position of acquisition point, mobile localization technology will be used. For time dimension, this paper sets up a multi terminal time synchronization mechanism. For frequency dimension, when mobile phone Wi-Fi switch to promiscuous mode, it could capture all the network message it can monitor, then the signal intensity of each channel would be calculated. The system applies a variety of popular techniques, and implements the spectrum measurement data acquisition on three dimensions, which provided sufficient datasets for spectrum analysis and post-processing.