k-hop neighbour knowledge-based clustering in CRN under opportunistic channel access

Cognitive radio networks (CRNs) enables cognitive users (CUs) to use available spectrum of primary users (PUs) using innovative techniques. On appearance of PUs, the available channel of CUs at different position may have different available channels which changes dynamically over time. Due to temporal and spatial variations of opportunistic channel availability, to ensure connectivity and robustness in CRN is of great research interests. We have proposed novel approach of k-hop neighbour knowledge-based clustering algorithm which guarantees robustness in CRN and converges in O(n2m), for n CUs and m clusters in the network. We have evaluated for varying 3-hop, 4-hop, … k-hop through simulation which shows that our scheme achieves 25%-30% more numbers of outward common channels and outperforms in terms of inner common channel index, outward common channel index, number of isolated nodes, throughput and frequency of route discovery compared to the competitive approaches.