A walrus optimization algorithm (WOA) for cluster head selection to optimize energy-efficiency in UWSNs-based IoT

The Internet of Things (IoT), which is one of the emerging technologies, has the potential to revolutionize smart ocean monitoring. Underwater wireless sensor networks (UWSNs) equipped with sensors can facilitate the flow of intelligent data to various applications, including environmental monitoring, navigation, pollution surveillance, coastline protection, and military operations, among others. Energy becomes a critical resource in UWSNs because sensor batteries cannot be replaced. Due to battery limitations, sensors are typically resource-constrained. Energy conservation is critical in IoT to extend network lifetime. This is achieved by employing clustering techniques in UWSNs. In recent years, many studies have devised clustering protocols to conserve energy in networks. However, selecting a Cluster Head (CH) node takes considerable time. To address this, this research presents an effective method, a Walrus Optimization Algorithm (WOA)-based routing protocol, which enhances network lifetime and reduces energy consumption. The performance of the proposed algorithm (WOA) is evaluated in MATLAB 2024a and compared with ZFO-SHO, TIOCHR, and M-PSO, wellknown nature-inspired algorithms. The proposed WOA has demonstrated observed improvement in the packet delivery ratio by approximately 7%–10% and network lifetime by 10%–15%, respectively. The results confirm that the suggested WOA improved energy efficiency within IoT-based UWSNs.