Optimization of Node Deployment in Underwater Sensor Networks Using Maximum Coverage and Connectivity Self-Deployment Algorithm Based on Particle Swarm Optimization

In recent years, Underwater Wireless Sensor Networks (UWSNs) monitor underwater environment such as aquatic life. Moreover, to enhance network efficiently, sensor nodes are strategically placed in a 3D underwater space. However, underwater networks failed to find the optimal depth for each sensor in a 3D underwater network and failed in maintaining the connectivity to the surface station. Further, this research proposes a Maximum Coverage and Connectivity self-deployment algorithm based on Particle Swarm Optimization namely (MCC-PSO) for maximum coverage of water surface. Initially, the system models such as Network Scenario and Coverage Model are designed to optimize node deployment in UWSNs by simulating realistic environments. Then the problem identified that the optimal depth for N sensors deployed on a water surface to maximize network coverage and ensure connectivity to the surface station. To address this, a MCC and Connectivity Scheme is designed, Further, the MCC-PSO enhanced for optimizing the node development and PSO optimizes moving particles in search space by dynamic adaptation to environmental factors, such as node distribution and communication range. From the results, proposed MCC-PSO achieves a 50% higher maximum coverage rate compared to the existing Growth Ring Style Uneven Node Depth-Adjustment Self-Deployment Optimization Algorithm (GRSUNDSOA).