Distributed Node Deployment Algorithm Using Grid-Based Depth Adjustable for UASNs

Abstract

Designing an efficient node deployment algorithm in Underwater Acoustic Sensor Networks (UASNs) is crucial to address coverage holes and connectivity issues while meeting the Quality of Service (QoS) requirements of underwater applications. At present, the research enhances underwater communication through random deployment algorithms. However, current research has focused on designing node deployment algorithms for underwater sensors, underwater relays, or surface stations, which leads to poor network performance. Therefore, it is critical to consider all underwater nodes while designing an efficient node deployment algorithm to guarantee meeting the QoS requirements of underwater applications. To address these issues, this paper proposes the Distributed Deployment Optimization algorithm using Grid-based Depth Adjustable (DDOGDA) that relies on the minimum number of underwater nodes to maximize coverage, connectivity, and Energy Efficiency (EE) while reducing the Total Collisions (TC). In DDOGDA, the underwater nodes are placed within their Sensing Range (SR) to maintain network coverage and connectivity. The proposed algorithm takes into account multiple factors, including deployment strategies, Geographical Information System (GIS) data for the Solomon Islands, non-environmental factors, and the unique characteristics of underwater nodes. We find that the grid-based distributed node deployment algorithm can achieve higher performance than random and geometric deployment algorithms. Simulation results confirm that DDOGDA can maximize coverage and connectivity while minimizing TC. Moreover, we compare DDOGDA with random, tetrahedron, cuboid, triangular, and adaptive triangular deployment algorithms in terms of coverage, connectivity, TC, and EE and demonstrate that DDOGDA outperforms state-of-art methods.