IoT and Data Governance in Long-Term Environmental Monitoring

Bruken av intelligente teknologier, IoT enheter, i forskning har bidratt til å gjøre arbeid med miljøforskning lettere og mer tilgjengelig, da det er mulig å måle nesten det en vil til en lav pris. Eksempler på intelligente teknologier er sensorer som måler temperatur, fuktighet eller trykk, kameraer og akustiske enheter slik som ekkolodd, og de er intelligente fordi de kan levere data automatisk uten behov menneskelig interaksjon. Automatiske innsamlinger av sanntidsdata, ofte fra enheter med ulik kvalitet og følsomhet, fører imidlertid til lagring av omfattende mengder med rådata. Som et resultat av dette må forskere kontinuerlig forsikre seg om at enhetene er kalibrerte, og gjøre dataene pålitelige, lesbare og meningsfulle med hensyn til konteksten. For å gjøre forskning gjennomførbart, er forskningsstasjoner – som også kan bli referert til som forskningsinfrastrukturer – noen ganger avhengige av finansiering via styrende institusjoner som for eksempel ESFRI og Forskningsrådet i Norge. For å motta finansiering – og med et økt fokus på åpen datadeling mellom forskningsinfrastrukturer, stilles det høyere krav til datadokumentasjon i henhold til de standarder og krav som myndighetene setter. Dette er med på å påvirke forskeres datastyringsarbeid. Forskningen som er gjort i dette prosjektet har som mål å bidra med empirisk innsikt i hvordan intelligente teknolgier brukes på forskningsstasjoner for miljøovervåkning, og hvordan forskeres arbeid påvirkes av økt bruk av teknologi i overvåkning. Det er undersøkt hvordan forskere opplever retningslinjene satt av forskningsinstitusjoner om hvordan dataene skal dokumenteres, og hvordan deres dataarbeid må tilpasses deretter. Det er gjennomført et casestudie av utvalgte forskningsstasjoner for miljøovervåkning i Norge, og studien baserer seg på kvalitative data fra strukturerte og semi-strukturerte intervjuer og relevante dokumenter fra myndighetene slik som strategidokumenter, retningslinjer og veikart. Funnene er basert på informasjon fra intervjuer av informanter som til daglig arbeider på forskningsstasjoner for miljøovervåkning som dataledere, forskningssjefer, forskningskoordinatorer eller miljøforskere. Funnene viser at IoT basert miljøovervåkning er muliggjort av datastyringsarbeid, med etablerte prosesser for å sikre at store sett av rådata blir gjort pålitelige, lesbare og meningsfulle for å støtte fremtidig gjenbruk og tolkning. Prosessene er påvirket av retningslinjer, prosedyrer og standarder for hvordan dataene skal samles inn og håndteres, som er med på å påvirke forskeres arbeidspraksiser. På grunn av begrensninger i tid, finansiering og ressurser, viser funnene imidlertid at det kan være vanskelig å ha ønsket kvalitet i forskernes vitenskapelige arbeid.The use of intelligent technologies, particularly IoT devices, in research has contributed to more accessible and available monitoring of the environment with the possibility of monitoring almost anything with low costs. Examples of intelligent technologies used in environmental research are sensors monitoring temperature, humidity or pressure, cameras, and acoustic devices like echo sounders. The devices are intelligent because they can automatically deliver data without human interaction. However, automatic collections of real-time data from sensor devices with heterogeneous quality and sensitivity lead to extensive raw data storage. Subsequently, researchers must ensure that the devices are calibrated and ensure that the data are trustworthy, readable, and meaningful to the context. To make research feasible, research institutions – which can also be referred to as research infrastructures – sometimes are dependent on receiving funding from governing institutions such as ESFRI and the Research Council of Norway. In order to receive funding – and with an increased focus on open data sharing between research infrastructures, researchers must adapt to the governments' guidelines and requirements to document their research, influencing their data governance activities. This research aims to contribute empirical insights into the use of intelligent technologies at research infrastructures for environmental monitoring and how researchers' work is affected by the increased use of technology in monitoring. Additionally, researchers' experience adapting to guidelines and requirements on data documenting is also investigated. It is conducted a case study of selected research infrastructures for environmental research in Norway. The study is based on qualitative data collected from structured and semi-structured interviews and relevant documents from governing institutions such as strategy documents, guidelines, and roadmaps. The findings are based on information retrieved from interviews of data managers, environmental researchers, research coordinators, and research managers that daily work at research infrastructures for environmental monitoring. The findings show that IoT-based environmental monitoring is enabled by data governance with the established processes to translate raw, often big data sets, into reliable, readable, and meaningful information to support future reuse and interpretation. The processes are affected by policies, procedures, and standards on collecting and managing the data that consequently affect researchers’ work practices. However, due to constraints in time, funding, and resources, the findings also show that it can be challenging to have the desired quality in researchers’ scientific work

Drowsiness Detection Using Federated Learning: Lessons Learnt from Dealing with Non-IID Data

The privacy of personal data is paramount in the realm of assisted living and digital healthcare. Federated Learning (FL), with its decentralised model training approach, has emerged as a compelling solution to reconcile the need for personalised models with the requirement to protect sensitive personal information. By allowing model training to occur locally on user devices without centralising raw data, FL is intended to strike a balance between personalisation and privacy. While the potential benefits of FL in assisted living and digital healthcare are substantial, practical implementation poses significant challenges. One of them is the non-Independently and Identically Distributed (non-IID) nature of personal data. Unlike centralised datasets, non-IID data exhibits inherent variability across different individuals, as well as their surrounding contexts. Unfortunately, many research approaches in this domain often overlook the nuances of non-IID data, potentially leading to models that lack robust generalisation across diverse healthcare scenarios. To highlight the importance of this challenge, in this paper, we report on our hands-on experience of building a FL system for drowsiness detection using non-IID data. We compare this federated setup with a traditional, centralised approach to model training by identifying and discussing the associated challenges from multiple perspectives, as well as possible solutions and recommendations for further research.publishedVersio

Raft Protocol for Fault Tolerance and Self-Recovery in Federated Learning

Federated Learning (FL) has emerged as a decentralised machine learning paradigm for distributed systems, particularly in edge and IoT environments. However, ensuring fault tolerance and self-recovery in such scenarios remains challenging, because of the centralised model aggregation which acts as a single point of failure. A possible solution to this challenge would rely on the continuous replication of the global FL state across participating nodes and the functional suitability of any node to replace the aggregator in case of failures. These functional requirements can be implemented using one of the existing distributed consensus algorithm, such as Raft. Our approach utilises Raft's leader election and log replication mechanisms to enable automatic stateful recovery after failures and thus to improve fault tolerance. The log replication process efficiently maintains consistency and coherence across distributed FL nodes, ensuring uninterrupted training process and model convergence. This enhances the robustness of the overall FL system, especially in dynamic and unreliable cyber-physical conditions. To demonstrate the viability of our approach, we present a proof-of-concept implementation based on the existing FL framework Flower. We conduct a series of experiments to measure the aggregator re-election time and traffic overheads associated with the state replication. Despite the expected traffic overheads growing with the number of FL nodes, the results demonstrate a resilient self-recovering system capable of withstanding node failures while maintaining model consistency.publishedVersio

Latency-Aware Node Selection in Federated Learning

Federated learning (FL) relies on the frequent exchange of model parameters between clients and the aggregator to achieve efficient model convergence. However, network latency presents a significant challenge, particularly in congested edge/IoT scenarios, hindering the efficiency and effectiveness of distributed machine learning (ML). While existing solutions often depend on hard-coded topologies, addressing this challenge is critical to unlocking FL's full potential in real-world scenarios. This paper proposes a novel approach to mitigate network latency issues by introducing a threefold functionality: latency-aware client selection, latency-aware aggregator assignment, and consistent replication of training progress. Our proof of concept provides a scalable and robust solution to alleviate latency's impact and improve the efficiency of distributed ML operations. Through this research, we aim to advance the field of FL by offering practical solutions that enhance performance and resilience in latency-sensitive environments.acceptedVersio

Delegating Agency in the Public Sector: A Case Study on Current Human-Technology Practices and Visions for AI

Human-technology collaboration is currently receiving a surge of attention in Information Systems (IS) due to attempts to introduce Artificial Intelligence (AI) in private and public organizations. In Scandinavia, governments are introducing AI-infused services to support decision-making and enhance efficiency in case processing within healthcare, education, and welfare. However, there is a need to shed more light on the conditions that precede AI implementation and the path that leads organizations to envision AI as a solution to a problem. We ask: How do humans and technology cooperate in the public sector? How is AI visioned to be part of this in the future? We report from an ongoing qualitative case study of work practices to assess sick leave cases at a Scandinavian welfare agency in which AI gradually emerged as a means to achieve more efficient resource distribution at the agency. Inspired by the concept of delegation drawn from Actor-Network Theory, we trace the distribution of work across technical and human agents in the sick leave department and illustrate how the agency is starting to envision a way to delegate tasks to AI-based tools in the future

Answers, Discussion and Teaching Points for Myopathies with Contracture

This PDF answers the questions posed in Issue 1 of the RRNMF Neuromuscular Journal, along with further discussion as to how to approach a case of muscle contracture and myopathy, as well as teaching points

Digital Transformation in the Welfare Sector: Unveiling Caseworkers’ Role for Service Provision

Digital Transformation (DT) involves processes of technological change to enhance the efficiency and accessibility of organizations. In this paper, we study DT in public welfare agencies, an often-overlooked type of organization. We focus on how DT affects the work and role of caseworkers, public employees who act as counselors and manage citizens who ask for benefits. We draw on an ongoing qualitative case study of the work to assess sick leave cases at a Scandinavian welfare agency. Our findings illustrate that caseworkers’ role as intermediaries between the agency and citizens becomes even more prominent and configures in three main practices. We contribute empirical insights shedding light on how caseworkers navigate the DT to continuously provide services to citizens. Moreover, we show how they exercise discretion by using the technology they have to personalize the pathway of each citizen and scale up their capacity to assess cases in a shorter time. We conclude by discussing the implications of our study for Information Systems research and by characterizing the role of caseworkers as fundamental service providers during DT

Deep learning to predict power output from respiratory inductive plethysmography data

Power output is one of the most accurate methods for measuring exercise intensity during outdoor endurance sports, since it records the actual effect of the work performed by the muscles over time. However, power meters are expensive and are limited to activity forms where it is possible to embed sensors in the propulsion system such as in cycling. We investigate using breathing to estimate power output during exercise, in order to create a portable method for tracking physical effort that is universally applicable in many activity forms. Breathing can be quantified through respiratory inductive plethysmography (RIP), which entails recording the movement of the rib cage and abdomen caused by breathing, and it enables us to have a portable, non-invasive device for measuring breathing. RIP signals, heart rate and power output were recorded during a N-of-1 study of a person performing a set of workouts on a stationary bike. The recorded data were used to build predictive models through deep learning algorithms. A convolutional neural network (CNN) trained on features derived from RIP signals and heart rate obtained a mean absolute percentage error (MAPE) of 0.20 (ie, 20% average error). The model showed promising capability of estimating correct power levels and reactivity to changes in power output, but the accuracy is significantly lower than that of cycling power meters.publishedVersio

Utforsking av kompleks barnelitteratur gjennom dramapedagogisk arbeid - En studie av barneboka Brune

Formålet med denne studien er å utforske kompleks barnelitteratur gjennom en litteraturanalyse og gjennom implementering av dramapedagogiske metoder i litteraturundervisningen i norskfaget. Det er barneboka Brune (2013), skrevet av Håkon Øvreås og illustrert av Øyvind Torseter, som er sentrum for studien. Problemstillingen for denne masteroppgaven lyder: Hvordan kan et dramaforløp være en kollektiv måte å lese og utforske en kompleks og produktiv barnelitterær tekst? Den litterære analysen av Brune (2013) er helt vesentlig og ligger til grunn for utformingen og gjennomføringen av dramaforløpet. Litteraturanalysen utforsker de litterære karakterene, bokas sentrale tematikk rundt sorg, vennskap, konflikter og mot, og hvordan kompleksiteten og grenseoverskridelsene mellom det realistiske og det fantastiske legger til rette for produktiv lesning. Framstillingen av barneperspektivet i Brune (2013) har en sentral plass i formidlingen av barns unike evne til å blande fantasi og virkelighet i lek, og hvordan den litterære karakteren Rune bruker fantasien for å bearbeide sorgen etter bestefarens bortgang. Dette er elementer som tas med inn i dramaforløpet for å utforske i hvilken grad drama fungerer som en form for produktiv og dialogisk høytlesning av Brune (2013). Dramaforløpet gjennomføres med en elevgruppe på 2. trinn. Leken er sentral for barnet, og dramaforløpet bidrar til å komme nærmere elevenes virkelighet gjennom dets lekne tilnærming. Funnene fra dramaforløpet viser elevenes opplevelse av fiksjonen og hvor mye de lever seg inn i boka gjennom fiksjonen. I arbeidet med de litterære karakterene viser funnene at elevene har empati med Rune som litterær karakter og hvordan de blir opptatt av hva som er rett og galt. Dramaforløpet bidrar til å levendegjøre litteraturen for elevene gjennom ulike dramapedagogiske metoder og de blir aktivt deltakende i lesningen. Studien viser hvor mye den komplekse handlingen i Brune (2013) gir rom for refleksjon, undring og utforskning, og hvordan dramaforløpet legger til rette for dette.Title: Exploring complex children's literature through drama pedagogical work – A study of the children's book Brune. The purpose of this study is to explore complex children’s literature through a literary analysis and the implementation of drama pedagogical methods in Norwegian literature education. The focal point and center of this study is the children’s book “Brune”, written by Håkon Øvreås and illustrated by Øyvind Torseter. This master’s thesis examines the research question: How can a dramatic process serve as a collective way to read and explore a complex and productive children's literary text? The literary analysis is essential and forms the basis for the development of the dramatic process. The analysis explores the literary characters, the book’s central themes of grief, friendship, conflicts, and courage, and how the complexity and boundary-crossings between the realistic and the fantastic facilitate productive reading. The portrayal of the children’s perspective in Brune (2013) conveys the children unique ability to mix reality and imagination in play, and how the literary character Rune uses his imagination to process the grief after his grandfather’s passing. These are elements that are included into the dramatic process to explore the extent to which drama functions as a form of productive and dialogic read aloud of Brune (2013). The dramatic process is carried out with a group of 2nd grade pupils. Play is central to the child, and the dramatic process helps to get closer to the pupils’ reality through its playful approach. The findings from the dramatic process shows the pupils experience of the fiction and how much they immerse themselves in the fiction. When working with the literary characters the findings show how the students empathize with Rune as a literary character and how they are become engaged in discussions of right and wrong. The dramatic process helps bringing the literature to life for the pupils through various drama pedagogical methods, and the pupils become actively involved in the reading. The study demonstrates how much the complex plot in Brune (2013) provides room for reflection, wonder and exploration, and how the dramatic process facilitates this