Master of Science Aril Bernhard Ovesen will Thursday May 28th, 2026, at 12:15 hold his Thesis Defense for the PhD degree in Science. The title of the thesis is:
« System Support for Compliant and Robust Data Storage and Processing in Remote Electronic Monitoring of Commercial Fisheries »
The over-exploitation of marine resources poses a serious threat to both the sustainability of fish stocks and the overall health of marine ecosystems. A major contributor to this issue is illegal, unreported, and unregulated fishing. To address this, the European Union and other governments have introduced Remote Electronic Monitoring (REM) programs for commercial fishing vessels. REM systems use sensors, such as video cameras, to continuously collect and analyze data, in order to support fishery inspectors and regulatory agencies in documenting fishing practices and detecting suspicious activity.
The deployment of REM systems on board fishing vessels has introduced several challenges, related to the lack of connectivity for long periods during voyages in remote ocean areas, risks of tampering by local adversaries, and the need to ensure compliance with modern regulatory frameworks such as the GDPR. Additionally, the use of REM has been met with concerns among fishing crews regarding potential privacy-infringement and government overreach.
To address these issues, we have developed Dorvu, a software system for supporting REM deployments at the edge, through robust, secure, and compliant storage and analysis of collected data. Dorvu facilitates an edge surveillance model which includes both inspectors and data subjects in the loop. Dorvu provides utility as tool for digital forensics for monitoring of fishery activity by inspectors, who can avoid overreach through compliance mechanisms enforced by the storage system. At the same time, data subjects are given means to verify that their sensitive data is not misused. When surveillance data is used as forensic evidence, both parties are able to audit the system to determine if data retrieved from Dorvu is genuine and was accessed in accordance with relevant regulations.
We present Dorvu as a distributed edge platform prototype for storage and analysis of fishery data on offshore fishing vessels, with the goal of promoting sustainable fishing practices. Dorvu facilitates robust and compliant surveillance and provides system support for REM scenarios by enabling trustworthy forensics without compromising data subject rights.
Supervisory Committee:
1st Opponent: Assistant Professor, Rolando Martins, University of Porto, Portugal
2nd Opponent: Professor Özgü Alay, UiO, Norway
Internal member and leader of the committee: Associate professor Weihai Yu, Department of Computer Science, UiT