A Novel Hybrid Clustering Method for Secured Cross-Layer and Cross-Domain Routing in Dense Wsns

Discovering a method that would allow the networks to last for a longer period of time is one of the utmost grave problems in Wireless Sensor Networks (WSN). It is necessary to model method that is efficient in order to conserve limited power assets that are available for the Wireless Sensors Network. One solution to these issues is the use of cross-layer rules, which are designed to improve the efficiency of routing while additionally extending the lifetime of the network. An innovative model that allows the routing of data across layers and domains is revealed, and subsequently the model is optimized for use in safe clustered inside vast wireless networks of sensor. A unique hybrid technique that combines features of Moth Flame Optimization (MFO) and Dragonfly Algorithm (DA) is presented in this study. The purpose of this strategy is to improve the effectiveness of a density wireless sensor. A comparison of the effectiveness of the suggested model with five other standard techniques is presented in the last section. We examine the impacts on the network over a lengthy period of time, as well as the analysis of living nodes, divided into three distinct groups: normal nodes, evolved nodes, and superior nodes. In terms of both the assessment of whether or not a node is alive and the longevity of the network, we discover that the technique that was provided gives satisfactory results. When compared to Adaptive Lion Optimization Protocol (ALOP), Genuine Grey Wolf Search Optimization (GGWSO), and Adaptive Whale Optimization Algorithm (AW-WOA), the Moth Flame Integrated Dragonfly Algorithm (MFI-DA) technique proposes achieves the highest level of accuracy, which is 96%.