An Energy Efficient Fuzzy Level Clustering for Stable Communications in Cognitive Sensor Networks

Abstract

The new challenges are posed in the Cognitive Radio Sensor Network (CRSN) when designing the methods for topology maintenance in the dynamic primary-user activities as it is an integration of wireless sensor networks and cognitive radio networks. The stability and connectivity improvements in the CRSNs by organizing the clustering of nodes into groups. Energy efficiency is a major issue in CRSNs due to the limited energy of sensor nodes and other related activities for data transmission. The aggregated data collected from the members of a network transmit towards the next-hop nodes over a routing path using Cluster Heads (CHs). However, the data is reached the sink node or destination node. The overall network collapses due to the failure of CHs. The energy consumption should be balanced among CHs by considering reliable CHs to prolong the network lifetime. An adaptive clustering algorithm is proposed using the parameter of weighted clustering. The parameters for CH selection are considered in the algorithm, such as node coverage, spectrum availability, queue length, and residual energy. The cluster stability improved using an optimal selection of reliable CHs for each cluster. The Cluster Head Weight (CHW) parameter for each node is determined and the CHs are chosen using the CHW parameter. An optimal relay selection method is incorporated in the proposed method based on fuzzy logic (FL) after CH selection and clustering to select the quality relay node for intra- and inter-cluster communications. The parameters like channel quality, link error rate, and traffic index are considered as input in the fuzzy system for relay selection. The simulation results prove that the proposed method achieves better performance in terms of end-to-end delay, routing overhead, throughput, network lifetime and residual energy than the earlier clustering algorithms of CRSN.