A Novel Trust-Enabled Data-Gathering Technique based on Modified Golden Eagle Optimization in Wireless Sensor Network
V Shobana, Jasmine Samraj · 2024
Wireless Sensor Networks (WSNs) are successfully used to produce a wide range of collaboration and intelligent applications that deliver a more pleasant and cost-effective way of living. Clustering is one of the most successful strategies for improving the lifespan of WSNs. Cluster Heads (CHs) serve an important role in cluster based WSNs; nevertheless, Once the CHs get compromised, the information gathered lost trustworthiness. The outcome is confidence method of clustering are crucial in a Wireless Sensor Networks to improve communication between nodes while simultaneously boosting the safety of the network. This study explains a Modified Golden Eagle Optimization with Stooping Technique (MGEO) developed using Trust Enabled Data Gathering Technique (TEDGTMGEO) for WSN-based applications. TEDGTMGEO suggests a nature-inspired strategy for selecting secure cluster heads (CH) in MGEO. Reflecting on the importance of node reliability and vitality. While selecting CH, the fitness algorithm considers the node's remaining energy and trust value. Quality of Service factors such as energy consumption and data transfer rate are considered to extend network lifespan and guarantee network dependability. The total energy consumption evaluates the algorithm's efficiency for each iteration. According on the simulation findings, the suggested technique uses less energy, has longer average network lifespan, and chooses more secure nodes than other studies in the literature. The parameters used to compare performance include living nodes, dead nodes, residual energy, throughput, and CHs Average Trust Value.