A Stochastic Geo-spatiotemporal Bipartite Network to Optimize GCOOS Sensor Placement Strategies

TL;DR

This paper introduces a new stochastic geo-spatiotemporal bipartite network model with coverage measures to optimize sensor placement in the Gulf of Mexico, enhancing ocean forecasting accuracy.

Contribution

It develops novel coverage metrics and applies them within a GSTBN framework to determine optimal sensor placements for improved ocean model predictions.

Findings

Coverage and robustness scores effectively evaluate sensor placement strategies.

Monte Carlo simulations assist in identifying optimal observer node locations.

Application to Gulf of Mexico improves GCOOS sensor deployment for better forecasts.

Abstract

This paper proposes two new measures applicable in a spatial bipartite network model: coverage and coverage robustness. The bipartite network must consist of observer nodes, observable nodes, and edges that connect observer nodes to observable nodes. The coverage and coverage robustness scores evaluate the effectiveness of the observer node placements. This measure is beneficial for stochastic data as it may be coupled with Monte Carlo simulations to identify optimal placements for new observer nodes. In this paper, we construct a Geo-SpatioTemporal Bipartite Network (GSTBN) within the stochastic and dynamical environment of the Gulf of Mexico. This GSTBN consists of GCOOS sensor nodes and HYCOM Region of Interest (RoI) event nodes. The goal is to identify optimal placements to expand GCOOS to improve the forecasting outcomes by the HYCOM ocean prediction model.