Spiral Elite Learning with Gazelle Optimization Algorithm Based Secure Clustering and Routing in Wireless Sensor Network

Abstract

Wireless Sensor Networks (WSNs) are a dispersed group of sensor nodes that are deployed in an open and hostile environment for data transmission. Sensor data is transmitted by each node in the network using multi-hop communication until it reaches the Base Station (BS). However, the resource-constrained nature of devices and their operation in potentially hostile and dynamic environments makes it difficult to ensure and maintain secure communication in WSN which is one of the major challenges. To address this issue, a Spiral Elite Learning based Gazelle Optimization Algorithm (SEL-GOA) is proposed for secure Cluster Head (CH) selection and routing in the WSNs without compromising the nodes. In secure CH selection and routing, to avoid node compromising and malicious attacks, the GOA model optimizes the selection process by security parameters (Fitness Functions) and ensures that selected CH and routing paths are in the secure area of the network. The experimental results of the proposed SEL-GOA show that the algorithm has achieved energy consumption of 0.31 J, delay of 16 ms and Throughput of 94 mbps for 500 nodes. These results are better when compared to existing clustering and routing methods such as Neuro Fuzzy Clustering with Sparrow Search Optimization Algorithm (NF-SSOA).