# Aika: A Distributed Edge System for AI Inference
## 1 Introduction  
In attempts to decrease latency, and increase security and reliability, some new AI solutions are gradually being deployed and executed on the edge. [PERSON] et al. [1] and [PERSON] et al. [2] discuss challenges related to the field of AI, and list both edge computing and security as important topics of research. The term _edge intelligence_ is often used to describe the confluence of the edge computing and AI fields [3]. Moving AI solutions to the edge may be required for systems that operate in environments where access to high-speed internet connections is either limited or non-existent, and available bandwidth is too low for effective data transportation. For instance, on-boat computer systems that operate in remote locations such as international waters and Antarctica rely on satellite connection to communicate with on-land services outside their environment. The development of 5G networks may improve connectivity in the future, but global coverage is not likely due to its short range [4]. Deploying an AI system on the edge comes with certain challenges, particularly for monitoring and surveillance systems that are concerned with privacy regulations and agreements. Systems deployed at physically remote edge locations may be prone to attacks from malicious actors in the environment, and the consequences of data leaks are more severe when sensitive and private data are being generated and stored. AI systems that deal with private data of users also need to satisfy the requirements of privacy-governing laws such as the General Data Protection Regulation (GDPR) [5] and constitutional rights to privacy. This places restrictions on the monitoring and data-collection process, while also heightening requirements of confidentiality and resilience to hostile attacks. This article presents Aika, a robust system for executing distributed AI applications on the edge. Aika is developed and evaluated in a scientific context as a concrete edge computing platform specially targeting support for real-time AI systems with special security and fault-tolerance properties. A key property of Aika is how it remains active and performs continuous analysis of data during various component failures. We investigate how to provide efficient data analytics in an unstable and non-trusted edge environment. Our work on the Aika system is motivated by the need for highly automated continuous AI-based monitoring and privacy-preserving surveillance of fishing vessels at sea. Fish is considered one of the most important food sources in the world, and it is estimated that it currently makes up around 17% of the current production of edible protein on a global scale [6]. At the same time, the fishing industry has fallen victim to crime in the form of illegal fishing and over-exploitation. According to the United Nations Office on Drugs and Crime, crimes in the fishery industry are typically organized and transnational in nature, and include money laundering, illegal fishing, document fraud, and drug trafficking [7]. The system is developed and evaluated in the context of surveillance and monitoring off the shore of Norway for the purpose of enforcing sustainable resource management and fish harvesting in the Arctic. This computing environment is assumed to be both unstable and untrusted. Systems operating in it have elevated risk of faults and intrusions compared to stationary or cloud-connected systems. Factors that contribute to this risk include low and unpredictable bandwidth, high latency, unstable connections, remote locations, and uncertain time frames between physical interaction with the components of the system, in addition to the potential threat of malicious actors. The challenging weather conditions in the Arctic that such fishing vessels operate in are also a concern. This increases the importance of designing a robust and secure system that not only is able to tolerate faults, but can also detect and monitor them.