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  • 1.
    Khadam, Umair
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Davidsson, Paul
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Spalazzese, Romina
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Exploring the Role of Artificial Intelligence in Internet of Things Systems: A Systematic Mapping Study2024Ingår i: Sensors, E-ISSN 1424-8220, Vol. 24, nr 20, artikel-id 6511Artikel, forskningsöversikt (Refereegranskat)
    Abstract [en]

    The use of Artificial Intelligence (AI) in Internet of Things (IoT) systems has gained significant attention due to its potential to improve efficiency, functionality and decision-making. To further advance research and practical implementation, it is crucial to better understand the specific roles of AI in IoT systems and identify the key application domains. In this article we aim to identify the different roles of AI in IoT systems and the application domains where AI is used most significantly. We have conducted a systematic mapping study using multiple databases, i.e., Scopus, ACM Digital Library, IEEE Xplore and Wiley Online. Eighty-one relevant survey articles were selected after applying the selection criteria and then analyzed to extract the key information. As a result, six general tasks of AI in IoT systems were identified: pattern recognition, decision support, decision-making and acting, prediction, data management and human interaction. Moreover, 15 subtasks were identified, as well as 13 application domains, where healthcare was the most frequent. We conclude that there are several important tasks that AI can perform in IoT systems, improving efficiency, security and functionality across many important application domains.

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  • 2.
    Lukianykhin, Oleh
    et al.
    Department of Information Technologies, Sumy State University, Sumy, Ukraine.
    Shendryk, Vira
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP). Department of Information Technologies, Sumy State University, Sumy, Ukraine.
    Shendryk, Sergii
    Department of Cybernetics and Informatics, Sumy National Agrarian University, Sumy, Ukraine.
    Malekian, Reza
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Promising AI Applications in Power Systems: Explainable AI (XAI), Transformers, LLMs2024Ingår i: New Technologies, Development and Application VII: Advanced Production Processes and Intelligent Sytems, Volume 2 / [ed] Isak Karabegovic; Ahmed Kovačević; Sadko Mandzuka, Springer, 2024, s. 66-76Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper aims to analyze and identify the most promising opportunities for Artificial Intelligence (AI) applications in the Power Systems (PS) domain. It identifies major challenges faced in PS and explores the corresponding technical tasks: forecasting and optimal control. Then, the paper investigates the key AI techniques commonly employed in PS for these tasks, e.g. reinforcement learning (RL) and time series forecasting. It also highlights promising methods with great potential in advancing PS solutions: attention-based models (Transformers, LLMs) and explainable AI (XAI) approaches. This study’s primary contribution lies in identifying critical research gaps in AI for PS, highlighting areas where research and development may have the biggest impact. Additionally, the paper provides a structured literature overview, serving as a valuable resource for researchers and practitioners in the field.

  • 3.
    Belfrage, Michael
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Johansson, Emil
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Lorig, Fabian
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Davidsson, Paul
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    [In]Credible Models – Verification, Validation & Accreditation of Agent-Based Models to Support Policy-Making2024Ingår i: JASSS: Journal of Artificial Societies and Social Simulation, E-ISSN 1460-7425, Vol. 27, nr 4, artikel-id 4Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This paper explores the topic of model credibility of Agent-based Models and how they should be evaluated prior to application in policy-making. Specifically, this involves analyzing bordering literature from different fields to: (1) establish a definition of model credibility -- a measure of confidence in the model's inferential capability -- and to (2) assess how model credibility can be strengthened through Verification, Validation, and Accreditation (VV&A) prior to application, as well as through post-application evaluation. Several studies have highlighted severe shortcomings in how V&V of Agent-based Models is performed and documented, and few public administrations have an established process for model accreditation. To address the first issue, we examine the literature on model V&V and, based on this review, introduce and outline the usage of a V&V plan. To address the second issue, we take inspiration from a practical use case of model accreditation applied by a government institution to propose a framework for the accreditation of ABMs for policy-making. The paper concludes with a discussion of the risks associated with improper assessments of model credibility. 

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  • 4.
    Doorshi, Raoof
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Khoshkangini, Reza
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Rajabi, Enayat
    Cape Breton University, Sydney, NS, Canada.
    Sahba, Amin
    The University of Texas at San Antonio, San Antonio, TX, USA.
    Sahba, Ramin
    The University of Texas at San Antonio, San Antonio, TX, USA.
    A Graph-Attention Solution for Breakdown Prediction (GASBP)2024Ingår i: Intelligent Systems and Applications: Proceedings of the 2024 Intelligent Systems Conference (IntelliSys) Volume 4 / [ed] Kohei Arai, Springer, 2024, s. 62-78Konferensbidrag (Refereegranskat)
    Abstract [en]

    Breakdown prediction is one of the essential steps in the maintenance of machinery devices and vehicles. The utilization of machine learning in breakdown detection is not new and has gone a long way, however, using sensory measurements of vehicles to forecast faults is still a challenge to be completely overcome. The strength of graph-based techniques in solving complex problems, and the recent success of these approaches to various modeling problems, motivated us to utilize such approaches to tackle the complex task of breakdown prediction. Thus, in this study, we propose a graph-based system that contains two main modules. The first module deals with preprocessing data which is a crucial part of the approach. In the second module, we used and adapted the GMAN technique to find the temporal aspect of the measurements and map them to the breakdown, such that we ultimately built the model for the final breakdown prediction. Our preliminary evaluation experiments on the forestry vehicles’ collected data showed promising.

  • 5.
    Ymeri, Gent
    Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Machine Learning-Driven Analysis of Sensor Data for Objective Assessment of Parkinson's Disease Motor Symptoms in Home Environments2024Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [en]

    Parkinson’s disease (PD) is a progressive neurodegenerative brain disorder that signifi- cantly impacts quality of life for those who are affected. It is a rapidly growing condition affecting millions of people worldwide, where treatments focus on managing symptoms and slowing the degenerative process, as there are no validated treatments that can stop its progression or preemptively prevent it. Effective management of the disease relies on accurate and timely assessment of symptoms based on clinical ratings, traditionally performed through clinical examinations using the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS). However, in-clinic assessments are infrequent and may not capture the full spectrum of symptom fluctuations in daily life. While existing literature has focused on diagnosing PD, the current understanding falls short in terms of objectively quantifying its symptoms in daily-living conditions.

    Following a design science research methodology, this thesis responds to this research gap by exploring the feasibility of using smartphones to quantify PD symptoms in a real- world, at-home setting. The research presents a cross-platform mobile application de- veloped for data collection from PD patients with the aim to identify promising system components and data types for capturing PD symptoms. Using data mining and machine learning techniques, the research explores if it is feasible to estimate the MDS-UPDRS scale based on objective measurements from smartphone-collected data. Additionally, it investigates the usability of the proposed mobile application for PD patients. By de- veloping and validating a cross-platform mobile application for symptom capturing, this thesis contributes both in terms of research results communicated in the associated peer- reviewed papers, and by providing an open source based app which makes PD symptom assessments more accessible, objective, and patient-centric.

    Delarbeten
    1. Linking data collected from mobile phones withsymptoms level in Parkinson’s Disease: Dataexploration of the mPower study
    Öppna denna publikation i ny flik eller fönster >>Linking data collected from mobile phones withsymptoms level in Parkinson’s Disease: Dataexploration of the mPower study
    2022 (Engelska)Ingår i: Pervasive Computing Technologies for Healthcare: 16th EAI International Conference, PervasiveHealth 2022, Thessaloniki, Greece, December 12-14, 2022, Proceedings / [ed] Tsanas, Athanasios; Triantafyllidis, Andreas, Cham: Springer, 2022Konferensbidrag, Publicerat paper (Refereegranskat)
    Abstract [en]

    Advancements in technology, such as smartphones and wearabledevices, can be used for collecting movement data through embeddedsensors. This paper focuses on linking Parkinson’s Disease severitywith data collected from mobile phones in the mPower study. As referencefor symptoms’ severity, we use the answers provided to part 2 ofthe standard MDS-UPDRS scale. As input variables, we use the featurescomputed within mPower from the raw data collected during 4 phonebasedactivities: walking, rest, voice and finger tapping. The features arefiltered in order to remove unreliable datapoints and associated to referencevalues. After pre-processing, 5 Machine Learning algorithms areapplied for predictive analysis. We show that, notwithstanding the noisedue to the data being collected in an uncontrolled manner, the regressedsymptom levels are moderately to strongly correlated with the actualvalues (highest Pearson’s correlation = 0.6). However, the high differencebetween the values also implies that the regressed values can not beconsidered as a substitute for a conventional clinical assessment (lowestmean absolute error = 5.4).

    Ort, förlag, år, upplaga, sidor
    Cham: Springer, 2022
    Serie
    Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, ISSN 1867-8211
    Nyckelord
    mobile health, Parkinson’s disease, mPower data
    Nationell ämneskategori
    Datavetenskap (datalogi)
    Identifikatorer
    urn:nbn:se:mau:diva-58646 (URN)10.1007/978-3-031-34586-9_29 (DOI)2-s2.0-85164108273 (Scopus ID)978-3-031-34585-2 (ISBN)978-3-031-34586-9 (ISBN)
    Konferens
    16th EAI International Conference, Pervasive Health 2022, Thessaloniki, Greece, December 12-14, 2022
    Tillgänglig från: 2023-03-14 Skapad: 2023-03-14 Senast uppdaterad: 2024-10-30Bibliografiskt granskad
    2. Mobistudy: Mobile-based, platform-independent, multi-dimensional data collection for clinical studies
    Öppna denna publikation i ny flik eller fönster >>Mobistudy: Mobile-based, platform-independent, multi-dimensional data collection for clinical studies
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    2022 (Engelska)Ingår i: IoT 2021: Conference Proceedings, ACM Digital Library, 2022, s. 219-222Konferensbidrag, Publicerat paper (Refereegranskat)
    Abstract [en]

    Internet of Things (IoT) can work as a useful tool for clinical research. We developed a software platform that allows researchers to publish clinical studies and volunteers to participate into them using an app and connected IoT devices. The platform includes a REST API, a web interface for researchers and an app that collects data during tasks volunteers are invited to contribute. Nine tasks have been developed: Forms, Positioning, Finger tapping, Pulse-oximetry, Peak Flow measurement, Activity tracking, Data query, Queen’s College step test and Six-minute walk test. These leverage sensors embedded in the phone, connected Bluetooth devices and additional APIs like HealthKit and Google Fit. Currently, the platform is used in two clinical studies by 25 patients: an asthma management study in the United Kingdom, and a neuropathic pain management study in Spain.

    Ort, förlag, år, upplaga, sidor
    ACM Digital Library, 2022
    Nyckelord
    clinical research, m-Health, IoT
    Nationell ämneskategori
    Datavetenskap (datalogi)
    Identifikatorer
    urn:nbn:se:mau:diva-50618 (URN)10.1145/3494322.3494363 (DOI)000936000600025 ()2-s2.0-85127119368 (Scopus ID)978-1-4503-8566-4 (ISBN)
    Konferens
    11th International Conference on the Internet of Things, November 8-11, 2021. St.Gallen, Switzerland
    Forskningsfinansiär
    KK-stiftelsen, 20140035
    Tillgänglig från: 2022-03-14 Skapad: 2022-03-14 Senast uppdaterad: 2024-10-30Bibliografiskt granskad
    3. Quantifying Parkinson's disease severity using mobile wearable devices and machine learning: the ParkApp pilot study protocol
    Öppna denna publikation i ny flik eller fönster >>Quantifying Parkinson's disease severity using mobile wearable devices and machine learning: the ParkApp pilot study protocol
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    2023 (Engelska)Ingår i: BMJ Open, E-ISSN 2044-6055, Vol. 13, nr 12, artikel-id e077766Artikel i tidskrift (Refereegranskat) Published
    Abstract [en]

    INTRODUCTION: The clinical assessment of Parkinson's disease (PD) symptoms can present reliability issues and, with visits typically spaced apart 6 months, can hardly capture their frequent variability. Smartphones and smartwatches along with signal processing and machine learning can facilitate frequent, remote, reliable and objective assessments of PD from patients' homes.

    AIM: To investigate the feasibility, compliance and user experience of passively and actively measuring symptoms from home environments using data from sensors embedded in smartphones and a wrist-wearable device.

    METHODS AND ANALYSIS: In an ongoing clinical feasibility study, participants with a confirmed PD diagnosis are being recruited. Participants perform activity tests, including Timed Up and Go (TUG), tremor, finger tapping, drawing and vocalisation, once a week for 2 months using the Mobistudy smartphone app in their homes. Concurrently, participants wear the GENEActiv wrist device for 28 days to measure actigraphy continuously. In addition to using sensors, participants complete the Beck's Depression Inventory, Non-Motor Symptoms Questionnaire (NMSQuest) and Parkinson's Disease Questionnaire (PDQ-8) questionnaires at baseline, at 1 month and at the end of the study. Sleep disorders are assessed through the Parkinson's Disease Sleep Scale-2 questionnaire (weekly) and a custom sleep quality daily questionnaire. User experience questionnaires, Technology Acceptance Model and User Version of the Mobile Application Rating Scale, are delivered at 1 month. Clinical assessment (Movement Disorder Society-Unified Parkinson Disease Rating Scale (MDS-UPDRS)) is performed at enrollment and the 2-month follow-up visit. During visits, a TUG test is performed using the smartphone and the G-Walk motion sensor as reference device. Signal processing and machine learning techniques will be employed to analyse the data collected from Mobistudy app and the GENEActiv and correlate them with the MDS-UPDRS. Compliance and user aspects will be informing the long-term feasibility.

    ETHICS AND DISSEMINATION: The study received ethical approval by the Swedish Ethical Review Authority (Etikprövningsmyndigheten), with application number 2022-02885-01. Results will be reported in peer-reviewed journals and conferences. Results will be shared with the study participants.

    Ort, förlag, år, upplaga, sidor
    BMJ Publishing Group Ltd, 2023
    Nyckelord
    health informatics, parkinson's disease, telemedicine
    Nationell ämneskategori
    Neurologi
    Identifikatorer
    urn:nbn:se:mau:diva-64860 (URN)10.1136/bmjopen-2023-077766 (DOI)001134943800008 ()38154904 (PubMedID)2-s2.0-85181165016 (Scopus ID)
    Tillgänglig från: 2024-01-08 Skapad: 2024-01-08 Senast uppdaterad: 2024-10-30Bibliografiskt granskad
    4. Measuring finger dexterity in Parkinson's disease with mobile phones
    Öppna denna publikation i ny flik eller fönster >>Measuring finger dexterity in Parkinson's disease with mobile phones
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    2024 (Engelska)Ingår i: 2024 IEEE International Conference on Pervasive Computing and Communications: workshops and other affiliated events, percom workshops, Institute of Electrical and Electronics Engineers (IEEE), 2024, s. 112-116Konferensbidrag, Publicerat paper (Refereegranskat)
    Abstract [en]

    This work aims to link finger tapping and drawing tests performed on mobile phone screens with clinical ratings of Parkinson's Disease (PD). Thirty PD patients were recruited and instructed to carry out these tests in their homes. Features were extracted and used to assess the validity of the data vis a vis clinical scales (MDS-UPDRS). Statistical tests show that several features correlate with clinical scores (max correlation 0.54) and significant differences between data collected before and after medication intake (p<0.05), demonstrating the clinical validity of smartphone data. The use of Machine Learning (ML) algorithms to regress Part-3 of MDS-UPDRS further supports the validity with an absolute mean error of 6.25 (over a 0-72 scale).

    Ort, förlag, år, upplaga, sidor
    Institute of Electrical and Electronics Engineers (IEEE), 2024
    Serie
    IEEE Annual Conference on Pervasive Computing and Communications Workshops, ISSN 2836-5348
    Nationell ämneskategori
    Annan data- och informationsvetenskap
    Identifikatorer
    urn:nbn:se:mau:diva-69978 (URN)10.1109/PerComWorkshops59983.2024.10503245 (DOI)001216220000036 ()2-s2.0-85192479973 (Scopus ID)979-8-3503-0436-7 (ISBN)979-8-3503-0437-4 (ISBN)
    Konferens
    IEEE International Conference on Pervasive Computing and Communications (PerCom), MAR 11-15, 2024, Biarritz, FRANCE
    Tillgänglig från: 2024-07-30 Skapad: 2024-07-30 Senast uppdaterad: 2024-10-30Bibliografiskt granskad
    5. Usability of a Mobile Application for Patients with Parkinson's Disease
    Öppna denna publikation i ny flik eller fönster >>Usability of a Mobile Application for Patients with Parkinson's Disease
    (Engelska)Manuskript (preprint) (Övrigt vetenskapligt)
    Identifikatorer
    urn:nbn:se:mau:diva-71852 (URN)
    Anmärkning

    Accepted for publication in the IEEE EMBC 2024 conference proceedings

    Tillgänglig från: 2024-10-30 Skapad: 2024-10-30 Senast uppdaterad: 2024-10-30Bibliografiskt granskad
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  • 6.
    O’Bryan, Mikaela
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Alvarez, Alberto
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Font, Jose
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Robinson, Raquel Breejon
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). IT University of Copenhagen.
    Decentering the Designer Through Live-Action Roleplay2024Ingår i: Proceedings of the Halfway to the Future Symposium, Association for Computing Machinery (ACM), 2024, artikel-id 37Konferensbidrag (Refereegranskat)
    Abstract [en]

    More-than-human approaches to HCI design research have gained traction in recent years, facilitating a need to employ new methods that decenter the human perspective. We propose live-action roleplay (larp), as a method and practice within HCI that can support perspective taking and empathy-building with non-humans actors. In this paper, we discuss a local community workshop where we facilitated numerous body-based and larp activities surrounding the theme of sustainability, and reflect on the strengths larp offers in service of decentering the human perspective.

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  • 7.
    Sarkheyli-Hägele, Arezoo
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Holmberg, Johan
    Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Hagele, Georg
    Husqvarna Grp, Autonomous Operat, Huskvarna, Sweden.
    Situation Awareness-based Evacuation Assistance System2024Ingår i: 2024 IEEE World Forum on Public Safety Technologies, WF-PST 2024, Institute of Electrical and Electronics Engineers (IEEE), 2024, s. 62-67Konferensbidrag (Refereegranskat)
    Abstract [en]

    Evacuating buildings during various emergencies, such as fire or terrorist attacks, requires high awareness of the environment, quick decision-making, and immediate action. In recent years, the research has increased regarding how to support this process and persons involved by dedicated technical means. However, still, a lot of questions remain unanswered. This contribution presents a concept and the first mobile application design as part of an intuitive evacuation assistance system for evacuation leaders to improve emergency situation awareness of individual leaders and the evacuation team. Using the proposed system, the leaders will get real-time support regarding environmental hazards, evacuation procedures, extra assistance requests, the number of people in the building or their area, etc., improving the overall situation awareness. This can be achieved by a proper sensory infrastructure, intelligent algorithms building an artificial situation awareness, and a user-focused interface design, as introduced in this contribution. The analytical discussion presented in this contribution points out the strength of the concept, and the first tests of sensory infrastructure presented show feasibility for real-world applications.

  • 8.
    Belfrage, Michael
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Frantz, Christopher
    Department of Computer Science, Norwegian University of Science and Technology.
    Fabris, Bertilla
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Lorig, Fabian
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Blueprinting Organ Donation: A ‘Policy-first’ Approach for Developing Agent-based Models2024Konferensbidrag (Refereegranskat)
    Abstract [en]

    Agent-based models have long been argued a useful toolto support policy analysis, variably targeting the assessment of policydesign, as well as establishing its performance. Challenging, however,remains appropriate empirical parameterization and validation of suchmodels. This paper contributes to the development of rigorous accountsof policy modelling primarily driven by policy documents in order to develop general conceptual model. Such models can then serve as a basis forearly validation by subject matter experts, but more importantly, informthe subsequent inquiry relevant for the parameterization of such models, while at the same time offering the opportunity to detect deviationsfrom regulated practice. Relying on the scenario of organ donation basedon the Swedish legislation, we explore the merits of such an approach,and sketch the individual steps from policy documents to conceptualmodel. Supporting the methodological process, this paper employs theInstitutional Grammar 2.0, which offers selected features supporting theproposed modelling approach.

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  • 9.
    Belfrage, Michael
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Lorig, Fabian
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Davidsson, Paul
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Simulating Change-A Systematic Literature Review of Agent-Based Models for Policy-Making2024Ingår i: Conference Proceedings: 2024 Annual Modeling and Simulation Conference(ANNSIM 2024), Society for Modeling and Simulation International (SCS) , 2024Konferensbidrag (Refereegranskat)
    Abstract [en]

    Social phenomena emerge from agent-environment interactions, rendering many statistical models unsuit-able. Agent-based Models (ABMs) offer a viable alternative for exploring policy implications. While recentcrises like the COVID-19 pandemic may have increased ABM awareness, their use in policy-making hasa long history. To better understand the potential challenges and opportunities of using ABMs to informpolicy-making, we conducted a systematic literature review and identified 34 articles describing the use ofABMs involving policymakers. This review revealed that ABMs have been implemented to support pol-icymakers across a range of policy areas, but also identified low levels of model traceability and formalcommunication. Moreover, the review showed that the model’s purpose and type tend to influence howvalidation is performed. The review concludes that models that have undergone little validation and lackproper documentation, while being informally communicated, may hinder policymakers from effectivelymotivating their decision-making.

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  • 10.
    Adewole, Kayode Sakariyah
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Jacobsson, Andreas
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    HOMEFUS: A Privacy and Security-Aware Model for IoT Data Fusion in Smart Connected Homes2024Ingår i: Proceedings of the 9th International Conference on Internet of Things, Big Data and Security IoTBDS: Volume 1, SciTePress, 2024, s. 133-140Konferensbidrag (Refereegranskat)
    Abstract [en]

    The benefit associated with the deployment of Internet of Things (IoT) technology is increasing daily. IoT has revolutionized our ways of life, especially when we consider its applications in smart connected homes. Smart devices at home enable the collection of data from multiple sensors for a range of applications and services. Nevertheless, the security and privacy issues associated with aggregating multiple sensors’ data in smart connected homes have not yet been sufficiently prioritized. Along this development, this paper proposes HOMEFUS, a privacy and security-aware model that leverages information theoretic correlation analysis and gradient boosting to fuse multiple sensors’ data at the edge nodes of smart connected homes. HOMEFUS employs federated learning, edge and cloud computing to reduce privacy leakage of sensitive data. To demonstrate its applicability, we show that the proposed model meets the requirements for efficient data fusion pipelines. The model guides practitio ners and researchers on how to setup secure smart connected homes that comply with privacy laws, regulations, and standards. 

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