Øystein Haugen

• Haugen, Øystein; Andersen, Martin Arthur; Klikovits, Stefan; Bordeleau, Francis; Mertens, Joost & Denil, Joachim (2026). Evolving the DarTwin evolution language. Innovations in Systems and Software Engineering. ISSN 1614-5046. 23(1). doi: 10.1007/s11334-026-00647-3. Full text in Research Archive
• Haugen, Øystein; Klikovits, Stefan; Andersen, Martin Arthur; Beaulieu, Jonathan; Bordeleau, Francis & Denil, Joachim [Show all 7 contributors for this article] (2025). DarTwin made precise by SysMLv2 – An Experiment. In Kessentini, Marouane; Sahraoui, Houari & Ali, Shaukat (Ed.), 2025 ACM/IEEE 28th International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798331579906. p. 322–332. doi: 10.1109/models-c68889.2025.00051. Full text in Research Archive Show summary

  The new SysMLv2 adds mechanisms for the built-in specification of domain-specific concepts and language extensions. This feature promises to facilitate the creation of Domain-Specific Languages (DSLs) and interfacing with existing system descriptions and technical designs. In this paper, we review these features and evaluate SysMLv2’s capabilities using concrete use cases. We develop DarTwin DSL, a DSL that formalizes the existing DarTwin notation for Digital Twin (DT) evolution, through SysMLv2, thereby supposedly enabling the wide application of DarTwin’s evolution templates using any SysMLv2 tool. We demonstrate DarTwin DSL, but also point out limitations in the currently available tooling of SysMLv2 in terms of graphical notation capabilities. This work contributes to the growing field of Model-Driven Engineering (MDE) for DTs and combines it with the release of SysMLv2, thus integrating a systematic approach with DT evolution management in systems engineering.

• Alskaif, Tarek; Babur, Önder; Bordeleau, Francis; Cleophas, Loek; Combemale, Benoit & Denil, Joachim [Show all 12 contributors for this article] (2025). Evolution at the Core of Digital Twin Engineering. In Kessentini, Marouane; Sahraoui, Houari & Ali, Shaukat (Ed.), 2025 ACM/IEEE 28th International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798331579906. p. 210–216. doi: 10.1109/models-c68889.2025.00119. Full text in Research Archive Show summary

  Engineering Digital Twins (EDT) presents a multifaceted challenge that extends beyond managing the lifecycle of a Digital Twin (DT) to include its continuous, dynamic interaction with the lifecycle of the actual object, system, or process it represents, referred to as the Actual Twin (AT). The relationship between the lifecycles of DT and AT necessitates a rethinking of the software development lifecycle of DTs. This vision paper examines the deeply intertwined lifecycles of DT and AT, arguing that effective methods for EDT must embrace the mutual and adaptive evolution of both over time. We propose placing evolution at the core of EDT. We identify key triggers of DT evolution, examine the engineering dimensions involved, and explore how the best practices, technologies, and tools of DevOps can support this evolution. Finally, we discuss current challenges and opportunities in the field. This paper serves as a call to action for the EDT community to adopt evolution as a crucial factor and core principle in EDT.

• Nguyen, Hoa Thi; Haugen, Øystein & Johnsen, Einar Broch (2025). Augmentor: a cognitive-inspired approach for context-aware adaptive learning with human guidance. Journal of Intelligent Manufacturing. ISSN 0956-5515. doi: 10.1007/s10845-025-02712-3. Full text in Research Archive Show summary

  Automation in complex industrial processes faces significant challenges due to its heavy reliance on human knowledge and tacit expertise. This knowledge is typically acquired through years of on-site training, making the transition to automation difficult. While data-driven approaches offer potential solutions by building automation models from expert observations, they often fall short of capturing deeper human understanding and contextual reasoning. In this paper, we introduce Augmentor, a cognitive-inspired framework for context-aware adaptive learning. The framework bridges reactive learning, which continuously updates models based on incoming data, with context-aware adaptive learning, which incorporates external context and expert knowledge for higher-order reasoning and decision-making. Inspired by theories of consciousness, Augmentor leverages autonomic computing principles through the MAPE-K model and semantic web technologies to create a global workspace for managing knowledge and guiding adaptive learning processes. The approach addresses two key objectives: continuously refining models through reactive learning and enabling experts to inject contextual knowledge to guide the learning process. Human-Gated Data Aggregation is used for data-driven updates, while context-aware adaptive learning ensures the system dynamically adjusts to complex scenarios and evolving industrial conditions. We validate Augmentor through a real-world use case in collaboration with our industrial partner, Mekanisk Service Halden, which specialises in various mechanical services. Experimental results show significant performance improvements across three strategies–static model without relearning, reactive learning, and context-aware adaptive learning–demonstrating the advantages of incorporating context-aware strategies to achieve more robust and adaptive control.

• Nguyen, Hoa Thi; Olsson, Roland & Haugen, Øystein (2024). Automatic Synthesis of Recurrent Neurons for Imitation Learning From CNC Machine Operators. IEEE Open Journal of the Industrial Electronics Society (OJ-IES). 5, p. 91–108. doi: 10.1109/OJIES.2024.3363500. Full text in Research Archive Show summary

  Analyzing time series data in industrial settings demands domain knowledge and computer science expertise to develop effective algorithms. AutoML approaches aim to automate this process, reducing human bias and improving accuracy and cost-effectiveness. This article applies an evolutionary algorithm to synthesize recurrent neurons optimized for specific datasets. This adds another layer to the AutoML framework, targeting the internal structure of neurons. We developed an imitation learning control system for an industry CNC machine to enhance operators' productivity. We specifically examine two recorded operator actions: adjusting the engagement rates for linear feed rate and spindle velocity. We compare the performance of our evolved neurons with support vector machine and four well-established neural network models commonly used for time series data: simple recurrent neural networks, long-short-term-memory, independently recurrent neural networks, and transformers. The results demonstrate that the neurons evolved via the evolutionary approach exhibit lower syntactic complexity than LSTMs and achieve lower error rates than other networks. They yield error rates 270% lower for the first operation action, while the error rates are 20% lower for the second action. We also show that our evolutionary algorithm is capable of creating skip-connections and gating mechanisms adapted to the specific characteristics of our dataset.

• Mertens, Joost; Klikovits, Stefan; Bordeleau, Francis; Denil, Joachim & Haugen, Øystein (2024). Continuous Evolution of Digital Twins using the DarTwin Notation. Software and Systems Modeling (SoSyM). ISSN 1619-1366. doi: 10.1007/s10270-024-01216-7. Full text in Research Archive Show summary

  Despite best efforts, various challenges remain in the creation and maintenance processes of digital twins (DTs). One of those primary challenges is the constant, continuous and omnipresent evolution of systems, their user’s needs and their environment, demanding the adaptation of the developed DT systems. DTs are developed for a specific purpose, which generally entails the monitoring, analysis, simulation or optimisation of a specific aspect of an actual system, referred to as the actual twin (AT). As such, when the twin system changes, that is either the AT itself changes, or the scope/purpose of a DT is modified, the DTs usually evolve in close synchronicity with the AT. As DTs are software systems, the best practices or methodologies for software evolution can be leveraged. This paper tackles the challenge of maintaining a (set of) DT(s) throughout the evolution of the user’s requirements and priorities and tries to understand how this evolution takes place. In doing so, we provide two contributions: (i) we develop DarTwin, a visual notation form that enables reasoning on a twin system, its purposes, properties and implementation, and (ii) we introduce a set of architectural transformations that describe the evolution of DT systems. The development of these transformations is driven and illustrated by the evolution and transformations of a family home’s DT, whose purpose is expanded, changed and re-prioritised throughout its ongoing lifecycle. Additionally, we evaluate the transformations on a laboratory-scale gantry crane’s DT.

• Nguyen, Hoa Thi; Haugen, Øystein & Olsson, Roland (2023). Imitation Learning from Operator Experiences for a Real time CNC Machine Controller. IEEE Conference on Industrial Informatics. ISSN 1935-4576. doi: 10.1109/INDIN51400.2023.10218164. Full text in Research Archive Show summary

  Controlling complex industrial systems can be a challenging task as it requires extensive knowledge and skills that are usually acquired through years of experience. This makes it difficult to program such expertise into machine algorithms. In this paper, we present a use case that demonstrates how we built control algorithms for a CNC machine using historical logging of observations from experts. With the advent of digital technologies, machining parts are now controlled by computer programs that offer high precision and speed. However, unforeseen scenarios can still arise, which demand operators’ attention and intervention, even with finely crafted machine programs. For our experiment, we collected data from a 5-axis Mazak Integrex i500-series CNC machine over a month manufacturing multiple instances of the same part. We collected observational states, which are sensor data that match the information operators receive and output engagement feed rates following the operator’s trajectories. Using behavioral cloning, we built an initial control policy from this data, testing three families of machine learning models: regression models, ensemble methods, and deep neural networks. The results showed that ensemble methods outperformed the baseline model significantly, proving that they have learned useful control patterns. The policies also demonstrated that ML models could eliminate noisy behaviors from operators’ actions. We believe that with interactive demonstrations in the future, these models have the potential to fully mature. Overall, our study demonstrates the feasibility of building control algorithms for complex industrial systems using historical expert demonstrations and machine learning techniques.

• Dinh, Nga & Haugen, Øystein (2022). Charging Scheduling Algorithm for Wireless-Powered Communication Networks, IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781665480253. doi: 10.1109/IECON49645.2022.9968357. Full text in Research Archive
• Lam, An Ngoc; Haugen, Øystein & Delsing, Jerker (2022). Dynamical Orchestration and Configuration Services in Industrial IoT Systems: An Autonomic Approach. IEEE Open Journal of the Industrial Electronics Society (OJ-IES). 3, p. 128–145. doi: 10.1109/OJIES.2022.3149093. Full text in Research Archive Show summary

  The Industrial Internet of Things (IIoT) enables the integration of physical devices such as sensors and actuators into the virtual world of automation application systems via different communication protocols. Interoperability among the “things” appears to be one of the biggest conceptual and technological challenges in developing the IIoT framework. Typically, collaboration at the field device level is very limited. Instead, the decision-making process is usually propagated to higher levels with substantial computational resources. This centralized architecture has been widely deployed based on global cloud infrastructure. However, sending data over the cloud for analysis may bring about privacy and security threats. Besides, network latency could be another factor that reduces adaptability. In this article, we propose a decentralized approach that applies the concepts of local automation cloud. By using semantic technologies to achieve autonomicity, the approach enables real-time monitoring of the control systems within one local cloud and automates orchestration and configuration locally through adaptation based on semantic policies. The approach is deployed and tested on a chemical production use case in which business-level policies have been used for dynamical planning for suppliers and automatic detection of malfunctioning sensors with subsequent adaptation to continuing supply planning and production as smooth as possible.

• Nguyen, Hoa Thi & Haugen, Øystein (2022). Building Experimental Laboratory for Digital Twin in Service Oriented Architecture, 2022 IEEE 5th International Conference on Industrial Cyber-Physical Systems - ICPS. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781665497701. doi: 10.1109/ICPS51978.2022.9816926. Full text in Research Archive Show summary

  The concept of Digital Twins is still in an infant state of development. Digital Twins are often built as a tool to aid in better understanding of physical systems through simulation. They can be used to visualize information during operations and provide instructions during training or execution of procedures. The use of Digital Twins to test is appealing as it can be done quickly and safely. However, testing without inclusion of the physical system can lead to a reality gap. The reality gap can lead to high risks when applying concepts tested on digital Twins to the physical system directly. Sometimes interaction with the physical system is unfeasible. In this paper, we present an experimental laboratory that we built to provide a platform for the development of high quality Digital Twins through a feedback loop. The physical system is a Palfinger crane. Our replicate physical twin is a Universal collaborative industrial robot model UR16e due to its similar anatomy to the crane. The RoboDK simulation software was used to rapidly develop a digital twin of the UR16e. We demonstrate a solution to the interoperability problem in digital Twins using the monitoring adaptation loop from the Autonomic Adaptation System of the Arrowhead Framework.

• Delsing, Jerker; Kulcsár, Géza & Haugen, Øystein (2022). SysML modeling of service-oriented system-of-systems. Innovations in Systems and Software Engineering. ISSN 1614-5046. doi: 10.1007/s11334-022-00455-5. Full text in Research Archive Show summary

  The success of the ongoing fourth industrial revolution largely depends on our ways to cope with the novel design challenges arising from a combination of an enormous increase in process and product complexity, as well as the expected autonomy and self-organization of complex and diverse industrial hardware–software installments, often called systems-of-systems. In this paper, we employ the service-oriented architectural paradigm, as materialized in the Eclipse Arrowhead framework, to represent modern systems engineering principles and their open structural principles and, thus, relevance to flexible and adaptive systems. As for adequately capturing the structural aspect, we propose using model-based engineering techniques and, in particular, a SysML-based specialization of systems modeling. The approach is illustrated by a real-life use-case in industrial automation.

• Arcega, Lorena; Font, Jaime; Haugen, Øystein & Cetina, Carlos (2022). Bug Localization in Model-Based Systems in the Wild. ACM Transactions on Software Engineering and Methodology. ISSN 1049-331X. 31(1), p. 10:1–10:32. doi: 10.1145/3472616. Full text in Research Archive
• Shimbara, Daisuke; Saeki, Motoshi; Hayashi, Shinpei & Haugen, Øystein (2021). Handling Quantity in Variability Models for System-of-Systems. International journal of software engineering and knowledge engineering. ISSN 0218-1940. 31(5), p. 693–724. doi: 10.1142/S0218194021500200. Full text in Research Archive
• Dinh, Nga; Tveito, Øystein & Haugen, Øystein (2021). Performance Evaluation for Energy Harvesting (EH)-based Sensor Nodes Under LoRaWAN Connectivity. In Bahk, Saewoong; Tran-Gia, Phuoc; Spiegel, Jan Van der & Quynh, Nguyen Xuan (Ed.), 2020 IEEE Eighth International Conference on Communications and Electronics (ICCE). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781728154718. p. 64–69. doi: 10.1109/ICCE48956.2021.9352044. Full text in Research Archive Show summary

  Sustainable operation is one of the key challenges for Internet of Thing (IoT) sensor nodes because they are usually powered by limited battery power. Many IoT applications require the sensor nodes to operate reliably and consistently for a long period of time. Approaches to prolong the lifespan of sensor nodes include sleep-mode mechanism, harvesting energy from the environment (such as light, wind, water mills...) and from a dedicated power supply. Energy harvesting (EH) from nature has emerged as especially promising because it is clean, free and does not interrupt operation of the nodes. Long-Range Wide Area Network (LoRaWAN) can provide low-power, low-cost, and long-range communication for IoT nodes. In LoRaWAN, a node can extend its lifetime by using a deep-sleep mechanism. Even though the lifetime can be extended by the deep-sleep mechanism, it is expected to further extend the lifespan to reduce or eliminate the replacement of outage devices. Due to the potentials of LoRaWAN for IoT and the importance of EH approach to provide sustainable operation for IoT nodes, this paper focuses on using both EH and the deep-sleep mechanism in LoRaWAN to significantly prolong the lifetime of sensor nodes. In particular, the paper proposes a new power metric, called power gradient, to evaluate power performance of the network. The paper also estimates the performance of EH-based sensor nodes in terms of power gradient and lifetime. The proposed metric provides some insights on parameters configuration to achieve the desirable lifetime of EH-based nodes under LoRaWAN.

• Lam, An Ngoc; Haugen, Øystein & Delsing, Jerker (2021). Interoperability for Industrial Internet of Things Based on Service-oriented Architecture, IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781665435543. doi: 10.1109/IECON48115.2021.9589102. Full text in Research Archive Show summary

  The new Industry 4.0 envisions a future for agile and effective integration of the physical operational technologies (OT) and the cyber information technologies (IT) as well as autonomous cooperation among them. However, the wide variety and heterogeneity of industrial systems and field devices -especially on the factory floor - increase integration complexity. To address these challenges, new technologies and concepts such as the Industrial Internet of Things (IIoT), Service-oriented Architecture (SoA), Semantic Technologies, Machine Learning and Artificial Intelligence are being introduced to the industrial environment. In this paper, we focus on how industrial automation systems and field devices can be integrated into the IIoT framework and coordinated to adapt to dynamic operating environment. Specifically, this paper proposed an interoperability solution that makes use of SoA and Semantic Technologies to achieve supervised coordination of IIoT application systems. To illustrate the potential of this approach, the Service-oriented Architecture-based Arrowhead Framework is used as the fundamental framework for the implementation of the approach.

• Shahraki, Amin; Taherkordi, Amirhosein & Haugen, Øystein (2021). TONTA: Trend-based Online Network Traffic Analysis in ad-hoc IoT networks. Computer Networks. ISSN 1389-1286. 194. doi: 10.1016/j.comnet.2021.108125. Full text in Research Archive Show summary

  Internet of Things (IoT) refers to a system of interconnected heterogeneous smart devices communicatingwithout human intervention. A significant portion of existing IoT networks is under the umbrella of ad-hoc andquasi ad-hoc networks. Ad-hoc based IoT networks suffer from the lack of resource-rich network infrastructuresthat are able to perform heavyweight network management tasks using, e.g. machine learning-based NetworkTraffic Monitoring and Analysis (NTMA) techniques. Designing light-weight NTMA techniques that do notneed to be (re-) trained has received much attention due to the time complexity of the training phase. In thisstudy, a novel pattern recognition method, called Trend-based Online Network Traffic Analysis (TONTA), isproposed for ad-hoc IoT networks to monitor network performance. The proposed method uses a statisticallight-weight Trend Change Detection (TCD) method in an online manner. TONTA discovers predominant trendsand recognizes abrupt or gradual time-series dataset changes to analyze the IoT network traffic. TONTA isthen compared with RuLSIF as an offline benchmark TCD technique. The results show that TONTA detectsapproximately 60% less false positive alarms than RuLSIF.

• Font, Jaime; Arcega, Lorena; Haugen, Øystein & Cetina, Carlos (2021). Handling nonconforming individuals in search-based model-driven engineering: nine generic strategies for feature location in the modeling space of the meta-object facility. Software and Systems Modeling (SoSyM). ISSN 1619-1366. 20(5), p. 1653–1688. doi: 10.1007/s10270-021-00870-5. Full text in Research Archive
• Ceja, Enrique Alejandro Garcia; Hugo, Åsmund Pedersen; Morin, Brice; Hansen, Per Olav; Martinsen, Espen & Lam, An Ngoc [Show all 7 contributors for this article] (2020). A Feature Importance Analysis for Soft-Sensing-Based Predictions in a Chemical Sulphonation Process, Proceedings of 2020 IEEE Conference on Industrial Cyberphysical Systems (ICPS). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781728163895. p. 62–66. doi: 10.1109/ICPS48405.2020.9274769. Full text in Research Archive Show summary

  In this paper we present the results of a feature importance analysis of a chemical sulphonation process. The task consists of predicting the neutralization number (NT), which is a metric that characterizes the product quality of active detergents. The prediction is based on a dataset of environmental measurements, sampled from an industrial chemical process. We used a soft-sensing approach, that is, predicting a variable of interest based on other process variables, instead of directly sensing the variable of interest. Reasons for doing so range from expensive sensory hardware to harsh environments, e.g., inside a chemical reactor. The aim of this study was to explore and detect which variables are the most relevant for predicting product quality, and to what degree of precision. We trained regression models based on linear regression, regression tree and random forest. A random forest model was used to rank the predictor variables by importance. Then, we trained the models in a forward-selection style by adding one feature at a time, starting with the most important one. Our results show that it is sufficient to use the top 3 important variables, out of the 8 variables, to achieve satisfactory prediction results. On the other hand, Random Forest obtained the best result when trained with all variables.

• Shahraki, Amin; Geitle, Marius & Haugen, Øystein (2020). A comparative node evaluation model for highly heterogeneous massive‐scale Internet of Things‐Mist networks. Transactions on Emerging Telecommunications Technologies. ISSN 1124-318X. 31(12), p. 1–28. doi: 10.1002/ett.3924. Full text in Research Archive Show summary

  Internet of Things (IoT) is a new technology that is driving the connection of billions of devices around the world. Because these devices are often resource‐constrained and very heterogeneous, this presents unique challenges. To address some of these challenges, new paradigms of Edge and Fog are emerging to bring computational resources of the IoT networks from remote devices like cloud closer to the end‐devices. Mist computing is a new paradigm that attempts to make use of the more resource‐rich nodes that are closer than Edge nodes to end‐users. Since these nodes might have enough resources to host services, execute tasks or even run containers, the utilization of network resources might be improved, and delay reduced by utilizing these nodes. The nodes must, therefore, be assessed to determine which nodes should offer resources to other nodes based on their situation. In this article, a new comparative assessment model for ranking Mist nodes in highly heterogeneous massive‐scale IoT networks in order to discover nodes that can offer their resources is proposed. The Mist nodes are evaluated based on parameters like resources, connections, applications, and environmental parameters to heuristically compare the neighbors with a novel learning‐to‐rank method to predict a suitability score for each node. The most suitable neighbor is then selected based on the score, with load balancing accomplished by a second chance method. When evaluating the performance, the results show that the proposed method succeeds in identifying resource‐rich nodes, while considering the selection of other nodes.

• Dinh, Nga & Haugen, Øystein (2020). Analysis and Power Scheduling Algorithm in Wireless Powered Communication Networks, 16th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob 2020). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781728197227. doi: 10.1109/WiMob50308.2020.9253379. Full text in Research Archive
• Shahraki, Amin; Taherkordi, Amirhosein; Haugen, Øystein & Eliassen, Frank (2020). A Survey and Future Directions on Clustering: From WSNs to IoT and Modern Networking Paradigms. IEEE Transactions on Network and Service Management. 18(2). doi: 10.1109/TNSM.2020.3035315. Full text in Research Archive Show summary

  Many Internet of Things (IoT) networks are created as an overlay over traditional ad-hoc networks such as Zigbee. Moreover, IoT networks can resemble ad-hoc networks over networks that support device-to-device (D2D) communication, e.g., D2D-enabled cellular networks and WiFi-Direct. In these ad-hoc types of IoT networks, efficient topology management is a crucial requirement, and in particular in massive scale deployments. Traditionally, clustering has been recognized as a common approach for topology management in ad-hoc networks, e.g., in Wireless Sensor Networks (WSNs). Topology management in WSNs and ad-hoc IoT networks has many design commonalities as both need to transfer data to the destination hop by hop. Thus, WSN clustering techniques can presumably be applied for topology management in ad-hoc IoT networks. This requires a comprehensive study on WSN clustering techniques and investigating their applicability to ad-hoc IoT networks. In this article, we conduct a survey of this field based on the objectives for clustering, such as reducing energy consumption and load balancing, as well as the network properties relevant for efficient clustering in IoT, such as network heterogeneity and mobility. Beyond that, we investigate the advantages and challenges of clustering when IoT is integrated with modern computing and communication technologies such as Blockchain, Fog/Edge computing, and 5G. This survey provides useful insights into research on IoT clustering, allows broader understanding of its design challenges for IoT networks, and sheds light on its future applications in modern technologies integrated with IoT.

• Shahraki, Amin; Abbasi, Mahmoud & Haugen, Øystein (2020). Boosting algorithms for network intrusion detection: A comparative evaluation of Real AdaBoost, Gentle AdaBoost and Modest AdaBoost. Engineering Applications of Artificial Intelligence. ISSN 0952-1976. 94. doi: 10.1016/j.engappai.2020.103770. Full text in Research Archive
• Shahraki, Amin; Taherkordi, Amirhosein; Haugen, Øystein & Eliassen, Frank (2020). Clustering objectives in wireless sensor networks: A survey and research direction analysis. Computer Networks. ISSN 1389-1286. 180. doi: 10.1016/j.comnet.2020.107376. Full text in Research Archive Show summary

  Wireless Sensor Networks (WSNs) typically include thousands of resource-constrained sensors to monitor their surroundings, collect data, and transfer it to remote servers for further processing. Although WSNs are considered highly flexible ad-hoc networks, network management has been a fundamental challenge in these types of net- works given the deployment size and the associated quality concerns such as resource management, scalability, and reliability. Topology management is considered a viable technique to address these concerns. Clustering is the most well-known topology management method in WSNs, grouping nodes to manage them and/or executing various tasks in a distributed manner, such as resource management. Although clustering techniques are mainly known to improve energy consumption, there are various quality-driven objectives that can be realized through clustering. In this paper, we review comprehensively existing WSN clustering techniques, their objectives and the network properties supported by those techniques. After refining more than 500 clustering techniques, we extract about 215 of them as the most important ones, which we further review, catergorize and classify based on clustering objectives and also the network properties such as mobility and heterogeneity. In addition, statistics are provided based on the chosen metrics, providing highly useful insights into the design of clustering techniques in WSNs.

• Shahraki, Amin; Geitle, Marius & Haugen, Øystein (2019). A distributed Fog node assessment model by using Fuzzy rules learned by XGBoost. In Perković, Toni (Eds.), 2019 4thInternational Conference on Smart and Sustainable Technologies(SpliTech). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9789532900910. doi: 10.23919/SpliTech.2019.8783016. Full text in Research Archive
• Lam, An Ngoc & Haugen, Øystein (2019). Applying semantics into Service-oriented IoT Framework, The 17th IEEE International Conference on Industrial Informatics (IEEE-INDIN 2019): Proceedings. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781728129273. p. 206–213. doi: 10.1109/INDIN41052.2019.8972295. Full text in Research Archive Show summary

  Introducing semantics into the Internet of Things (IoT) has been attracting increasing attention from researchers and industrial practitioners. Semantic technologies have been used to enable interoperability as well as deal with the heterogeneity, massive scale, and dynamic nature of IoT resources. With the emergence of Industry 4.0, semantic technologies arise as a potential approach toward information modeling and dynamic reconfiguration of highly complex automation systems with high diversity of domains, protocols, tools or hardware platforms. Applying semantics into existing IoT frameworks requires a thorough understanding of the framework architectures as well as careful considerations of different semantic technologies. To support this process, we survey the literature on the contributions and usage of semantics in IoT. We find that semantics are mainly used to handle interoperable systems and heterogeneous standards. In this paper, we also propose procedures for applying semantics into IoT frameworks. Further, we present our idea of using semantics to enable dynamical orchestration of services within the Arrowhead Framework - an IoT framework that supports the development of industrial automation systems.

• Lam, An Ngoc & Haugen, Øystein (2019). Implementing OPC-UA services for Industrial Cyber-Physical Systems in Service-Oriented Architecture, Proceeding 45th Annual Conference of the IEEE Industrial Electronics Society - IECON 2019. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781728148786. p. 5486–5492. doi: 10.1109/IECON.2019.8926972. Full text in Research Archive
• Shahraki, Amin & Haugen, Øystein (2019). An outlier detection method to improve gathered datasets for network behavior analysis in IoT. Journal of Communications. 14(6), p. 455–462. doi: 10.12720/jcm.14.6.455-462. Full text in Research Archive Show summary

  Outlier detection is a subfield of data mining to determine data points that notably deviate from the rest of a dataset. Their deviation can indicate that these data points are generated by errors and should therefore be removed or repaired. There are many reasons for outliers in a network dataset such as human or instrument errors, noise or system behavior changes. On the other side, Network Behavior Analysis (NBA) is a way to monitor traffic and recognize unusual actions in a network. Analyzing data trends in NBA methods is a common way to interpret network situation. Outliers can deviate and produce erroneous trends that influence the results of the NBA methods. This paper presents an approach that based on a method for trend detection divides the data set into subsets where contextual outliers are discovered. The outliers can then be removed to have a clear dataset that better shows the network behavior when using NBA methods. Increasing the accuracy and reliability are the goals of our method. We compare the proposed method with the Hampel method on simulated IoT network data.

• Arcega, Lorena; Font, Jaime; Haugen, Øystein & Cetina, Carlos (2019). An approach for bug localization in models using two levels: model and metamodel. Software and Systems Modeling (SoSyM). ISSN 1619-1366. 18(6), p. 3551–3576. doi: 10.1007/s10270-019-00727-y. Full text in Research Archive Show summary

  Bug localization is a common task in software engineering, especially when maintaining and evolving software products. This paper introduces a bug localization approach that, in contrast to existing source code approaches, takes advantage of domain information found in the model and the metamodel. Throughout this paper, we present an approach for bug localization in models (BLiM2) that applies the source code ideas for bug localization (textual similarity to the bug description and the Defect Localization Principle) and takes advantage of the domain information from the model and the metamodel. We evaluated our approach in BSH, a real-world industrial case study in the induction hob domain measuring the results in terms of recall, precision, the combination of both the F-measure and the Matthews correlation coefficient. Our study shows that our BLiM2 approach, which combines information from the model and the metamodel for the textual similarity and differentiates between the timespan from the model and metamodel, provides the best results in this work. We also performed a statistical analysis to provide evidence of the significance of the results. The values obtained show that there exist significant differences in the performance of the best BLiM2 approach with the approach used by our industrial partner. Finally, the effect size statistics reveals that the best BLiM2 approach obtains better results in the 78% of the times in the worst case.

View all works in NVA

• Haugen, Øystein (2025). Digital Twins: Models Everywhere. doi: https:/modelsward.scitevents.org/KeynoteSpeakers.aspx?y=2025#0. Full text in Research Archive Show summary

  Finally, everybody embraces models - in the form of "Digital Twins"! What a relief for someone who has worked with models for many years in several different domains. But we have not conquered all challenges concerning digital twins, and this talk will look at digital twins in the light of experiences from working with models and model-driven engineering for a professional lifetime. What is a model in the era of digital twins and what should it be? Do we need specific languages for describing digital twins? How should digital twins evolve as their importance increases?

• Nguyen, Hoa Thi; Haugen, Øystein & Olsson, Roland (2023). Imitation Learning from Operator Experiences for a Real time CNC Machine Controller. Full text in Research Archive
• Haugen, Øystein (2022). Requirements and state of the art. Full text in Research Archive
• Haugen, Øystein (2022). Tingenes Internett – en vidunderlig ny verden? Full text in Research Archive
• Arcega, Lorena; Font, Jaime; Haugen, Øystein & Cetina, Carlos (2022). Feature Location in Models (FLiM): Design Time and Runtime. In Lopez-Herrejon, Roberto Erick; Martinez, Jabier; Assunção, Wesley Klewerton Guez; Ziadi, Tewfik; Acher, Mathieu & Vergilio, Silvia (Ed.), Handbook of Re-Engineering Software Intensive Systems into Software Product Lines. Springer. ISSN 9783031116858. p. 79–113. doi: 10.1007/978-3-031-11686-5_4. Full text in Research Archive
• Haugen, Øystein (2022). Practitioners’ Validation of systems. Full text in Research Archive
• Dinh, Thi Thuy Nga; Tveito, Øystein & Haugen, Øystein (2021). Performance Evaluation for Energy Harvesting (EH)-based Sensor Nodes Under LoRaWAN Connectivity. Full text in Research Archive
• Haugen, Øystein (2020). Digitalisering i Europa: erfaring og demo. Full text in Research Archive
• Haugen, Øystein (2020). Tingenes internett for Industri4.0 med sikkerhet og fleksibilitet. Full text in Research Archive
• Haugen, Øystein & Stølen, Ketil (2020). Industri 4.0 – Hva kan vi lære av europeisk forskning? [Internet]. Webinar. Full text in Research Archive
• Haugen, Øystein (2020). Standardizing SysML v2. Full text in Research Archive
• Haugen, Øystein (2020). Presentation of WP6 Standardization (part). Full text in Research Archive
• Haugen, Øystein (2020). Productive4.0 - Opening the gates to the digital future. Full text in Research Archive
• Haugen, Øystein (2020). Productive 4.0 EU project update and potential standardization. Full text in Research Archive
• Haugen, Øystein (2020). Standardization – promises and perils. Full text in Research Archive
• Haugen, Øystein (2020). Productive4.0. Full text in Research Archive
• Dinh, Nga & Haugen, Øystein (2020). Analysis and Power Scheduling Algorithm in Wireless Powered Communication Networks. Full text in Research Archive
• Wąsowski, Andrzej; Paige, Richard F. & Haugen, Øystein (2020). Guest editorial to the special section on MODELS 2018. Software and Systems Modeling (SoSyM). ISSN 1619-1366. 19, p. 825–826. doi: 10.1007/s10270-020-00800-x. Full text in Research Archive
• Garcia-Ceja, Enrique; Hugo, Åsmund Pedersen; Morin, Brice; Hansen, Per Olav; Martinsen, Espen & Lam, An Ngoc [Show all 7 contributors for this article] (2019). Towards the Automation of a Chemical Sulphonation Process with Machine Learning. Full text in Research Archive
• Haugen, Øystein (2019). ECSEL-JU Lighthouse Projects Productive4.0 and Arrowhead Tools. Full text in Research Archive
• Shahraki, Amin; Geitle, Marius & Haugen, Øystein (2019). A distributed Fog node assessment model by using Fuzzy rules learned by XGBoost. Full text in Research Archive
• Haugen, Øystein & Rabi, Maben (2019). Internet of Things. Full text in Research Archive

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