Towards scalable modern marine ecosystem monitoring
type: CreativeWork
description: The ocean covers 71% of Earth's surface yet remains profoundly undersampled. Climate change is driving warming, acidification and deoxygenation at unprecedented rates, while ecosystems, often represented by iconic umbrella species such as North Atlantic right whales, emperor penguins, and coral reef ecosystems, face mounting anthropogenic pressures. Addressing these data gaps requires fundamentally better ocean observation capabilities, yet progress is constrained by the high cost of field campaigns, and institutional risk-aversion toward novel technology. This dissertation addresses the “observation gap” in ocean science by exploring low-cost, IoTderived technologies and empirically driven modeling to create robust, scalable marine ecosystem observing systems. Chapter 2 presents an agent-based model that integrates empirical whale diving behavior and realistic vessel kinematics, to evaluate the detection performance of surface-based thermal infrared sensors under realistic operational conditions, providing an evidence base for speed-restriction and real-time avoidance policies. Chapter 3 introduces ALERT, an automated IoT system integrating custom VHF signal processors with LoRaWAN to deliver near-real-time notifications for efficient archival biologger retrieval. Deployed in Antarctica, it successfully detected returning emperor penguins, demonstrating that robust, polar-grade automation improves detection reliability and response time while reducing manual effort and freeing field teams for other scientific work. Chapter 4 extends this approach by introducing a cooperative, encounterdriven mesh network in which LoRa-enabled tags opportunistically exchange data at sea before wirelessly offloading to a fixed gateway. Empirically driven agent-based modeling and LoRa linkbudget analyses indicate that this architecture could recover nearly 100% of data from colonyreturning penguins and ~40% from non-returning individuals. Two independent prototypes and field tests fur…
abstract: The ocean covers 71% of Earth's surface yet remains profoundly undersampled. Climate change is driving warming, acidification and deoxygenation at unprecedented rates, while ecosystems, often represented by iconic umbrella species such as North Atlantic right whales, emperor penguins, and coral reef ecosystems, face mounting anthropogenic pressures. Addressing these data gaps requires fundamentally better ocean observation capabilities, yet progress is constrained by the high cost of field campaigns, and institutional risk-aversion toward novel technology. This dissertation addresses the “observation gap” in ocean science by exploring low-cost, IoTderived technologies and empirically driven modeling to create robust, scalable marine ecosystem observing systems. Chapter 2 presents an agent-based model that integrates empirical whale diving behavior and realistic vessel kinematics, to evaluate the detection performance of surface-based thermal infrared sensors under realistic operational conditions, providing an evidence base for speed-restriction and real-time avoidance policies. Chapter 3 introduces ALERT, an automated IoT system integrating custom VHF signal processors with LoRaWAN to deliver near-real-time notifications for efficient archival biologger retrieval. Deployed in Antarctica, it successfully detected returning emperor penguins, demonstrating that robust, polar-grade automation improves detection reliability and response time while reducing manual effort and freeing field teams for other scientific work. Chapter 4 extends this approach by introducing a cooperative, encounterdriven mesh network in which LoRa-enabled tags opportunistically exchange data at sea before wirelessly offloading to a fixed gateway. Empirically driven agent-based modeling and LoRa linkbudget analyses indicate that this architecture could recover nearly 100% of data from colonyreturning penguins and ~40% from non-returning individuals. Two independent prototypes and field tests fur…
author: Baille, Loicka Myriam R.
keywords: Ocean, Ecosystem, IoT
date published: 2026-05
date modified: 2026-07-27T22:05:19Z
provider: Woods Hole Oceanographic Institute
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Massachusetts Institute of Technology and Woods Hole Oceanographic Institution

PropertyValue
property ID: Handle
value: https://hdl.handle.net/1912/72998

PropertyValue
property ID: DOI
value: 10.1575/1912/72998