A database for publications published by researchers and students at SimulaMet.
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- Journal articles (290)
- Books (9)
- Edited books (3)
- Proceedings, refereed (320)
- Book chapters (13)
- Talks, keynote (23)
- PhD theses (9)
- Proceedings, non-refereed (19)
- Posters (16)
- Technical reports (15)
- Manuals (1)
- Talks, invited (186)
- Talks, contributed (30)
- Public outreach (62)
- Miscellaneous (21)
Journal articles
Energy-efficient online control of a water distribution network based on Deep Reinforcement Learning
IEEE Internet of Things Journal (2023).Status: Submitted
Energy-efficient online control of a water distribution network based on Deep Reinforcement Learning
Afilliation | Machine Learning |
Project(s) | Signal and Information Processing for Intelligent Systems |
Publication Type | Journal Article |
Year of Publication | 2023 |
Journal | IEEE Internet of Things Journal |
Publisher | IEEE Internet of Things Journal |
Notes | This work is a joint collaboration between SimulaMet and University of Agder. This work was supported by the IKTPLUSS INDURB grant 270730/O70 from the Research Council of Norway. |
Network-Aware RF-Energy Harvesting for Designing Energy Efficient IoT Networks
Elsevier Internet of Things 22 (2023).Status: Accepted
Network-Aware RF-Energy Harvesting for Designing Energy Efficient IoT Networks
Afilliation | Communication Systems |
Project(s) | Signal and Information Processing for Intelligent Systems |
Publication Type | Journal Article |
Year of Publication | 2023 |
Journal | Elsevier Internet of Things |
Volume | 22 |
Date Published | 07/2023 |
Publisher | Elsevier |
DOI | 10.1016/j.iot.2023.100770 |
A logic-based event controller for means-end reasoning in simulation environments
SIMULATION 61 (2023).Status: Published
A logic-based event controller for means-end reasoning in simulation environments
Simulation games are designed to cultivate expertise and rehearse particular skill sets. To yield longitudinal effects, sequences of events must be crafted to yield intended learning outcomes, sometimes by focusing on particularly difficult situations and replaying variants. The present paper develops a logic-based approach for encoding the interrelation between action, events, and objects in a manner that allows the resulting scenario description to immediately be executed in a game development environment. This has the dual effect of decoupling the description of a scenario from the simulation platform itself, as well as supporting iterative and flexible development of learning content. To this end, we provide three interrelated components: First, we develop a scenario description language based on Answer Set Programming. The language is designed to allow an automated reasoner to deduce a schedule of the future events that are caused by an action taken in a given simulation environment. Second, we define a protocol for exchanging actions and computed futures between, respectively, the simulation environment and the external automated reasoner. Finally, as a proof of concept, we develop an Application Programming Interface (API) for the Unity Real-Time Development Platform that implements the protocol and offers a software framework for connecting the computed future events to concrete game objects. This allows the game to evolve coherently from the specification. We argue that the resulting system inherits capabilities for artificial commonsense reasoning from its declarative basis which are useful for reasoning about an evolving emergency incident or training scenario.
Afilliation | Software Engineering |
Project(s) | EDOS: Effective Digitalization of Public Sector |
Publication Type | Journal Article |
Year of Publication | 2023 |
Journal | SIMULATION |
Volume | 61 |
Date Published | 03/2023 |
Publisher | SAGE journals |
ISSN | 0037-5497 |
URL | http://journals.sagepub.com/doi/10.1177/00375497231157384http://journals... |
DOI | 10.1177/00375497231157384 |
Towards a Lightweight Task Scheduling Framework for Cloud and Edge Platform
Internet of Things; Engineering Cyber Physical Human Systems (2023).Status: Accepted
Towards a Lightweight Task Scheduling Framework for Cloud and Edge Platform
Mobile devices are becoming ubiquitous in our daily lives, but they have limited computational capacity. Thanks to the advancement in the network infrastructure, task offloading from resource-constrained devices to the near edge and the cloud becomes possible and advantageous. Complete task offloading is now possible to almost limitless computing resources of public cloud platforms. Generally, the edge computing resources support latency-sensitive applications with limited computing resources, while the cloud supports latency-tolerant applications. This paper proposes one lightweight task-scheduling framework from cloud service provider perspective, for applications using both cloud and edge platforms. Here, the challenge is using edge and cloud resources efficiently when necessary. Such decisions have to be made quickly, with a small management overhead. Our framework aims at solving two research questions. They are: i) How to distribute tasks to the edge resource pools and multi-clouds? ii) How to manage these resource pools effectively with low overheads? To answer these two questions, we examine the performance of our proposed framework based on Reliable Server Pooling (RSerPool). We have shown via simulations that RSerPool, with the correct usage and configuration of pool member selection policies, can accomplish the cloud/edge setup resource selection task with a small overhead.
Afilliation | Communication Systems |
Project(s) | The Center for Resilient Networks and Applications, Simula Metropolitan Center for Digital Engineering, Simula Metropolitan Center for Digital Engineering, NorNet, SMIL: SimulaMet Interoperability Lab |
Publication Type | Journal Article |
Year of Publication | 2023 |
Journal | Internet of Things; Engineering Cyber Physical Human Systems |
Publisher | Elsevier |
Keywords | Cloud computing, Edge Computing, Reliable Server Pooling (RSerPool), Resource Pools, Task Scheduling |
Distributed Linear Network Operators via Successive Graph Shift Matrices
IEEE Transactions on Signal and Information Processing over Networks 9 (2023): 315-328.Status: Published
Distributed Linear Network Operators via Successive Graph Shift Matrices
Afilliation | Machine Learning |
Project(s) | Signal and Information Processing for Intelligent Systems |
Publication Type | Journal Article |
Year of Publication | 2023 |
Journal | IEEE Transactions on Signal and Information Processing over Networks |
Volume | 9 |
Pagination | 315-328 |
Date Published | 04/2023 |
Publisher | IEEE Transactions on Signal and Information Processing over Networks |
ISSN | 2373-776X |
Notes | This work is a joint collaboration between SimulaMet and University of Agder. This work was supported by the PETROMAKS Smart-Rig grant 244205 and the IKTPLUSS INDURB grant 270730/O70 from the Research Council of Norway. |
DOI | 10.1109/TSIPN.2023.3271148 |
Proxy Path Scheduling and Erasure Reconstruction for Low Delay mmWave Communication
IEEE Communications Letters (2023).Status: Accepted
Proxy Path Scheduling and Erasure Reconstruction for Low Delay mmWave Communication
Afilliation | Communication Systems |
Project(s) | The Center for Resilient Networks and Applications, Information Theory Section |
Publication Type | Journal Article |
Year of Publication | 2023 |
Journal | IEEE Communications Letters |
Publisher | IEEE |
ISSN | 1558-2558 |
URL | https://ieeexplore.ieee.org/document/10107383 |
DOI | 10.1109/LCOMM.2023.3269526 |
Characteristics and generative mechanisms of software development productivity distributions
Information and Software Technology (2023).Status: Published
Characteristics and generative mechanisms of software development productivity distributions
Context: There is considerable variation in the productivity of software developers. Better knowledge about this variation may provide valuable inputs for the design of skill tests and recruitment processes. Objective: This paper aims to identify properties of software development productivity distributions and gain insight into mechanisms that potentially explain these productivity differences. Method: Four data sets that contain the results of software developers solving the same programming tasks were collected. The properties of the productivity distributions were analyzed, the fits of different types of distributions to the productivity data were compared, and potential generative mechanisms that would lead to the types of distributions with the best fit to the productivity data were evaluated. Results: The coefficient of variance of the productivity of the software developers was, on average, 0.55, with the top 50% of developers having average productivity that was 2.44 times higher than the bottom 50% of developers. All productivity samples were right-skewed, with an average skew of 1.79. About 30% of the observed productivity variance was explained by non-systematic, i.e., within-developer, variance. The distributions with the best fit to the empirical productivity data were the lognormal and power-law-with-an-exponential-cutoff distributions. The analysis of the mechanisms leading to productivity differences found no support for the "rich-getting-richer" explanation proposed for other disciplines. Instead, it suggests a constant productivity difference with increasing experience. Conclusion: The substantial difference in productivity among software developers solving programming tasks indicates that a thorough evaluation of skill in the recruitment process can be rewarding. In particular, the long tail towards higher productivity values demonstrates the large gains that can be achieved by detecting and recruiting developers with very high productivity. More research is needed to understand the mechanisms leading to the large productivity differences.
Afilliation | Software Engineering |
Project(s) | EDOS: Effective Digitalization of Public Sector |
Publication Type | Journal Article |
Year of Publication | 2023 |
Journal | Information and Software Technology |
Publisher | Elsevier |
Enabling Autonomous Teams and Continuous Deployment at Scale
IEEE IT Professional (2023).Status: Published
Enabling Autonomous Teams and Continuous Deployment at Scale
In this article, we give advice on transitioning to a more agile delivery model for large-scale agile development projects based on experience from the Parental Benefit Project of the Norwegian Labour and Welfare Administration. The project modernized a central part of the organization’s IT portfolio and included up to ten development teams working in parallel. The project successfully changed from using a delivery model which combined traditional project management elements and agile methods to a more agile delivery model with autonomous teams and continuous deployment. This transition was completed in tandem with the project execution. We identify key lessons learned which will be useful for other organizations considering similar changes and report how the new delivery model reduced risk and opened up a range of new possibilities for delivering the benefits of digitalization.
Afilliation | Software Engineering |
Project(s) | EDOS: Effective Digitalization of Public Sector |
Publication Type | Journal Article |
Year of Publication | 2023 |
Journal | IEEE IT Professional |
Publisher | IEEE |
Improved Measurement of Software Development Effort Estimation Bias
Information and software technology (2023).Status: Published
Improved Measurement of Software Development Effort Estimation Bias
Context: While prior software development effort estimation research has examined the properties of estimation error measures, there has not been much research on the properties of measures of estimation bias. Objectives: Improved measurement of software development effort estimation bias. Methods: Analysis of the extent to which measures of estimation bias meet the criterion that perfect estimates should result in zero bias. Results: Recommendations for measurement of estimation bias for estimates of the mean, median, and mode software development effort. The results include the recommendation to avoid a commonly used measure of effort estimation bias. Conclusion: Proper evaluation of estimation bias requires knowledge about the type of estimates evaluated, together with the selection of a measure of estimation bias that gives zero bias for perfect estimates of that type.
Afilliation | Software Engineering |
Project(s) | EDOS: Effective Digitalization of Public Sector |
Publication Type | Journal Article |
Year of Publication | 2023 |
Journal | Information and software technology |
Publisher | Elsevier |
Sparse Online Learning with Kernels using Random Features for Estimating Nonlinear Dynamic Graphs
IEEE Transactions on Signal Processing (2023).Status: Published
Sparse Online Learning with Kernels using Random Features for Estimating Nonlinear Dynamic Graphs
Online topology estimation of graph-connected time series is challenging in practice, particularly because the dependencies between the time series in many real-world scenarios are nonlinear. To address this challenge, we introduce a novel kernel-based algorithm for online graph topology estimation. Our proposed algorithm also performs a Fourier-based random feature approximation to tackle the curse of dimensionality associated with kernel representations. Exploiting the fact that real-world networks often exhibit sparse topologies, we propose a group-Lasso based optimization framework, which is solved using an iterative composite objective mirror descent method, yielding an online algorithm with fixed computational complexity per iteration. We provide theoretical guarantees for our algorithm and prove that it can achieve sublinear dynamic regret under certain reasonable assumptions. In experiments conducted on both real and synthetic data, our method outperforms existing state-of-the-art competitors.
Afilliation | Machine Learning |
Project(s) | Signal and Information Processing for Intelligent Systems |
Publication Type | Journal Article |
Year of Publication | 2023 |
Journal | IEEE Transactions on Signal Processing |
Publisher | IEEE |
ISSN | 1941-0476 |
Other Numbers | Print ISSN: 1053-587X |
Notes | This work is a joint collaboration between SimulaMet and University of Agder. This work was supported by the IKTPLUSS INDURB grant 270730/O70 and the SFI Offshore Mechatronics grant 237896/O30 from the Research Council of Norway. |
URL | https://ieeexplore.ieee.org/document/10141675 |
DOI | 10.1109/TSP.2023.3282068 |