Optimized Cross-Layer Cross-Domain Routing for Cluster-Based Dense Wireless Sensor Networks

One of the most pressing issues in Wireless Sensor Networks (WSN) is finding a way to extend the life of the networks. In order to save the limited energy assets of Wireless Sensors Network, it is essential to model efficient approaches. The use of cross-layer procedures, which aim to optimize routing while also increasing the lifespan of the network, is a solution to these problems. In this paper, we provide a novel clustering-based Cross-Layer Cross-Domain Forwarding model. An innovative cross-layer cross-domain route methodology is used to discuss and improve a cluster-based heavy wireless network of sensors. To improve the functionality of complex wireless sensors, we presented a new hybrid method that combines elements of the Gray Wolf and Whale Optimize Algorithms. In the outcomes section, we compare the suggested model's performance to that of six additional typical models; we also look at the life span of the entire network as a whole and analyse the alive nodes for typical, developed, and absolutely advanced nodes in three separate test cases that vary in cluster size. Results for network lifetime and alive nodes assessment are better with the suggested technique compared to the current method. The suggested Adaptive Weighted Whale Optimization Algorithm (AW-WOA) consistently exceeds the other approaches, with an accuracy of 95% Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), and Bat Search Optimization (BSO). This is the case when compared to the alternative methods. On the other hand, AW-WOA retains outstanding outcomes, displaying improved stability and efficacy in optimisation throughout all phases, while all other approaches see a drop in accuracy with time.