Hybrid Mexican axolotl and bitterling fish optimization algorithm-based spectrum sensing multi-hop clustering routing protocol for cognitive sensor networks

In clustered cognitive radio sensor networks (CRSNs), availability of free channels, spectrum sensing and energy utilization during clustering and cluster head (CH) selection is essential for fairness of time and event-driven data traffic. The existing multi-hop routing protocols in CRSNs generally adopt a perfect spectrum sensing which is not same in the practical spectrum sensing of nodes in real networks. High imbalance in residual energy between the selected CHs negatively impacts the delivery of data packets. Hence, hybrid mexican axolotl and bitterling fish optimization algorithm-based spectrum sensing multi-hop clustering routing protocol (HMABFOA) is proposed as an imperfect spectrum sensing approach for achieving better utilization of downlink energy harvesting and sustain maximized degree of energy between the nodes in the network. This HMABFOA scheme reduces the negative impact of imperfect spectrum sensing for extended network lifetime which sustains the capabilities of the network surveillance. It helped in constructing a distributed cluster with multi-hop routing selection between clusters depending on a energy level function that explores and exploits the factors associated with CHs selection. The merits of Mexican axolotl optimization algorithm (MAOA) is used for better CH selection and cluster formation with energy stability is sustained in the network. Further bitterling fish optimization (BFOA) algorithm is used for optimized multi-hop route between the clusters with minimal energy consumption and maximized spectrum sensing that proves better channels availability. The simulation results guaranteed maximized network lifetime of 24.38%, spectrum utilization rate of 24.58%, and minimized energy utilization of 25.62%, better than the baseline approaches.