Event Details
Development of a Secure Underwater Sensor Suite for Real-Time Environmental Monitoring of Blue Carbon Ecosystems
Presenter: Rudra Pratap Singh
Supervisor:
Date: Thu, December 11, 2025
Time: 11:00:00 - 00:00:00
Place: Zoom - see below.
ABSTRACT
Join Zoom Meeting
https://uvic.zoom.us/j/82178502011?pwd=Smd6DcSGwcl5ldtEngVLc3LaF6zjxa.1
Meeting ID: 821 7850 2011
Password: 277114
One tap mobile
+17789072071,,82178502011# Canada
+16475580588,,82178502011# Canada
Dial by your location
+1 778 907 2071 Canada
+1 647 558 0588 Canada
Meeting ID: 821 7850 2011
Find your local number: https://uvic.zoom.us/u/ks2F3GsAN
Abstract: The health of Canada’s blue-carbon ecosystems—kelp forests, seagrass meadows, and salt marshes—plays a vital role in marine biodiversity and long-term carbon sequestration. Yet these ecosystems are increasingly vulnerable to anthropogenic and natural stressors such as temperature variation, pH fluctuations, heavy-metal pollution, and hydrocarbon extraction. Traditional monitoring methods, relying on sporadic field sampling and manual analysis, fail to capture the temporal and spatial complexity of these changes. This thesis, Development of Machine Learning-Based Techniques for Monitoring and Analyzing the Effects of Natural and Manmade Stressors on Canada’s Blue Carbon Ecosystem Using a Secure Underwater Communication Suite, presents a comprehensive hardware-driven approach to address these gaps. The research involves the design, fabrication, and laboratory validation of a modular underwater sensor suite deployed via a Blue Robotics ROV platform to collect high-resolution oceanographic data. The integrated system measures temperature, salinity, dissolved oxygen, pH, turbidity, and chlorophyll concentrations through a network of calibrated probes, ensuring precise and repeatable environmental sensing. To support continuous operation, a secure underwater communication and data-handling framework was developed using a hybrid Ethernet-acoustic link and lightweight encryption protocols to preserve data integrity and mitigate cyber vulnerabilities within the Internet of Underwater Things (IoUT). Extensive laboratory testing in controlled aquatic environments demonstrated stable sensor calibration, minimal noise drift (< 0.05% FS), and consistent data throughput at depths up to 1 m. Complementary studies explored intrusion detection and federated-learning frameworks for distributed underwater nodes, strengthening the resilience of the proposed communication network