Investigating The Influence of Aquatic Algorithms on Underwater Wireless Routing Beyond Surface Constraints
DOI:
https://doi.org/10.37385/jaets.v7i1.7298Keywords:
UWSN, Routing Protocols, QoS Performance, Aqua-Sim, NS-3Abstract
Underwater Wireless Sensor Networks (UWSNs) play a vital role in ocean monitoring, seismic activity tracking, and environmental observation, yet their performance is hindered by high propagation delay, limited bandwidth, and dynamic topological variations. This study conducts a comprehensive comparative analysis of aquatic algorithm on three prominent UWSN routing protocols—Vector-Based Forwarding (VBF), Depth-Based Routing (DBR), and Flood Routing—to evaluate their Quality of Service (QoS) performance in terms of delay, throughput, and packet loss under varying conditions of node density (20–100 nodes), communication distance (50–200 m), and deployment depth (0–200 m). Simulation data were generated using the Aqua-Sim framework in NS-3, ensuring a controlled and reproducible evaluation environment. The results indicate that VBF consistently achieves the lowest delay (0.0676 s at 20 nodes; 0.0769 s at 100 nodes) and the highest throughput (1772.80 bps at a depth of 51–100 m), making it the most efficient protocol for real-time and latency-sensitive applications. However, VBF experiences moderate packet loss (up to 57.34% in dense networks) due to its constrained forwarding region. DBR exhibits higher delay (0.1420 s at 151–200 m) and the greatest packet loss (85% at a 50 m distance), reflecting the limitations of depth-only forwarding in dynamic environments. In contrast, Flood Routing achieves the lowest packet loss (10.24% at 50 m) but suffers from increased delay (0.1063 s at 200 m) and inefficiency due to redundant transmissions. Overall, this study provides a unified performance benchmark for UWSN routing protocols, highlighting the fundamental trade-offs between efficiency, reliability, and scalability. These insights offer practical guidance for protocol selection and serve as a foundation for future adaptive and AI-driven routing strategies in underwater sensor networks.
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