Optimizing Underwater Network Performance for Earthquake Monitoring Application Using Stochastic Network Calculus

Abstract

Aquatic wireless sensor networks are paramount for adequate earthquake monitoring, and this study shows a visionary approach to energy harvesting within these systems. By integrating a stochastic network calculus (SNC) mathematical model with piezoelectric wave parameters, the study aims to enhance the performance of sensor nodes, which is crucial for sustaining long-term monitoring in challenging underwater environments. The combination of SNC equations with the unique properties of piezoelectric materials capable of converting mechanical stress from oceanic waves and seismic activities into electrical energy facilitates a robust methodology for managing and predicting energy availability in real time. Analytical and simulation results demonstrate significant improvements in key performance metrics, including packet delivery ratio, energy efficiency, network throughput, and network latency, compared to existing methodologies. This work lays the groundwork for more resilient underwater monitoring systems, contributing to improved preparedness and response strategies for earthquake-related events while also paving the way for the application of advanced mathematical models in energy harvesting technologies.