Collaborative research: energy-aware deployment and scheduling for sparse ocean sensor networks

The National Science Foundation Division of Electrical, Communications and Cyber Systems awarded Woods Hole Oceanographic Institution $263,236 on October 1, 2026, for a collaborative research project on energy-aware deployment and scheduling for sparse ocean sensor networks.

The project develops mathematical tools and control engineering methods to optimize how drifting ocean sensors (drifters) are deployed and operated across a connected network fleet. Rather than using rule-of-thumb placement and transmission schedules that waste battery power, the research will determine optimal sensor release locations and transmission timing to reveal ocean transport structures—including eddies, jets, and transport pathways—while reducing overall device count and energy consumption. The work models drifters as nodes in dynamic time-evolving similarity graphs, where connections reflect how similarly two drifters move, and applies graph Laplacian mathematics to encode key ocean transport structures.

The project addresses three central challenges: developing optimal deployment strategies to recover transport structures within fixed energy budgets while accounting for sensor losses and transmission gaps; designing energy-efficient transmission scheduling; and creating provable guarantees that scientific accuracy is preserved under tight energy constraints. Deliverables include new theory linking network science, control engineering, and ocean physics; open-source software; publicly available course materials; and applications to oil-spill response, search-and-rescue operations, and climate observation records.

Performance occurs in Woods Hole, Falmouth, Massachusetts, with a period of performance from October 1, 2026, through September 30, 2029. The funding falls under NSF Engineering (CFDA 47.041).

Abstract

This project aims to make ocean monitoring more efficient by rethinking how floating sensors are deployed and operated. Scientists rely on drifting ocean sensors (?drifters?), which periodically report their positions, to track currents that redistribute heat, pollution, and marine life. However, drifters are costly and run on limited batteries, and their deployment locations and reporting schedules are set by rule of thumb?wasting energy in some regions while missing important activity in others. The project will bring transformative change by treating a fleet of drifters as a connected network whose overall structure can be optimized. Mathematical tools will be developed for deciding where to release sensors and when each one should transmit, so that the fleet reveals ocean features using fewer devices and less power. The intellectual merit of the project includes new theory linking network science, control engineering, and ocean physics, with provable guarantees that scientific accuracy is preserved under tight energy budgets. Broader impacts include improved oil-spill and search-and-rescue response, longer and more efficient climate observation records, training of students across engineering and oceanography, new publicly available course materials, public outreach, and opensource software. The project models an oceanic sensor fleet as a dynamic time-evolving ?similarity graph,? In which each sensor, or drifter, is a node, and connections (edges) reflect how similarly two drifters move. The mathematics of such graphs, captured by the graph laplacian, encodes the ocean?s key transport structures (eddies or jets) and their resulting transport pathways and barriers. Three central challenges will be addressed. First, developing optimal deployment strategies to recover transport structures within a fixed budget, while accounting for sensor losses and transmission gaps due to unpredictable events. Second, developing energy-aware scheduling ?sleep? Of redundant sensors to save power. These tasks will use a structure-preserving model reduction technique called kron reduction, optimally maintaining the fleet?s information content when nodes are removed. The core technical challenge is guaranteeing that removed or resting sensors do not corrupt detected ocean features. The project will resolve this challenge by estimating error bounds that to maintain clustering accuracy, measured by the adjusted rand index (a standard score for comparing groupings). Third, the methods will be validated on idealized flows, realistic ocean-model simulations, and large historical datasets, and released as open-source software for operational use. This work will establish a rigorous, energy-aware standard for sparse ocean sensing and a bridge between control theory, network science, and physical oceanography. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the foundation's intellectual merit and broader impacts review criteria.- subawards are not planned for this award.