A database for publications published by researchers and students at SimulaMet.
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- Journal articles (131)
- Books (5)
- Proceedings, refereed (170)
- Book chapters (4)
- Talks, keynote (15)
- PhD theses (1)
- Proceedings, non-refereed (17)
- Posters (5)
- Technical reports (10)
- Talks, invited (148)
- Talks, contributed (11)
- Public outreach (41)
- Miscellaneous (7)
Journal articles
Enhanced Equivalence Projective Simulation: A Framework for Modeling Formation of Stimulus Equivalence Classes
Neural Computation 33 (2021): 1-45.Status: Published
Enhanced Equivalence Projective Simulation: A Framework for Modeling Formation of Stimulus Equivalence Classes
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Journal Article |
Year of Publication | 2021 |
Journal | Neural Computation |
Volume | 33 |
Number | 1 |
Pagination | 1–45 |
Publisher | {MIT Press |
Archetypes of delay: An analysis of online developer conversations on delayed work items in IBM Jazz
Information and Software Technology 129 (2021): 106435.Status: Published
Archetypes of delay: An analysis of online developer conversations on delayed work items in IBM Jazz
Project(s) | Department of IT Management |
Publication Type | Journal Article |
Year of Publication | 2021 |
Journal | Information and Software Technology |
Volume | 129 |
Pagination | 106435 |
Publisher | {Elsevier |
NB-IoT Random Access: Data-driven Analysis and ML-based Enhancements
IEEE Internet of Things Journal (2021).Status: Published
NB-IoT Random Access: Data-driven Analysis and ML-based Enhancements
In the context of massive Machine Type Communications (mMTC), the Narrowband Internet of Things (NB-IoT) technology is envisioned to efficiently and reliably deal with massive device connectivity. Hence, it relies on a tailored Random Access (RA) procedure, for which theoretical and empirical analyses are needed for a better understanding and further improvements. This paper presents the first data-driven analysis of NB-IoT RA, exploiting a large scale measurement campaign. We show how the RA procedure and performance are affected by network deployment, radio coverage, and operators’ configurations, thus complementing simulation-based investigations, mostly focused on massive connectivity aspects. Comparison with the performance requirements reveals the need for procedure enhancements. Hence, we propose a Machine Learning (ML) approach, and show that RA outcomes are predictable with good accuracy by observing radio conditions. We embed the outcome prediction in a RA enhanced scheme, and show that optimized configurations enable a power consumption reduction of at least 50%. We also make our dataset available for further exploration, toward the discovery of new insights and research perspectives.
Afilliation | Communication Systems, Machine Learning |
Project(s) | Department of Mobile Systems and Analytics |
Publication Type | Journal Article |
Year of Publication | 2021 |
Journal | IEEE Internet of Things Journal |
Date Published | 01/2021 |
Publisher | IEEE |
Keywords | Cellular Internet of Things, Empirical Analysis, massive Machine Type Communications, Narrowband Internet of Things, Random Access |
Notes | Supplementary Materials, Results, and Dataset available at https://mosaic-simulamet.com/nbiot-randomaccess/ |
URL | https://ieeexplore.ieee.org/document/9324758 |
DOI | 10.1109/JIOT.2021.3051755 |
Book chapters
Multilinear Models, Iterative Methods☆
In Reference Module in Chemistry, Molecular Sciences and Chemical Engineering. Elsevier, 2020.Status: Published
Multilinear Models, Iterative Methods☆
In this section, multilinear models for multi-way arrays requiring iterative fitting algorithms are outlined. Among them: the PARAFAC (PARAllel FACtor analysis) model and one of its variants (the PARAFAC2 model); Tucker models in which one or more modes are reduced (viz., the N-way Tucker-N and Tucker-m models); hybrid models having intermediate properties between PARAFAC and Tucker ones; and coupled matrix and tensor decompositions (CMTF) which simultaneously decomposes multiple tensors. Five examples are included as to illustrate some practical aspects concerning the use of these models on analytical data.
Afilliation | Machine Learning |
Project(s) | Department of Machine Intelligence |
Publication Type | Book Chapter |
Year of Publication | 2020 |
Book Title | Reference Module in Chemistry, Molecular Sciences and Chemical Engineering |
Publisher | Elsevier |
ISBN Number | 978-0-12-409547-2 |
Keywords | -way Tucker models, CANDECOMP, Curve resolution, Exploratory analysis, Least squares, Linked mode PARAFAC, Multi-way analysis, Multi-way array, Multilinear model, PARAFAC, PARAFAC2, PARALIND, Restricted Tucker models, Tensor decomposition, Tensor-matrix factorization |
URL | http://www.sciencedirect.com/science/article/pii/B9780124095472146098 |
DOI | 10.1016/B978-0-12-409547-2.14609-8 |
Journal articles
PoBT: A Light Weight Consensus Algorithm for Scalable IoT Business Blockchain
IEEE Internet of Things Journal 7, no. 3 (2020): 2343-2355.Status: Published
PoBT: A Light Weight Consensus Algorithm for Scalable IoT Business Blockchain
Efficient and smart business processes are heavily dependent on the Internet of Things (IoT) networks, where end-to-end optimization is critical to the success of the whole ecosystem. These systems, including industrial, healthcare, and others, are large scale complex networks of heterogeneous devices. This introduces many security and access control challenges. Blockchain has emerged as an effective solution for addressing several such challenges. However, the basic algorithms used in the business blockchain are not feasible for large scale IoT systems. To make them scalable for IoT, the complex consensus-based security has to be downgraded. In this article, we propose a novel lightweight proof of block and trade (PoBT) consensus algorithm for IoT blockchain and its integration framework. This solution allows the validation of trades as well as blocks with reduced computation time. Also, we present a ledger distribution mechanism to decrease the memory requirements of IoT nodes. The analysis and evaluation of security aspects, computation time, memory, and bandwidth requirements show significant improvement in the performance of the overall system.
Afilliation | Communication Systems |
Project(s) | Simula Metropolitan Center for Digital Engineering, The Center for Resilient Networks and Applications |
Publication Type | Journal Article |
Year of Publication | 2020 |
Journal | IEEE Internet of Things Journal |
Volume | 7 |
Issue | 3 |
Pagination | 2343 - 2355 |
Publisher | IEEE |
Blockchain Empowered Cooperative Authentication with Data Traceability in Vehicular Edge Computing
IEEE Transactions on Vehicular Technology 69, no. 4 (2020): 4221-4232.Status: Published
Blockchain Empowered Cooperative Authentication with Data Traceability in Vehicular Edge Computing
The dynamic environment due to traffic mobility and wireless communication from/to vehicles make identity authentication and trust management for privacy preservation based on vehicular edge computing (VEC) an increasingly important problem in vehicular networks. However, existing authentication schemes mainly focus on communication between a single trusted edge computing node and multiple vehicles. This framework may suffer the bottleneck problem due to the single edge computing node, and the performance depends heavily on its resources. In this paper, a blockchain empowered group-authentication scheme is proposed for vehicles with decentralized identification based on secret sharing and dynamic proxy mechanism. Sub-authentication results are aggregated for trust management based blockchain to implement collaborative authentication. The edge computing node with a higher-reputation stored in the tamper-proof blockchain can upload the final aggregated authentication result to the central server to achieve the decentralized authentication. This work analyzes typical attacks for this scheme and shows that the proposed scheme achieves cooperative privacy preservation for vehicles while also reducing communication overhead and computation cost.
Afilliation | Communication Systems |
Project(s) | Simula Metropolitan Center for Digital Engineering, The Center for Resilient Networks and Applications |
Publication Type | Journal Article |
Year of Publication | 2020 |
Journal | IEEE Transactions on Vehicular Technology |
Volume | 69 |
Issue | 4 |
Pagination | 4221 - 4232 |
Publisher | IEEE |
Deep Reinforcement Learning for Partially Observable Data Poisoning Attack in Crowdsensing Systems
IEEE Internet of Things Journal (Early Access) (2020).Status: Published
Deep Reinforcement Learning for Partially Observable Data Poisoning Attack in Crowdsensing Systems
Crowdsensing systems collect various types of data from sensors embedded on mobile devices owned by individuals. These individuals are commonly referred to as workers that complete tasks published by crowdsensing systems. Because of the relative lack of control over worker identities, crowdsensing systems are susceptible to data poisoning attacks which interfering with data analysis results by injecting fake data conflicting with ground truth. Frameworks like TruthFinder can resolve data conflicts by evaluating the trustworthiness of the data providers. These frameworks somehow make crowdsensing systems more robust since they can limit the impact of dirty data by reducing the value of unreliable workers. However, previous work has shown that TruthFinder may also be affected by data poisoning attack when the malicious workers have access to global information. In this paper, we focus on partially observable data poisoning attacks in crowdsensing systems. We show that even if the malicious workers only have access to local information, they can find effective data poisoning attack strategies to interfere with crowd sensing systems with TruthFinder. First, we formally model the problem of partially observable data poisoning attack against crowdsensing systems. Then, we propose a data poisoning attack method based on deep reinforcement learning, which helps malicious workers jeopardize with TruthFinder while hiding themselves. Based on the method, the malicious workers can learn from their attack attempts and evolve the poisoning strategies continuously. Finally, we conduct experiments on real-life data sets to verify the effectiveness of the proposed method.
Afilliation | Communication Systems |
Project(s) | Simula Metropolitan Center for Digital Engineering, The Center for Resilient Networks and Applications |
Publication Type | Journal Article |
Year of Publication | 2020 |
Journal | IEEE Internet of Things Journal (Early Access) |
Publisher | IEEE |
Deep Reinforcement Learning for Economic Dispatch of Virtual Power Plant in Internet of Energy
IEEE Internet of Things Journal (Early Access) (2020).Status: Published
Deep Reinforcement Learning for Economic Dispatch of Virtual Power Plant in Internet of Energy
With high penetration of large scale distributed renewable energy generation, the power system is facing enormous challenges in terms of the inherent uncertainty of power generation of renewable energy resources. In this regard, virtual power plants (VPPs) can play a crucial role in integrating a large number of distributed generation units (DGs) more effectively to improve the stability of the power systems. Due to the uncertainty and nonlinear characteristics of DGs, reliable economic dispatch in VPPs requires timely and reliable communication between DGs, and between the generation side and the load side. The online economic dispatch optimizes the cost of VPPs. In this paper, we propose a deep reinforcement learning (DRL) algorithm for the optimal online economic dispatch strategy in VPPs. By utilizing DRL, our proposed algorithm reduced the computational complexity while also incorporating large and continuous state space due to the stochastic characteristics of distributed power generation. We further design an edge computing framework to handle the stochastic and large-state space characteristics of VPPs. The DRL based real time economic dispatch algorithm is executed online. We utilize real meteorological and load data to analyze and validate the performance of our proposed algorithm. The experimental results show that our proposed DRL based algorithm can successfully learn the characteristics of DGs and industrial user demands. It can learn to choose actions to minimize the cost of VPPs. Compared with DPG and DDPG, our proposed method has lower time complexity.
Afilliation | Communication Systems |
Project(s) | Simula Metropolitan Center for Digital Engineering, The Center for Resilient Networks and Applications |
Publication Type | Journal Article |
Year of Publication | 2020 |
Journal | IEEE Internet of Things Journal (Early Access) |
Publisher | IEEE |
Cross-product penalized component analysis (X-CAN)
Chemometrics and Intelligent Laboratory Systems 203 (2020): 104038.Status: Published
Cross-product penalized component analysis (X-CAN)
Afilliation | Machine Learning |
Project(s) | Department of Machine Intelligence |
Publication Type | Journal Article |
Year of Publication | 2020 |
Journal | Chemometrics and Intelligent Laboratory Systems |
Volume | 203 |
Pagination | 104038 |
Date Published | 06/2020 |
Publisher | Elsevier |
ISSN | 01697439 |
DOI | 10.1016/j.chemolab.2020.104038 |
Multi-Linear Population Analysis (MLPA) of LFP Data Using Tensor Decompositions
Frontiers in Applied Mathematics and Statistics 6 (2020).Status: Published
Multi-Linear Population Analysis (MLPA) of LFP Data Using Tensor Decompositions
Afilliation | Machine Learning |
Project(s) | Department of Machine Intelligence |
Publication Type | Journal Article |
Year of Publication | 2020 |
Journal | Frontiers in Applied Mathematics and Statistics |
Volume | 6 |
Number | 41 |
Date Published | Aug-09-2020 |
Publisher | Frontiers |
Keywords | CANDECOMP/PARAFAC, independent component analysis (ICA), local field potential (LFP), Neuroscience, population rate model, principal component analysis (PCA), tensor decompositions |
URL | https://www.frontiersin.org/article/10.3389/fams.2020.00041/fullhttps://... |
DOI | 10.3389/fams.2020.00041 |