@article {42768,
	title = {{\'A}ika: A Distributed Edge System for AI Inference},
	journal = {Big Data and Cognitive Computing},
	volume = {6},
	year = {2022},
	month = {Jan-06-2022},
	pages = {68},
	publisher = {MDPI},
	abstract = {Video monitoring and surveillance of commercial fisheries in world oceans has been proposed by the governing bodies of several nations as a response to crimes such as overfishing. Traditional video monitoring systems may not be suitable due to limitations in the offshore fishing environment, including low bandwidth, unstable satellite network connections and issues of preserving the privacy of crew members. In this paper, we present {\'A}ika, a robust system for executing distributed Artificial Intelligence (AI) applications on the edge. {\'A}ika provides engineers and researchers with several building blocks in the form of Agents, which enable the expression of computation pipelines and distributed applications with robustness and privacy guarantees. Agents are continuously monitored by dedicated monitoring nodes, and provide applications with a distributed checkpointing and replication scheme. {\'A}ika is designed for monitoring and surveillance in privacy-sensitive and unstable offshore environments, where flexible access policies at the storage level can provide privacy guarantees for data transfer and access.\&nbsp;},
	doi = {10.3390/bdcc6020068},
	url = {https://www.mdpi.com/2504-2289/6/2/68},
	author = {Joakim Aalstad {Alslie} and Aril Bernhard {Ovesen} and Tor-Arne Schmidt {Nordmo} and H{\r a}vard Dagenborg {Johansen} and Halvorsen, P{\r a}l and Riegler, Michael and Johansen, Dag}
}
@inproceedings {42796,
	title = {Njord: a fishing trawler dataset},
	journal = {Proceedings of the 13th ACM Multimedia Systems Conference (MMSYS)},
	year = {2022},
	month = {08/2022},
	publisher = {ACM},
	address = {New York, NY, USA},
	abstract = {Fish is one of the main sources of food worldwide. The commercial fishing industry has a lot of different aspects to consider, ranging from sustainability to reporting. The complexity of the domain also attracts a lot of research from different fields like marine biology, fishery sciences, cybernetics, and computer science. In computer science, detection of fishing vessels via for example remote sensing and classification of fish from images or videos using machine learning or other analysis methods attracts growing attention. Surprisingly, little work has been done that considers what is happening on board the fishing vessels. On the deck of the boats, a lot of data and important information are generated with potential applications, such as automatic detection of accidents or automatic reporting of fish caught. This paper presents Njord, a fishing trawler dataset consisting of surveillance videos from a modern off-shore fishing trawler at sea. The main goal of this dataset is to show the potential and possibilities that analysis of such data can provide. In addition to the data, we provide a baseline analysis and discuss several possible research questions this dataset could help answer.},
	isbn = {9781450392839},
	doi = {10.1145/3524273.3532886},
	url = {https://dl.acm.org/doi/pdf/10.1145/3524273.3532886},
	author = {Tor-Arne Schmidt {Nordmo} and Aril Bernhard {Ovesen} and Bj{\o}rn Aslak {Juliussen} and Hicks, Steven and Thambawita, Vajira and H{\r a}vard Dagenborg {Johansen} and Halvorsen, P{\r a}l and Riegler, Michael and Johansen, Dag},
	editor = {Murray, Niall and Simon, Gwendal and Farias, Mylene and Viola, Irene and Montagud, Mario}
}
@article {28094,
	title = {File System Support for Privacy-Preserving Analysis and Forensics in Low-Bandwidth Edge Environments},
	journal = {Information},
	volume = {12},
	year = {2021},
	month = {10/2021},
	pages = {430},
	publisher = {MDPI},
	abstract = {In this paper, we present initial results from our distributed edge systems research in the domain of sustainable harvesting of common good resources in the Arctic Ocean. Specifically, we are developing a digital platform for real-time privacy-preserving sustainability management in the domain of commercial fishery surveillance operations. This is in response to potentially privacy-infringing mandates from some governments to combat overfishing and other sustainability challenges. Our approach is to deploy sensory devices and distributed artificial intelligence algorithms on mobile, offshore fishing vessels and at mainland central control centers. To facilitate this, we need a novel data plane supporting efficient, available, secure, tamper-proof, and compliant data management in this weakly connected offshore environment. We have built our first prototype of Dorvu, a novel distributed file system in this context. Our devised architecture, the design trade-offs among conflicting properties, and our initial experiences are further detailed in this paper.\&nbsp;\&nbsp;},
	doi = {10.3390/info12100430},
	url = {https://www.mdpi.com/2078-2489/12/10/430},
	author = {Aril Bernhard {Ovesen} and Tor-Arne Schmidt {Nordmo} and H{\r a}vard Dagenborg {Johansen} and Riegler, Michael and Halvorsen, P{\r a}l and Johansen, Dag}
}
@inproceedings {28229,
	title = {Njord: An out-in-the-wild real world fish vessel catch analysis dataset},
	journal = {Arctic Frontiers},
	year = {2021},
	publisher = {Arctic Frontiers},
	address = {Troms{\o}, Norway},
	author = {Riegler, Michael and Johansen, Dag and Bj{\o}rn Aslak {Juliussen} and Tor-Arne Schmidt {Nordmo} and Aril Bernhard {Ovesen} and Halvorsen, P{\r a}l and H{\r a}vard D. {Johansen} and Jon Petter {Rui}}
}
@inproceedings {28230,
	title = {Sustainable Commercial Fishing: Digital Inspectors to the Rescue},
	journal = {Arctic Frontiers},
	year = {2021},
	publisher = {Arctic Frontiers},
	author = {Johansen, Dag and Bj{\o}rn Aslak {Juliussen} and Tor-Arne Schmidt {Nordmo} and Aril Bernhard {Ovesen} and Halvorsen, P{\r a}l and H{\r a}vard Dagenborg {Johansen} and Riegler, Michael and Jon Petter {Rui}}
}
