Leveraging Grey Wolf Optimizer for Reliable Data Transmission in Underwater Wireless Sensor Networks

This research investigates the application of the Grey Wolf Optimizer (GWO) to enhance reliable data transmission in Underwater Wireless Sensor Networks (UWSNs). UWSNs are critical for various applications, including environmental monitoring and underwater exploration, but face challenges such as high propagation delays, limited bandwidth, and energy constraints. The GWO algorithm, inspired by the social hierarchy and hunting behavior of grey wolves, is employed to optimize routing paths, minimize energy consumption, and improve packet delivery ratio. The methodology includes simulating UWSNs with different network configurations using synthetic and real-world datasets, and implementing the GWO algorithm in Python within the NS-3 simulation framework. Comparative analysis with traditional routing protocols, AODV and DSR, demonstrates that the GWO-based routing algorithm significantly reduces energy consumption, increases packet delivery ratio, lowers latency, and extends network lifetime. These results underscore the effectiveness of GWO in enhancing UWSN performance. The study concludes that GWO offers a robust and adaptable solution for reliable data transmission in UWSNs, with potential for further enhancements through hybrid optimization techniques and real-world validations. This research provides a comprehensive understanding of GWO's application in UWSNs, contributing to advancements in underwater communication technologies.