@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}
}
