A novel clustering-based spectrum sensing in cognitive radio wireless sensor networks

In this paper, we consider a cognitive radio wireless sensor network (CR-WSN), where each sensor node is equipped with cognitive radio. A typical concern in CR-WSN is energy consumption due to resource-constrained nature of sensor nodes. Moreover, additional energy is consumed in a CR-WSN to support CR-exclusive functionality such as spectrum sensing and switching, which could shorten sensor node lifetime. However, some sensor nodes could receive similar signal due to similar channel condition such that they probably have same spectrum sensing results. Consequently, we propose a clustering based scheme for spectrum sensing in CR-WSN, which reduces energy consumption by involving less nodes in spectrum sensing. With our improved clustering algorithm, sensor nodes are grouped into different sets based on their similarity in sensing result. In order to identify the optimal cluster number, a new objective function, based on new intra-cluster and inter-cluster proximity measures has been proposed in our study. The simulation results show that the proposed scheme can effectively reduce the energy consumption of sensor node and improve global detection probability.