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Publications (10 of 57) Show all publications
Rahal, M., Ahmed, B. S. & Samuelsson, J. (2024). Machine Learning Data Suitability and Performance Testing Using Fault Injection Testing Framework. In: Jan Kofroň, Tiziana Margaria, Cristina Seceleanu (Ed.), ECBS 2023: Engineering of Computer-Based Systems. Paper presented at 8th International Conference, ECBS, Västerås, Sweden, October 16–18, 2023. (pp. 42-59). Springer, 14390 LNCS
Open this publication in new window or tab >>Machine Learning Data Suitability and Performance Testing Using Fault Injection Testing Framework
2024 (English)In: ECBS 2023: Engineering of Computer-Based Systems / [ed] Jan Kofroň, Tiziana Margaria, Cristina Seceleanu, Springer, 2024, Vol. 14390 LNCS, p. 42-59Conference paper, Published paper (Refereed)
Abstract [en]

Creating resilient machine learning (ML) systems has become necessary to ensure production-ready ML systems that acquire user confidence seamlessly. The quality of the input data and the model highly influence the successful end-to-end testing in data-sensitive systems. However, the testing approaches of input data are not as systematic and are few compared to model testing. To address this gap, this paper presents the Fault Injection for Undesirable Learning in input Data (FIUL-Data) testing framework that tests the resilience of ML models to multiple intentionally-triggered data faults. Data mutators explore vulnerabilities of ML systems against the effects of different fault injections. The proposed framework is designed based on three main ideas: The mutators are not random; one data mutator is applied at an instance of time, and the selected ML models are optimized beforehand. This paper evaluates the FIUL-Data framework using data from analytical chemistry, comprising retention time measurements of anti-sense oligonucleotide. Empirical evaluation is carried out in a two-step process in which the responses of selected ML models to data mutation are analyzed individually and then compared with each other. The results show that the FIUL-Data framework allows the evaluation of the resilience of ML models. In most experiments cases, ML models show higher resilience at larger training datasets, where gradient boost performed better than support vector regression in smaller training sets. Overall, the mean squared error metric is useful in evaluating the resilience of models due to its higher sensitivity to data mutation. 

Place, publisher, year, edition, pages
Springer, 2024
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; LNCS, volume 14390
Keywords
Input output programs, Machine learning, Mean square error, Software testing, Chromatography data, Data mutation, Fault injection, Input datas, Machine learning models, Machine learning systems, Machine learning testing, Machine-learning, Mutation testing, Responsible AI, Oligonucleotides
National Category
Software Engineering Computer Sciences Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-97919 (URN)10.1007/978-3-031-49252-5_5 (DOI)2-s2.0-85180147228 (Scopus ID)978-3-031-49251-8 (ISBN)978-3-031-49252-5 (ISBN)
Conference
8th International Conference, ECBS, Västerås, Sweden, October 16–18, 2023.
Available from: 2024-01-04 Created: 2024-01-04 Last updated: 2024-01-04Bibliographically approved
Bayram, F. & Ahmed, B. S. (2023). A domain-region based evaluation of ML performance robustness to covariate shift. Neural Computing & Applications, 35(24), 17555-17577
Open this publication in new window or tab >>A domain-region based evaluation of ML performance robustness to covariate shift
2023 (English)In: Neural Computing & Applications, ISSN 0941-0643, E-ISSN 1433-3058, Vol. 35, no 24, p. 17555-17577Article in journal (Refereed) Published
Abstract [en]

Most machine learning methods assume that the input data distribution is the same in the training and testing phases.However, in practice, this stationarity is usually not met and the distribution of inputs differs, leading to unexpectedperformance of the learned model in deployment. The issue in which the training and test data inputs follow differentprobability distributions while the input–output relationship remains unchanged is referred to as covariate shift. In thispaper, the performance of conventional machine learning models was experimentally evaluated in the presence of covariateshift. Furthermore, a region-based evaluation was performed by decomposing the domain of probability density function ofthe input data to assess the classifier’s performance per domain region. Distributional changes were simulated in a twodimensional classification problem. Subsequently, a higher four-dimensional experiments were conducted. Based on theexperimental analysis, the Random Forests algorithm is the most robust classifier in the two-dimensional case, showing thelowest degradation rate for accuracy and F1-score metrics, with a range between 0.1% and 2.08%. Moreover, the resultsreveal that in higher-dimensional experiments, the performance of the models is predominantly influenced by the complexity of the classification function, leading to degradation rates exceeding 25% in most cases. It is also concluded that themodels exhibit high bias toward the region with high density in the input space domain of the training samples.

Place, publisher, year, edition, pages
Springer, 2023
Keywords
Degradation, Function evaluation, Input output programs, Machine learning, Probability distributions, Classifier evaluation, Concept drifts, Covariate shifts, Input datas, Machine-learning, Model degradations, Performance, Region-based, Robust machine learning, Two-dimensional, Probability density function
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-94905 (URN)10.1007/s00521-023-08622-w (DOI)000988193400003 ()2-s2.0-85159332578 (Scopus ID)
Funder
Knowledge Foundation, 20200067Karlstad University
Available from: 2023-05-29 Created: 2023-05-29 Last updated: 2023-12-05Bibliographically approved
Chahed, H., Usman, M., Chatterjee, A., Bayram, F., Chaudhary, R., Brunstrom, A., . . . Kassler, A. (2023). AIDA—Aholistic AI-driven networking and processing framework for industrial IoT applications. Internet of Things: Engineering Cyber Physical Human Systems, 22, Article ID 100805.
Open this publication in new window or tab >>AIDA—Aholistic AI-driven networking and processing framework for industrial IoT applications
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2023 (English)In: Internet of Things: Engineering Cyber Physical Human Systems, E-ISSN 2542-6605, Vol. 22, article id 100805Article in journal (Refereed) Published
Abstract [en]

Industry 4.0 is characterized by digitalized production facilities, where a large volume of sensors collect a vast amount of data that is used to increase the sustainability of the production by e.g. optimizing process parameters, reducing machine downtime and material waste, and the like. However, making intelligent data-driven decisions under timeliness constraints requires the integration of time-sensitive networks with reliable data ingestion and processing infrastructure with plug-in support of Machine Learning (ML) pipelines. However, such integration is difficult due to the lack of frameworks that flexibly integrate and program the networking and computing infrastructures, while allowing ML pipelines to ingest the collected data and make trustworthy decisions in real time. In this paper, we present AIDA - a novel holistic AI-driven network and processing framework for reliable data-driven real-time industrial IoT applications. AIDA manages and configures Time-Sensitive networks (TSN) to enable real-time data ingestion into an observable AI-powered edge/cloud continuum. Pluggable and trustworthy ML components that make timely decisions for various industrial IoT applications and the infrastructure itself are an intrinsic part of AIDA. We introduce the AIDA architecture, demonstrate the building blocks of our framework and illustrate it with two use cases. 

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
Edge/cloud computing, Internet of Things (IoT), Machine Learning, Time-Sensitive Networks (TSN)
National Category
Computer Engineering Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-94900 (URN)10.1016/j.iot.2023.100805 (DOI)001053228900001 ()2-s2.0-85159450974 (Scopus ID)
Funder
Knowledge Foundation, 20200067
Available from: 2023-05-29 Created: 2023-05-29 Last updated: 2024-02-07Bibliographically approved
Ma, Y., Younis, K., Ahmed, B. S., Kassler, A., Krakhmalev, P., Thore, A. & Lindback, H. (2023). Automated and Systematic Digital Twins Testing for Industrial Processes. In: Proceedings - 2023 IEEE 16th International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2023: . Paper presented at 16th IEEE International Conference on Software Testing, Verification and Validation Workshops, Dublin,Ireland, April 16-20, 2023. (pp. 149-158). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Automated and Systematic Digital Twins Testing for Industrial Processes
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2023 (English)In: Proceedings - 2023 IEEE 16th International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2023, Institute of Electrical and Electronics Engineers (IEEE), 2023, p. 149-158Conference paper, Published paper (Refereed)
Abstract [en]

Digital twins (DT) of industrial processes have become increasingly important. They aim to digitally represent the physical world to help evaluate, optimize, and predict physical processes and behaviors. Therefore, DT is a vital tool to improve production automation through digitalization and becomes more sophisticated due to rapidly evolving simulation and modeling capabilities, integration of IoT sensors with DT, and high-capacity cloud/edge computing infrastructure. However, the fidelity and reliability of DT software are essential to represent the physical world. This paper shows an automated and systematic test architecture for DT that correlates DT states with real-time sensor data from a production line in the forging industry. Our evaluation shows that the architecture can significantly accelerate the automatic DT testing process and improve its reliability. A systematic online DT testing method can significantly detect the performance shift and continuously improve the DT’s fidelity. The snapshot creation methodology and testing agent architecture can be an inspiration and can be generally applicable to other industrial processes that use DT to generalize their automated testing. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Keywords
Automation, E-learning, Industry 4.0, Reinforcement learning, Software reliability, Industrial processs, Machine-learning, Modelling capabilities, Physical behaviors, Physical process, Physical world, Production automation, Reinforcement learnings, Simulation and modeling, Software testings, Software testing
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-96056 (URN)10.1109/ICSTW58534.2023.00037 (DOI)2-s2.0-85163093915 (Scopus ID)979-8-3503-3335-0 (ISBN)
Conference
16th IEEE International Conference on Software Testing, Verification and Validation Workshops, Dublin,Ireland, April 16-20, 2023.
Funder
Knowledge FoundationVinnova
Available from: 2023-07-07 Created: 2023-07-07 Last updated: 2023-08-07Bibliographically approved
Bayram, F., Aupke, P., Ahmed, B. S., Kassler, A., Theocharis, A. & Forsman, J. (2023). DA-LSTM: A dynamic drift-adaptive learning framework for interval load forecasting with LSTM networks. Engineering applications of artificial intelligence, 123, Article ID 106480.
Open this publication in new window or tab >>DA-LSTM: A dynamic drift-adaptive learning framework for interval load forecasting with LSTM networks
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2023 (English)In: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 123, article id 106480Article in journal (Refereed) Published
Abstract [en]

Load forecasting is a crucial topic in energy management systems (EMS) due to its vital role in optimizing energy scheduling and enabling more flexible and intelligent power grid systems. As a result, these systems allow power utility companies to respond promptly to demands in the electricity market. Deep learning (DL) models have been commonly employed in load forecasting problems supported by adaptation mechanisms to cope with the changing pattern of consumption by customers, known as concept drift. A drift magnitude threshold should be defined to design change detection methods to identify drifts. While the drift magnitude in load forecasting problems can vary significantly over time, existing literature often assumes a fixed drift magnitude threshold, which should be dynamically adjusted rather than fixed during system evolution. To address this gap, in this paper, we propose a dynamic drift-adaptive Long Short-Term Memory (DA-LSTM) framework that can improve the performance of load forecasting models without requiring a drift threshold setting. We integrate several strategies into the framework based on active and passive adaptation approaches. To evaluate DA-LSTM in real-life settings, we thoroughly analyze the proposed framework and deploy it in a real-world problem through a cloud-based environment. Efficiency is evaluated in terms of the prediction performance of each approach and computational cost. The experiments show performance improvements on multiple evaluation metrics achieved by our framework compared to baseline methods from the literature. Finally, we present a trade-off analysis between prediction performance and computational costs.

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
Concept drift Change-point detection Dynamic drift adaptation Adaptive LSTM Interval load forecasting
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-95022 (URN)10.1016/j.engappai.2023.106480 (DOI)001013639800001 ()2-s2.0-85160615665 (Scopus ID)
Funder
Knowledge Foundation, 20200067Swedish Energy Agency, 50246-1SOLVE, 52693-1
Available from: 2023-06-02 Created: 2023-06-02 Last updated: 2024-03-13Bibliographically approved
Bayram, F., Ahmed, B. S., Hallin, E. & Engman, A. (2023). DQSOps: Data Quality Scoring Operations Framework for Data-Driven Applications. In: EASE '23: Proceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering: . Paper presented at The International Conference on Evaluation and Assessment in Software Engineering, Oulu, Finland, June 14-16, 2023. (pp. 32-41). Association for Computing Machinery (ACM)
Open this publication in new window or tab >>DQSOps: Data Quality Scoring Operations Framework for Data-Driven Applications
2023 (English)In: EASE '23: Proceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering, Association for Computing Machinery (ACM), 2023, p. 32-41Conference paper, Published paper (Refereed)
Abstract [en]

Data quality assessment has become a prominent component in the successful execution of complex data-driven artificial intelligence (AI) software systems. In practice, real-world applications generate huge volumes of data at speeds. These data streams require analysis and preprocessing before being permanently stored or used in a learning task. Therefore, significant attention has been paid to the systematic management and construction of high-quality datasets. Nevertheless, managing voluminous and high-velocity data streams is usually performed manually (i.e. offline), making it an impractical strategy in production environments. To address this challenge, DataOps has emerged to achieve life-cycle automation of data processes using DevOps principles. However, determining the data quality based on a fitness scale constitutes a complex task within the framework of DataOps. This paper presents a novel Data Quality Scoring Operations (DQSOps) framework that yields a quality score for production data in DataOps workflows. The framework incorporates two scoring approaches, an ML prediction-based approach that predicts the data quality score and a standard-based approach that periodically produces the ground-truth scores based on assessing several data quality dimensions. We deploy the DQSOps framework in a real-world industrial use case. The results show that DQSOps achieves significant computational speedup rates compared to the conventional approach of data quality scoring while maintaining high prediction performance.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2023
Keywords
Data reduction, Quality control, Automated data, Automated data scoring, Data assessment, Data quality; Data quality dimensions, Data stream, Data-driven applications, Dataops, Mutation testing, Real-world, Life cycle
National Category
Software Engineering Computer Sciences Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-95940 (URN)10.1145/3593434.3593445 (DOI)2-s2.0-85162259466 (Scopus ID)979-8-4007-0044-6 (ISBN)
Conference
The International Conference on Evaluation and Assessment in Software Engineering, Oulu, Finland, June 14-16, 2023.
Funder
Knowledge Foundation
Available from: 2023-07-04 Created: 2023-07-04 Last updated: 2023-07-04Bibliographically approved
Klima, M., Bures, M., Ahmed, B. S., Bellekens, X., Atkinson, R., Tachtatzis, C. & Herout, P. (2023). Specialized path-based technique to test Internet of Things system functionality under limited network connectivity. Internet of Things: Engineering Cyber Physical Human Systems, 22, Article ID 100706.
Open this publication in new window or tab >>Specialized path-based technique to test Internet of Things system functionality under limited network connectivity
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2023 (English)In: Internet of Things: Engineering Cyber Physical Human Systems, E-ISSN 2542-6605, Vol. 22, article id 100706Article in journal (Refereed) Published
Abstract [en]

Contemporary Internet-of-Things (IoT) systems are hindered by several reliability-related issues, especially, the dynamic behavior of IoT systems caused by limited and often unstable network connectivity. Several intuitive ad-hoc approaches can be employed to test this behavior; however, the effectiveness of these approaches in detecting defects and their overall testing costs remain questionable. Therefore, we present a new specialized path-based technique to test the processes of an IoT system in scenarios wherein parts of these processes are influenced by limited or disrupted network connectivity. The proposed technique can be scaled using two levels of test coverage criteria to determine the strengths of the test cases. For this purpose, we propose two algorithms for generating test cases to implement the technique: an ant colony optimization-based search and a graph-traversal-based test case composition. We compared the efficiency of the proposed approach with possible solutions obtained using a standard path-based testing approach based on prime paths computed by a set-covering algorithm. We consider the total number of test case steps as the main proxy for test effort in experiments employing 150 problem models. For the less intensive of the two used test-coverage criteria, EachBorderOnce, an ant colony optimization-based algorithm, produced test sets with the same averaged number of steps as the graph traversal-based test-case composition; however, this algorithm performed with averaged number of steps 10% lower than a prime paths-based algorithm. For the more intensive test coverage criterion, AllBorderCombinations, these differences favoring the ant colony optimization-based algorithm were 18% and 25%, respectively. For these two types of defined test coverage criteria, the ant colony optimization-based search, graph-traversal-based algorithm, and standard path-based testing approach based on prime paths achieved the best results for 93 and 78, 14 and 24, and 13 and 17 models for AllBorderCombinations and EachBorderOnce criterion, respectively. Therefore, to guarantee the best test set, all compared algorithms are combined in a portfolio strategy that yields the best results based on the potential of the produced test sets to detect simulated defects caused by limited network connectivity. Additionally, this portfolio strategy also yields test sets, implying the lowest test effort for experimental problem instances. 

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
Internet of Things, Limited network connectivity, Model-based testing, Path-based testing, Reliability, Test automation, Test case generation
National Category
Computer and Information Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-93965 (URN)10.1016/j.iot.2023.100706 (DOI)001004574800001 ()2-s2.0-85149070767 (Scopus ID)
Available from: 2023-03-20 Created: 2023-03-20 Last updated: 2023-06-30Bibliographically approved
Bayram, F., Ahmed, B. S., Hallin, E. & Engman, A. (2022). A Drift Handling Approach for Self-Adaptive ML Software in Scalable Industrial Processes. In: Mario Aehnelt and Thomas Kirste (Ed.), Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering: . Paper presented at 37th IEEE/ACM International Conference on Automated Software Engineering, Rochester, USA, October 10-14, 2022. (pp. 1-5). Association for Computing Machinery (ACM), Article ID 129.
Open this publication in new window or tab >>A Drift Handling Approach for Self-Adaptive ML Software in Scalable Industrial Processes
2022 (English)In: Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering / [ed] Mario Aehnelt and Thomas Kirste, Association for Computing Machinery (ACM), 2022, p. 1-5, article id 129Conference paper, Published paper (Refereed)
Abstract [en]

Most industrial processes in real-world manufacturing applications are characterized by the scalability property, which requires an automated strategy to self-adapt machine learning (ML) software systems to the new conditions. In this paper, we investigate an Electroslag Remelting (ESR) use case process from the Uddeholms AB steel company. The use case involves predicting the minimum pressure value for a vacuum pumping event. Taking into account the long time required to collect new records and efficiently integrate the new machines with the built ML software system. Additionally, to accommodate the changes and satisfy the non-functional requirement of the software system, namely adaptability, we propose an automated and adaptive approach based on a drift handling technique called importance weighting. The aim is to address the problem of adding a new furnace to production and enable the adaptability attribute of the ML software. The overall results demonstrate the improvements in ML software performance achieved by implementing the proposed approach over the classical non-adaptive approach. 

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2022
Keywords
Application programs, Automation, Electroslag remelting, Adaptive approach, Automated industrial process, Changing environment, Concept drifts, Electroslag remelting, Industrial processs, Machine learning software, Machine learning software adaptability, Software adaptability, Software-systems, Remelting
National Category
Software Engineering
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-93618 (URN)10.1145/3551349.3559495 (DOI)2-s2.0-85146949434 (Scopus ID)978-1-4503-9475-8 (ISBN)
Conference
37th IEEE/ACM International Conference on Automated Software Engineering, Rochester, USA, October 10-14, 2022.
Funder
Knowledge Foundation, 20200067
Available from: 2023-02-13 Created: 2023-02-13 Last updated: 2023-06-22Bibliographically approved
Bayram, F., Ahmed, B. S. & Kassler, A. (2022). From concept drift to model degradation: An overview on performance-aware drift detectors. Knowledge-Based Systems, 245, Article ID 108632.
Open this publication in new window or tab >>From concept drift to model degradation: An overview on performance-aware drift detectors
2022 (English)In: Knowledge-Based Systems, ISSN 0950-7051, E-ISSN 1872-7409, Vol. 245, article id 108632Article, review/survey (Refereed) [Artistic work] Published
Abstract [en]

The dynamicity of real-world systems poses a significant challenge to deployed predictive machine learning (ML) models. Changes in the system on which the ML model has been trained may lead to performance degradation during the system’s life cycle. Recent advances that study non-stationary environments have mainly focused on identifying and addressing such changes caused by a phenomenon called concept drift. Different terms have been used in the literature to refer to the same type of concept drift and the same term for various types. This lack of unified terminology is set out to create confusion on distinguishing between different concept drift variants. In this paper, we start by grouping concept drift types by their mathematical definitions and survey the different terms used in the literature to build a consolidated taxonomy of the field. We also review and classify performance-based concept drift detection methods proposed in the last decade. These methods utilize the predictive model’s performance degradation to signal substantial changes in the systems. The classification is outlined in a hierarchical diagram to provide an orderly navigation between the methods. We present a comprehensive analysis of the main attributes and strategies for tracking and evaluating the model’s performance in the predictive system. The paper concludes by discussing open research challenges and possible research directions.

National Category
Computer and Information Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-89786 (URN)10.1016/j.knosys.2022.108632 (DOI)000795145800005 ()2-s2.0-85127829086 (Scopus ID)
Projects
AIDA
Available from: 2022-05-16 Created: 2022-05-16 Last updated: 2023-06-22Bibliographically approved
Chatterjee, A. & Ahmed, B. S. (2022). IoT anomaly detection methods and applications: A survey. Internet of Things: Engineering Cyber Physical Human Systems, 19, Article ID 100568.
Open this publication in new window or tab >>IoT anomaly detection methods and applications: A survey
2022 (English)In: Internet of Things: Engineering Cyber Physical Human Systems, E-ISSN 2542-6605, Vol. 19, article id 100568Article, review/survey (Refereed) Published
Abstract [en]

Ongoing research on anomaly detection for the Internet of Things (IoT) is a rapidly expanding field. This growth necessitates an examination of application trends and current gaps. The vast majority of those publications are in areas such as network and infrastructure security, sensor monitoring, smart home, and smart city applications and are extending into even more sectors. Recent advancements in the field have increased the necessity to study the many IoT anomaly detection applications. This paper begins with a summary of the detection methods and applications, accompanied by a discussion of the categorization of IoT anomaly detection algorithms. We then discuss the current publications to identify distinct application domains, examining papers chosen based on our search criteria. The survey considers 64 papers among recent publications published between January 2019 and July 2021. In recent publications, we observed a shortage of IoT anomaly detection methodologies, for example, when dealing with the integration of systems with various sensors, data and concept drifts, and data augmentation where there is a shortage of Ground Truth data. Finally, we discuss the present such challenges and offer new perspectives where further research is required.

Place, publisher, year, edition, pages
Elsevier, 2022
Keywords
Anomaly detection, Internet of Things, IoT, Review, Survey, Applications
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-91577 (URN)10.1016/j.iot.2022.100568 (DOI)000834078100004 ()2-s2.0-85134350982 (Scopus ID)
Funder
Knowledge Foundation, 20200067
Available from: 2022-08-24 Created: 2022-08-24 Last updated: 2022-12-01Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-9051-7609

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