Thangavelu Shankar

Underwater Wireless Sensor Networks (UWSNs), a subset of traditional WSNs, face critical challenges due to their reliance on non-rechargeable, irreplaceable power sources, making energy-efficient communication essential. This paper proposes a novel meta-heuristic clustering-based routing protocol inspired by the hunger-driven hunting and territorial behaviour of lions, termed the Hunger Driven Lion Clustering Algorithm (HDLCA). Unlike other approaches, HDLCA directly maps lion behaviour to sensor node dynamics, enabling adaptive cluster head selection and efficient sub-cluster formation based on energy levels and node proximity. The algorithm is evaluated using key performance metrics including residual energy, dead node count per round, first and last node death, and throughput. Simulation results show that HDLCA optimizes these metrics effectively compared to EERBLC, EECMR, LEACH, and K-Means Clustering. Specifically, HDLCA achieves improvements in network longevity by 23.3%, 14.37%, 34.04%, and 59.91% when compared to EECMR, EERBLC, K-Means Clustering, and LEACH respectively. Additionally, HDLCA exhibits strong scalability, noise resilience, and consistent throughput, making it a robust and efficient solution for underwater deployments.

Underwater wireless sensor networks (UWSNs) are commonly used for data transmission underwater, detecting volcanic movements, and detecting water pollution. A UWSN is made up of acoustic sensors powered by batteries. Replacement of these batteries is complicated because of the complex underwater conditions of UWSNs. One strategy for fixing this issue is to extend the lifetime of UWSNs by decreasing the energy they spend and enhancing their energy usage. This research proposes a set of rules based on Energy-balanced unequal layering clustering (EULC) and PSO (Particle swarm optimization) that enhances the performance of the lifetime of UWSNs. The proposed hybrid PSO-EULC combination produces UWSNs with different layering based entirely on node depth, with Cluster head (CH) nodes chosen using the PSO cost function. The hybrid PSO-EULC combination successfully balances the energy used in UWSN nodes, thus extending sensor node lifetime, packet transmission cost, and network lifetime. The results are compared with Energy Efficient Unequal Multilevel Clustering (EEUMC), Energy-balanced reliable and effective clustering (EBREC), and LEACH with parameters such as residual energy, dead nodes, and total packets received at different initial power levels of 1J,0.5J for 6400 bits, and 4000 bits of data transmission. As per case 4 the residual energy of 44.4% improved, dead nodes 15% less, and 50.25% of received packets is achieved by comparing it with the EEUMC algorithm.

The underwater wireless sensor networks (UWSNs) area is a developing area of research since there are tremendous opportunities like surveying marine life, installing and monitoring optical cables, detecting earthquakes, and surveillance of territorial borders. Though many applications exist, underwater research explored to date is less than five percent as it poses many issues and challenges like water currents, temperature, pressure, water salinity, disturbance by aquatic animals, and many more factors that affect the performance of sensors deployed inside water. A significant issue UWSNs face is focusing on energy efficiency to extend the life of submerged sensors placed in isolated areas. Resolving localization concerns is a primary additional concern. In this comprehensive survey, the basics of UWSNs are covered in the introduction, followed by a thorough literature review of the existing works mainly focusing on localization, energy efficiency, Bio-inspired algorithms (BIA), and the impact of implementing Machine Learning (ML) are discussed. In concurrent sections, we have discussed attributes, parameters useful for analysis, issues and challenges in UWSN, soft computing techniques, software and hardware tools available for extended research, and opportunities in UWSN. The researchers could gain perspective pathways at the end of this survey.