In resource-intensive Internet of Things applications, Lightweight Stream Ciphers (LWSCs) play a vital role in influencing both the security and performance of the system. Numerous LWSCs have been proposed, each offering certain properties and trade-offs that carefully balance security and performance requirements. This paper presents a comprehensive evaluation of prominent LWSCs, with a focus on ...

This thesis proposes a secure and resilient system for generating and managing unique identification number series for tracing food products within the Norwegian fishing industry, without relying on a central authority. Given the context of mutual mistrust among stakeholders and the threat of hostile entities, this project proposes a blockchain-based solution to ensure the uniqueness and security ...

The marine sector has unique and challenging problems supporting high bandwidth, low-latency internet connectivity, often unavailable or only avail able through satellite services. Multi-hop manets that utilise low-cost com modity hardware potentially offer a cost-effective solution compared to satellite services but come with their own limitations. This thesis is motivated by the need for reliable ...

In our increasingly interconnected digital landscape, the constant generation and consumption of data on various computing devices present challenges for ensuring constant accessibility, particularly in intermittent network scenarios. The emerging focus on distributed systems is aimed at not only managing substantial data volumes but also guaranteeing storage on devices for low latency and high ...

Regulating the catch of fishing vessels is crucial for maintaining sustainable fish populations, preventing illegal fishing, and ensuring the quality of the fish being delivered. One effective method of controlling the catch is to have controllers physically present at the port where the catch is being delivered. However, vessels do not always report their destination port in a timely manner, which ...

In personalized software, collected user data is used to give a tailored user experience. A user might be interested in understanding how their data (inputs) resulted in their personalized output. The Variable Dependency Graph (VDG) can explain how inputs of a program flow to the output. However, with increasing program size, there is a need for summarizing the VDGs and understanding these ...

We believe the underutilization of clinically validated algorithms for heart health assessment in current medical platforms undermines patient confidence in using associated screening instruments. The importance of earlier detection and improved health outcomes in heart disease motivated the development of these screening instruments. Available risk assessment tools are stand-alone online apps or ...

Artificial Intelligence (AI) and the underlying Machine Learning (ML) technology is experiencing increased applications in various areas. The training of ML models requires significant amounts of data, and data might contain restrictions regarding their permitted usage. High-performant models are often called black-boxes because of their complex decision-making process. Thus, ML applications threaten ...

Federated Learning (FL) is a privacy-preserving approach to train machine learning models on distributed datasets across different organizations. This is particularly beneficial for domains like healthcare and finance, where user data is often sensitive and tabular (e.g., hospital records and financial transactions). However, recent research like Tableak highlighted vulnerabilities that can exploit ...

For people with Type 1 Diabetes Mellitus (T1DM), engaging in physical activities (PA) presents unique challenges. The aim of this thesis was to improve the prediction of blood glucose (BG) levels for individuals with T1DM during and after PA. The study began with a literature review to guide the research direction and understand existing prediction models. Then particular emphasis was placed on ...