Energy and spectrum aware unequal clustering with deep learning based primary user classification in cognitive radio sensor networks

Abstract

The problem of energy efficiency in cognitive radio sensor networks (CRSN) is mainly caused by the limited energy of sensor nodes and other channel-related operations for data transmission. The unequal clustering method should be considered for balancing the energy consumption among the cluster heads (CHs) for prolonging the network lifetime. The CH selection should consider the number of accessible free channels for efficient channel assignment. To improve fairness, the channel assignment problem should consider energy consumption among the cluster members. Furthermore, the relay metric for the selection of the best next-hop should consider the stability of the link for improving the transmission time. The CH rotation for cluster maintenance should be energy and spectrum aware. With regard to the above objectives, this paper proposes an energy and spectrum aware unequal clustering (ESAUC) protocol that jointly overcomes the limitations of energy and spectrum for maximizing the lifetime of CRSN. Our proposed ESAUC protocol improves fairness by achieving residual energy balance among the sensor nodes and enhances the network lifetime by reducing the overall energy consumption. Deep Belief Networks algorithm is exploited to predict the spectrum holes. ESAUC improves the stability of the cluster by optimally adjusting the number of common channels. ESAUC uses a CogAODV based routing mechanism to perform inter-cluster forwarding. Simulation results show that the proposed scheme outperforms the existing CRSN clustering algorithms in terms of residual energy, Network Lifetime, secondary user–primary user Interference Ratio, Route Discovery Frequency, throughput, Packet Delivery Ratio, and end-to-end delay.