A highly secure and stable energy aware multi-objective constraints-based hybrid optimization algorithms for effective optimal cluster head selection and routing in wireless sensor networks

The Wireless Sensor Network (WSN) has become a famous technology because of its huge range of military and public usage. However, the WSNs are highly limited concerning energy. Routing is the primary challenging WSN component. Unbalanced energy usage during the data packet routing tasks is a complex task in WSNs. This problem should be focused on because the sensor node's energy levels are highly limited. For this purpose, an optimal Cluster Head (CH) selection and routing approach is developed in the WSN that minimizes energy utilization and improves the lifespan of the WSN. Initially, the clustering method is applied to group the sensor nodes in the WSN. Clustering is a network organization mechanism that supports energy efficiency, data collection, and workload distribution. For optimally selecting the CH in WSN, the novel Hybridized Zebra and Bitterling Fish Optimization Algorithm (HZ-BFOA) is introduced. The developed HZ-BFOA is a hybrid algorithm with a novel concept, which includes the merits of two effective algorithms named Zebra Optimization Algorithm (ZOA) and Bitterling Fish Optimization (BFO). This optimized CH selection process optimally chooses the CH thus enhancing the energy efficiency and life time of WSN. After the optimal CH selection process, the optimal routing is performed for performing the effective communication process. The implemented HZ-BFOA optimally chooses the shortest path thus minimizing the latency and also preventing the data packet loss. In this, a new multi-objective function is formulated in terms of constraints like stability period, scalability, security, inter and intra-cluster distance, network energy utilization, delay, throughput, and Packet Delivery Ratio (PDR) respectively. Finally, the developed technique is validated through various techniques. The developed HZ-BFOA-based optimal CH selection and routing shows an 89% throughput rate, which is greater than the previous models thus proving its effectiveness.