In the rapidly evolving landscape of the Internet of Things (IoT), ensuring consistency and reliability becomes a top priority for a seamless user experience. In many instances, reliability is assessed through Quality of Service (QoS) metrics, sidelining traditional reliability metrics that thrive on time-dependent failure rates. The lack of a comprehensive framework that fully integrates all layers of an IoT system adds to the complexity. This gap makes it difficult to pinpoint specific areas that need improvement and to conduct a thorough assessment of the system’s reliability. This project addresses this intricate challenge, which holds significant relevance for industry professionals but remains unresolved. The project introduced an IoT architecture spanning the Power, Device, Edge, Application, and Cloud Layers. Within each layer, potential failure points were identified, and the reliability was analysed deploying time-based failure rates with an exponential distribution. Reliability Block Diagrams (RBDs) were employed to map the intricate inter-dependencies, though the framework’s adaptive nature allows for other system reliability methodologies. One of the primary outcomes of this research is the development of a new framework tailored for evaluating the reliability of various IoT system components. This framework yields insights into both system reliability and availability over time, serving as a pivotal tool for stakeholders such as device manufacturers, system integrators, network providers, and research institutions. The results show how the framework emerges as a pivotal starting point for IoT system reliability evaluation. Before this thesis, the feasibility of such a framework was uncertain, with concerns about its potential bias – being either too pessimistic or optimistic. Yet, the tangible results from this work affirm its capability to provide a balanced and reasonable reliability estimation, given the intricacies of IoT devices. This paves the way for subsequent research, enabling a deeper dive into targeted enhancements and fostering a nuanced understanding of IoT reliability. / I det snabbt föränderliga IoT-landskapet (Internet of Things) är det av högsta prioritet att säkerställa konsekvens och tillförlitlighet för en sömlös användarupplevelse. I många fall bedöms tillförlitligheten med hjälp av QoSmått (Quality of Service), vilket innebär att traditionella tillförlitlighetsmått som bygger på tidsberoende felfrekvenser åsidosätts. Avsaknaden av ett heltäckande ramverk som integrerar alla lager i ett IoT-system bidrar till komplexiteten. Denna brist gör det svårt att identifiera specifika områden som behöver förbättras och att göra en grundlig bedömning av systemets tillförlitlighet. Detta projekt tar itu med denna komplicerade utmaning, som har stor relevans för branschfolk men som fortfarande inte har lösts. Projektet introducerade en IoT-arkitektur som spänner över kraft-, enhets-, Edge-, applikationsoch molnlagren. Inom varje lager identifierades potentiella felpunkter och tillförlitligheten analyserades med hjälp av tidsbaserade felfrekvenser med en exponentiell fördelning. Tillförlitlighetsblockdiagram (RBD) användes för att kartlägga de komplicerade ömsesidiga beroendena, även om ramverkets adaptiva natur möjliggör andra metoder för systemtillförlitlighet. Ett av de främsta resultaten av denna forskning är utvecklingen av ett nytt ramverk som är skräddarsytt för att utvärdera tillförlitligheten hos olika IoT-systemkomponenter. Detta ramverk ger insikter om både systemets tillförlitlighet och tillgänglighet över tid, och fungerar som ett viktigt verktyg för intressenter som tillverkare av enheter, systemintegratörer, nätverksleverantörer och forskningsinstitutioner. Resultaten visar hur ramverket framstår som en viktig utgångspunkt för utvärdering av IoT-systemens tillförlitlighet. Före den här avhandlingen var det osäkert om ett sådant ramverk var genomförbart, med farhågor om dess potentiella partiskhet - att vara antingen för pessimistisk eller optimistisk. De konkreta resultaten från detta arbete bekräftar dock ramverkets förmåga att ge en balanserad och rimlig uppskattning av tillförlitligheten, med tanke på IoT-enheternas komplexitet. Detta banar väg för efterföljande forskning, vilket möjliggör en djupare analys av riktade förbättringar och främjar en nyanserad förståelse av IoT-tillförlitlighet.

A recent model of Parkinson’s disease (PD) suggests that the neuropathological, behavioural and cognitive symptoms progress in stages. There is substantial evidence for a prodromal stage of PD, during which time pre-motor symptoms develop. Rapid eye movement (REM) sleep behaviour disorder (RBD) is a risk factor for developing PD and may be part of the pre-motor stage. In both disorders, neuropathological α-synuclein aggregates are thought to be a direct cause of the resulting symptoms. One model has shown that in rats, proteasome inhibition produced by systemic exposure to environmental toxins results in α-synuclein pathology and motor behaviour dysfunction that mimics the progression of PD in humans. The present study examined the hypothesis that the systemic proteasome inhibition model would produce pre-Parkinsonian RBD-like pathology in rats. It was expected that sleep disturbances would be seen prior to behavioural disturbances in rats treated systemically with PSI (a proteasome inhibitor). Following baseline sleep recording and training on the inclined beam-traverse task, rats were injected with PSI (a proteasome inhibitor) or ethanol (control), 6 times over 2 wk. Sleep recording over 8 wk and behavioural testing over 16 wk provided no evidence of sleep disturbances or motor dysfunction. Post-mortem immunohistochemical analyses of brain tissue provided no evidence of PSI-associated α-synuclein aggregates in the locus coeruleus, subcoeruleus (dorsal part), or substantia nigra (areas involved in RBD and/or PD). These results did not provide support for RBD as a prodromal phase of PD within the systemic proteasome inhibitor-based model and add to a growing body of research reporting inconsistent findings using this model. We suggest that systemic PSI exposure in rats does not produce a viable model of RBD or PD. Whether RBD is an early symptom in the progression of PD remains to be established. / Thesis (Master, Neuroscience Studies) -- Queen's University, 2010-04-28 12:04:50.613

No description available.