Professor Emeritus
Academic Interests
Distributed systems, Middleware, Self-adaptive software systems, Internet of Things, Fog Computing, Cyber-Physical Systems, Adaptive P2P video streaming, Energy Informatics
Teaching
Recent publications
Tags:
Distributed systems,
middleware,
self-adaptive software systems,
internet of things,
edge & fog computing,
cyber-physical systems,
smart cities,
energy informatics
Publications
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Foroughi, Mehdi; Maharjan, Sabita; Zhang, Yan & Eliassen, Frank
(2023).
Autonomous Peer-to-Peer Energy Trading in Networked Microgrids: A Distributed Deep Reinforcement Learning Approach.
In Harid, Noureddine (Eds.),
2023 IEEE PES Conference on Innovative Smart Grid Technologies - Middle East (ISGT Middle East).
IEEE conference proceedings.
ISSN 978-1-6654-6543-4.
doi:
10.1109/ISGTMiddleEast56437.2023.10078444.
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Osnes, Idun; Yazidi, Anis; Jacobsen, Hans-Arno; Eliassen, Frank & Sartori, Sabrina
(2022).
Harnessing Task Usage Prediction and Latency Sensitivity for Scheduling Workloads in Wind-Powered Data Centers.
Energies.
ISSN 1996-1073.
15(12).
doi:
10.3390/en15124469.
Full text in Research Archive
Show summary
The growing number of data centers consumes a vast amount of energy for processing.
There is a desire to reduce the environmental footprint of the IT industry, and one way to achieve
this is to use renewable energy sources. A challenge with using renewable resources is that the
energy output is irregular as a consequence of the intermittent nature of this form of energy. In
this paper, we propose a simple and yet efficient latency-aware workload scheduler that creates
an energy-agile workload, by deferring tasks with low latency sensitivity to periods with excess
renewable energy. The scheduler also increases the overall efficiency of the data center, by packing
the workload into as few servers as possible, using neural-network-based predictions of resource
usage on an individual task basis to avoid unnecessarily provisioning an excess number of servers.
The scheduler was tested on a subset of real-world workload traces, and real-world wind-power
generation data, simulating a small-scale data center co-located with a wind turbine. Extensive
experimental results show that the devised scheduler reduced the number of servers doing work
in periods of low wind-power production up to 93% of the time, by postponing tasks with a low
latency sensitivity to a later interval.
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Chung, Hwei-Ming; Maharjan, Sabita; Zhang, Yan; Eliassen, Frank & Strunz, Kai
(2021).
Optimal Energy Trading with Demand Responses in Cloud Computing Enabled Virtual Power Plant in Smart Grids.
IEEE Transactions on Cloud Computing.
ISSN 2168-7161.
10(1),
p. 17–30.
doi:
10.1109/TCC.2021.3118563.
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Mohammadi, Sara; Eliassen, Frank; Zhang, Yan & Jacobsen, Hans-Arno
(2021).
Detecting False Data Injection Attacks in Peer to Peer Energy Trading Using Machine Learning.
IEEE Transactions on Dependable and Secure Computing.
ISSN 1545-5971.
19(5),
p. 3417–3431.
doi:
10.1109/TDSC.2021.3096213.
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Bordin, Chiara; Mishra, Sambeet; Safari, Amir & Eliassen, Frank
(2021).
Educating the energy informatics specialist: opportunities and challenges in light of research and industrial trends.
SN Applied Sciences.
ISSN 2523-3963.
3,
p. 1–17.
doi:
10.1007/s42452-021-04610-8.
Full text in Research Archive
Show summary
Contemporary energy research is becoming more interdisciplinary through the involvement of technical, economic, and social aspects that must be addressed simultaneously. Within such interdisciplinary energy research, the novel domain of energy informatics plays an important role, as it involves different disciplines addressing the socio-techno-economic challenges of sustainable energy and power systems in a holistic manner. The objective of this paper is to draw an overview of the novel domain of energy informatics by addressing the educational opportunities as well as related challenges in light of current trends and the future direction of research and industrial innovation. In this study we discuss the energy informatics domain in a way that goes beyond a purely scientific research perspective. This paper widens the analyses by including reflections on current and future didactic approaches with industrial innovation and research as a background. This paper provides key recommendations for the content of a foundational introductory energy informatics course, as well as suggestions on distinguishing features to be addressed through more specialized courses in the field. The importance of this work is based on the need for better guidelines for a more appropriate education of a new generation of experts who can take on the novel interdisciplinary challenges present in future integrated, sustainable energy systems.
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Natori, Kohei; Mizuno, Keisuke; Namerikawa, Toru; Sartori, Sabrina & Eliassen, Frank
(2020).
Frequency Response-Based Initial Parameter Estimation for SOC of Lithium-Ion Battery.
IFAC-PapersOnLine.
ISSN 2405-8963.
53(2),
p. 12695–12700.
doi:
10.1016/j.ifacol.2020.12.1859.
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Mohammadi, Sara; Eliassen, Frank & Zhang, Yan
(2020).
Effects of false data injection attacks on a local P2P energy trading market with prosumers,
2020 IEEE PES Innovative Smart Grid Technologies Europe - ISGT-Europe.
IEEE (Institute of Electrical and Electronics Engineers).
ISSN 978-1-7281-7100-5.
p. 31–35.
doi:
10.1109/ISGT-Europe47291.2020.9248761.
Show summary
In the energy sector, peer-to-peer (P2P) energy trading is a promising method for the future smart grid. Despite all the benefits, this method is vulnerable to some malicious attacks, e.g., false data injection attacks (FDIAs). This paper explores the vulnerability of local P2P energy trading to FDIAs. Previous works on FDIAs in energy neighborhoods consider consumers only, or do not consider the effect of including prosumers. We consider the situation where an attacker tries to modify the participants’ demands to gain some benefits. Through simulations using real datasets, we demonstrate possible effects of FDIAs on both selling and buying energy prices in P2P energy trading involving both prosumers and local energy suppliers. From the simulations, we learn that the best chance for an attacker to remain undetected is to target a high number of prosumers and only modify their demand with a small fraction. Moreover, by comparing the results from the attack scenario with the normal situation, we observe that an attack generally leads to less favorable energy prices and thus reduced incentives to become or even remain an energy-selling prosumer.
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Mohamedelsadig Ali Ahmed, Awadelrahman; Zhang, Yan & Eliassen, Frank
(2020).
Generative Adversarial Networks and Transfer
Learning for Non-Intrusive Load Monitoring in
Smart Grids,
2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm).
IEEE International Conference on Smart Grid Communications (SmartGridComm).
ISSN 978-1-7281-6127-3.
doi:
10.1109/SmartGridComm47815.2020.9302933.
Show summary
Abstract—Non-intrusive load monitoring (NILM) objective is
to disaggregate the total power consumption of a building into individual appliance-level profiles. This gives insights to consumers to efficiently use energy and realizes smart grid efficiency outcomes. While many studies focus on achieving accurate models, few of them address the models generalizability. This paper proposes two approaches based on generative adversarial networks
to achieve high-accuracy load disaggregation. Concurrently, the paper addresses the model generalizability in two ways, the first is by transfer learning by parameter sharing and the other is by learning compact common representations between source and target domains. This paper also quantitatively evaluate the worth
of these transfer learning approaches based on the similarity between the source and target domains. The models are evaluated on three open-access datasets and outperformed recent machine learning methods.
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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.
ISSN 1932-4537.
doi:
10.1109/TNSM.2020.3035315.
Full text in Research Archive
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Chung, Hwei-Ming; Maharjan, Sabita; Zhang, Yan & Eliassen, Frank
(2020).
Intelligent Charging Management of Electric Vehicles Considering Dynamic User Behavior and Renewable Energy: A Stochastic Game Approach.
IEEE transactions on intelligent transportation systems (Print).
ISSN 1524-9050.
22(12),
p. 7760–7771.
doi:
10.1109/TITS.2020.3008279.
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Chung, Hwei-Ming; Maharjan, Sabita; Zhang, Yan & Eliassen, Frank
(2020).
Distributed Deep Reinforcement Learning for Intelligent Load Scheduling in Residential Smart Grid.
IEEE Transactions on Industrial Informatics.
ISSN 1551-3203.
doi:
10.1109/TII.2020.3007167.
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Chung, Hwei-Ming; Maharjan, Sabita; Zhang, Yan; Eliassen, Frank & Strunz, Kai
(2020).
Placement and Routing Optimization for Automated Inspection with UAVs: A Study in Offshore Wind Farm .
IEEE Transactions on Industrial Informatics.
ISSN 1551-3203.
17(5),
p. 3032–3043.
doi:
10.1109/TII.2020.3004816.
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Velazquez-Garcia, Francisco Javier; Halvorsen, Pål; Stensland, Håkon Kvale & Eliassen, Frank
(2018).
Dynamic Adaptation of Multimedia Presentations for Videoconferencing in Application Mobility.
IEEE International Conference on Multimedia and Expo.
ISSN 1945-7871.
2018-July,
p. 1–6.
doi:
10.1109/ICME.2018.8486565.
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Provensi, Lucas Luiz; Singh, Abhishek; Eliassen, Frank & Vitenberg, Roman
(2018).
Maelstream: Self-organizing media streaming for many-to-many interaction.
IEEE Transactions on Parallel and Distributed Systems.
ISSN 1045-9219.
29(6),
p. 1342–1356.
doi:
10.1109/TPDS.2018.2791599.
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Velazquez-Garcia, Francisco Javier & Eliassen, Frank
(2017).
DAMPAT: Dynamic Adaptation of Multimedia Presentations in Application Mobility.
In Bulterman, Dick C.A.; Kankanhalli, Mohan; Sheu, Phillip C.-Y. & Tsai, Jeffrey J.P. (Ed.),
2017 IEEE International Symposium on Multimedia.
IEEE (Institute of Electrical and Electronics Engineers).
ISSN 978-1-5386-2937-6.
p. 312–317.
doi:
10.1109/ISM.2017.56.
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Pham, Hai Ngoc; Zhang, Yan; Skeie, Tor; Engelstad, Paal E. & Eliassen, Frank
(2016).
Joint energy-efficient cooperative spectrum sensing and power allocation in Cognitive Machine-to-Machine Communications,
Proceedings of the 2016 International Wireless Communications and Mobile Computing Conference (IWCMC 2016).
IEEE (Institute of Electrical and Electronics Engineers).
ISSN 978-1-5090-0304-4.
doi:
10.1109/IWCMC.2016.7577208.
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Dar, Kashif Sana; Taherkordi, Amirhosein & Eliassen, Frank
(2016).
Enhancing Dependability of Cloud-Based IoT Services through Virtualization.
In Abdelzaher, Tarek; Cao, Jiannong & Marron, Pedro Jose (Ed.),
2016 IEEE First International Conference on Internet-of-Things Design and Implementation (IoTDI).
IEEE (Institute of Electrical and Electronics Engineers).
ISSN 978-1-4673-9948-7.
p. 106–116.
doi:
10.1109/IoTDI.2015.38.
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Taherkordi, Amirhosein & Eliassen, Frank
(2015).
Models@run.time for creating in-cloud dynamic cyber-physical ecosystems.
IEEE International Conference on Cloud Computing Technology and Science.
ISSN 2330-2186.
2015-February(February),
p. 976–982.
doi:
10.1109/CloudCom.2014.158.
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Maharjan, Sabita; Zhang, Yan; Gjessing, Stein; Ulleberg, Øystein & Eliassen, Frank
(2015).
Providing Microgrid Resilience during Emergencies using Distributed Energy Resources.
In Tiedemann, Ed; Krishnaswamy, Dilip & Prasad, Neeli R. (Ed.),
2015 IEEE Global Communications Conference.
IEEE Press.
ISSN 978-1-4673-9526-7.
doi:
10.1109/GLOCOMW.2015.7414031.
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Orfanus, Dalimir; Eliassen, Frank & Pignaton de Freitas, Edison
(2014).
Self-Organizing Relay Network Supporting Remotely Deployed Sensor Nodes in Military Operations.
In Koucheryavy, Yevgeni & Makarov, Sergey (Ed.),
Proceedings of 6th IEEE International Congress on Ultra Modern Telecommunications and Control Systems (ICUMT).
IEEE conference proceedings.
ISSN 978-1-4799-5291-5.
p. 326–333.
doi:
10.1109/ICUMT.2014.7002122.
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Dar, Kashif Sana; Taherkordi, Amirhosein; Baraki, Harun; Eliassen, Frank & Geihs, Kurt
(2014).
A Resource Oriented Integration Architecture for the Internet of Things: A Business Processes Perspective.
Pervasive and Mobile Computing.
ISSN 1574-1192.
20,
p. 145–159.
doi:
10.1016/j.pmcj.2014.11.005.
Show summary
The vision of the Internet of Things (IoT) foresees a future Internet incorporating smart physical objects that offer hosted functionality as IoT services. These services when integrated with the traditional enterprise level services form the creation of ambient intelligence for a wide range of applications. To facilitate seamless access and service life cycle management of large, distributed and heterogeneous IoT resources, service oriented computing and resource oriented approaches have been widely used as promising technologies. However, a reference architecture integrating IoT services into either of these two technologies is still an open research challenge. In this article, we adopt the resource oriented approach to provide an end-to-end integration architecture of front-end IoT devices with the back-end business process applications. The proposed architecture promises a programmer friendly access to IoT services, an event management mechanism to propagate context information of IoT devices, a service replacement facility upon service failure, and a decentralized execution of the IoT aware business processes.
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Orfanus, Dalimir; Janacik, Peter & Eliassen, Frank
(2013).
Integration Framework for Simulation Tools to Engineer Emergent Self-Organizing Behavior.
In Al-Dabass, David; Orsoni, Alessandra; Yunus, Jasmy; Cant, Richard & Ibrahim, Zuwairie (Ed.),
UKSim 15th International Conference on Computer Modelling and Simulation.
IEEE Press.
ISSN 978-1-4673-6421-8.
p. 335–340.
doi:
10.1109/UKSim.2013.53.
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Romero, Daniel; Hermosillo, Gabriel; Taherkordi, Amirhosein; Nzekwa, Russel; Rouvoy, Romain & Eliassen, Frank
(2013).
The DigiHome Service-Oriented Platform.
Software, Practice & Experience.
ISSN 0038-0644.
43(10),
p. 1205–1218.
doi:
10.1002/spe.1125.
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Provensi, Lucas Luiz; Eliassen, Frank; Vitenberg, Roman & Rouvoy, Romain
(2013).
Using Fuzzy Policies to Improve Context Interpretation in Adaptive Systems.
ACM SIGAPP Applied Computing Review.
ISSN 1559-6915.
13(3),
p. 26–37.
doi:
10.1145/2537728.2537731.
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Provensi, Lucas Luiz; Eliassen, Frank; Vitenberg, Roman & Rouvoy, Romain
(2013).
Improving context interpretation by using fuzzy policies: the case of adaptive video streaming.
In Sung Y., Shin & José Carlos, Maldonado (Ed.),
Proceedings of the 28th Annual ACM Symposium on Applied Computing.
Association for Computing Machinery (ACM).
ISSN 978-1-4503-1656-9.
p. 415–422.
doi:
10.1145/2480362.2480447.
Show summary
Adaptation is an increasingly important requirement for software systems executing in large-scale, heterogeneous, and dynamic environments. A central aspect of the adaptation methodology is management of contextual information needed to support the adaptation process. A major design challenge of managing contextual data lies in the fact that the information is partial, uncertain, and inherently suitable for diverging interpretations. While existing adaptation solutions focus on techniques, methods, and tools, the challenge of managing and interpreting ambiguous contextual information remains largely unresolved. In this paper, we present a new adaptation approach that aims to overcome these issues by applying fuzzy set theory and approximate reasoning. It proposes a knowledge management scheme to interpret imprecise information and effectively integrate this information into the adaptation feedback control loop. To test and evaluate our solution, we implemented it in an adaptation engine to perform rate control for media streaming applications. The evaluation results show the benefits of our approach in terms of flexibility and performance when compared to more traditional methods, such as TCP-friendly rate control.
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Hallsteinsen, Svein Olav; Geihs, Kurt; Paspallis, Nearchos; Eliassen, Frank; Horn, Geir Henrik & Lorenzo, Jorge
[Show all 8 contributors for this article]
(2012).
A development framework and methodology for self-adapting applications in ubiquitous computing environments.
Journal of Systems and Software.
ISSN 0164-1212.
85(12),
p. 2840–2859.
doi:
10.1016/j.jss.2012.07.052.
Show summary
Today software is the main enabler of many of the appliances and devices omnipresent in our daily life and important for our well being and work satisfaction. It is expected that the software works as intended, and that the software always and everywhere provides us with the best possible utility. This paper discusses the motivation, technical approach, and innovative results of the MUSIC project. MUSIC provides a comprehensive software development framework for applications that operate in ubiquitous and dynamic computing environments and adapt to context changes. Context is understood as any information about the user needs and operating environment which vary dynamically and have an impact on design choices. MUSIC supports several adaptation mechanisms and offers a model-driven application development approach supported by a sophisticated middleware that facilitates the dynamic and automatic adaptation of applications and services based on a clear separation of business logic, context awareness and adaptation concerns. The main contribution of this paper is a holistic, coherent presentation of the motivation, design, implementation, and evaluation of the MUSIC development framework and methodology.
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Le-Trung, Quan; Engelstad, Paal Einar; Skeie, Tor; Eliassen, Frank & Taherkordi, Amirhosein
(2011).
Mobility Management for All-IP Mobile Networks.
In Makaya, Christian & Pierre, Samuel (Ed.),
Emerging Wireless Networks - Concepts, Techniques, Applications.
Taylor & Francis.
ISSN 9781439821350.
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Provensi, Lucas Luiz & Eliassen, Frank
(2011).
Towards a Flexible and Evolvable Framework for Self-Adaptation.
Electronic Communications of the EASST.
ISSN 1863-2122.
43.
Show summary
The growing complexity, scale and heterogeneity of software systems boosted a great deal of research in the field of self-management and self-adaptation. In general, current solutions are built as fixed frameworks, with rigid methodology, models and tools that are best suited for their target application domain but can not be easily applied in different domains. Furthermore, they lack the flexibility to let the developer make decisions on how the adaptation engine should work and do not consider the engine itself as a system subject to adaptation that can dynamically evolve. In this work-in-progress paper we discuss the requirements of a more flexible and evolvable framework for self-adaptation. We propose a conceptual model for realizing this framework, showing its benefits with an application scenario.
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Taherkordi, Amirhosein; Loiret, Frederic; Rouvoy, Romain & Eliassen, Frank
(2011).
A Generic Component-Based Approach for Programming, Composing and Tuning Sensor Software.
Computer journal.
ISSN 0010-4620.
54(8),
p. 1248–1266.
doi:
10.1093/comjnl/bxq102.
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Nguyen, Anh Tuan; Li, Baochun & Eliassen, Frank
(2010).
Quality- and Context-aware Neighbor Selection for Layered Peer-to-Peer Streaming.
IEEE International Conference on Communications.
ISSN 1550-3607.
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Anh, Tuan Nguyen; Li, Baochun & Eliassen, Frank
(2010).
Chameleon: Adaptive Peer-to-Peer Streaming with Network Coding.
IEEE Infocom. Proceedings.
ISSN 0743-166X.
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Bruaset, Are Magnus; Eide, Viktor S. W.; Eliassen, Frank; Gallis, Hans Enger & Tarrou, Christian
(2010).
Spinning off from Simula.
In Tveito, Aslak; Bruaset, Are Magnus & Lysne, Olav (Ed.),
Simula Research Laboratory - by thinking constantly about it.
Springer.
ISSN 978-3-642-01155-9.
p. 613–626.
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Paspallis, Nearchos; Eliassen, Frank; Hallsteinsen, Svein & Papadopoulos, George A.
(2009).
Developing Self-Adaptive Mobile Applications and Services with Separation-of-Concerns.
In Di NItto, Elisabetta; Sassen, Anne-Marie; Traverso, Paolo & Zwegers, Arian (Ed.),
At Your Service: Service-Oriented Computing from an EU Perspective.
MIT Press.
ISSN 9780262042536.
p. 129–158.
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Le Trung, Quan; Taherkordi, Amirhosein; Eliassen, Frank; Pham, Hai Ngoc; Skeie, Tor & Engelstad, Paal Einar
(2009).
DCM-Arch: An Architecture for Data, Control, and Management in Wireless Sensor Networks.
In Awan, Irfan; Younas, Muhammad; Hara, Takahiro & Durresi, Arjan (Ed.),
The IEEE 23rd International Conference on Advanced Information Networking and Applications, AINA 2009, Bradford, United Kingdom, May 26-29, 2009.
IEEE (Institute of Electrical and Electronics Engineers).
ISSN 978-0-7695-3638-5.
p. 898–905.
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Rouvoy, Romain; Barone, Paolo; Ding, Yun; Eliassen, Frank; Hallsteinsen, Svein & Mamelli, Alessandro
[Show all 7 contributors for this article]
(2009).
MUSIC: Middleware Support for Self-Adaptation in Ubiquitous and Service-Oriented Environments.
In Cheng, Betty; de Lemos, Rogerio; Giese, Holger; Inverardi, Paola & Magee, Jeff (Ed.),
Software Engineering for Self-Adaptive Systems (SEfSAS).
Springer.
ISSN 978-3-642-02160-2.
p. 164–182.
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Rouvoy, Romain; Eliassen, Frank; Floch, Jacqueline; Hallsteinsen, Svein & Stav, Erlend
(2008).
Composing Components and Services using a Planning-based Adaptation Middleware.
In Pautasso, Cesare & Tanter, Eric (Ed.),
Proceedings of the 7th International Symposium on Software Composition (SC).
Springer.
ISSN 978-3-540-78788-4.
p. 52–67.
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Oudenstad, Johannes; Rouvoy, Romain; Eliassen, Frank & Gjørven, Eli
(2008).
Brokering Planning Metadata in a P2P Environment.
In Meier, René & Terzis, Sotirios (Ed.),
Proceedings of the 8th IFIP WG 6.1 International Conference on Distributed Applications and Interoperable Systems (DAIS).
Springer.
ISSN 978-3-540-68639-2.
p. 168–181.
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Paspallis, Nearchos; Rouvoy, Romain; Barone, Paolo; Papadopoulos, George A.; Eliassen, Frank & Mamelli, Alessandro
(2008).
A Pluggable and Reconfigurable Architecture for a Context-aware Enabling Middleware System.
In Little, Mark; Montresor, Alberto & Pavlik, Greg (Ed.),
Proceedings of the 10th International Symposium on Distributed Objects, Middleware, and Applications (DOA).
Springer.
ISSN 978-3-540-88870-3.
p. 553–570.
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Mourad, Alia; Eide, Viktor S W; Paspallis, Nearchos; Eliassen, Frank; Hallsteinsen, Svein & Papadopoulos, George A.
(2007).
A Utility-based Adaptivity Model for Mobile Applications.
In Yang, L.T.; Chaudhary, V. & Huang, R. (Ed.),
21st International Conference on Advanced Information Networking and Applications Workshops, 2007, AINAW '07.
IEEE (Institute of Electrical and Electronics Engineers).
ISSN 0-7695-2847-3.
p. 556–563.
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View all works in Cristin
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Eliassen, Frank; Meling, Hein & Vitenberg, Roman
(2015).
Proceedings of the 9th ACM International Conference on Distributed Event-Based Systems, DEBS '15.
Association for Computing Machinery (ACM).
ISBN 978-1-4503-3286-6.
371 p.
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Eliassen, Frank & Kapitza, Rüdiger
(2010).
Distributed Applications and Interoperable Systems.
Springer.
ISBN 3-642-13644-3.
243 p.
View all works in Cristin
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Ali Ahmed, Awadelrahman Mohamedelsadig; Eliassen, Frank & Zhang, Yan
(2023).
Combinatorial Auctions and Graph Neural Networks for Local Energy Flexibility Markets,
Proceedings of 2023 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), 23-23 Octorber, 2023.
IEEE conference proceedings.
ISSN 979-8-3503-9678-2.
p. 1–6.
doi:
10.1109/ISGTEUROPE56780.2023.10407292.
Show summary
This paper proposes a new combinatorial auction framework for local energy flexibility markets, which addresses the issue of prosumers’ inability to bundle multiple flexibility time intervals. To solve the underlying NP-complete winner determination problems, we present a simple yet powerful heterogeneous tri-partite graph representation and design graph neural network-based models. Our models achieve an average optimal value deviation of less than 5% from an off-the-shelf optimization tool and show linear inference time complexity compared to the exponential complexity of the commercial solver. Contributions and results demonstrate the potential of using machine learning to efficiently allocate energy flexibility resources in local markets and solving optimization problems in general.
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Zhang, Min; Eliassen, Frank; Zhang, Yan & Taherkordi, Amirhosein
(2019).
Energy Trading with Demand Response in a
Community-based P2P Energy Market.
[Internet].
https://ieeexplore.ieee.org/document/8909798.
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Pham, Hai Ngoc; Zhang, Yan; Engelstad, Paal Einar; Skeie, Tor & Eliassen, Frank
(2010).
Energy Minimization Approach for Optimal Cooperative Spectrum Sensing in Sensor-Aided Cognitive Radio Networks.
Show summary
In a sensor-aided cognitive radio network, collaborating battery-powered sensors are deployed to aid the network in cooperative spectrum sensing. These sensors consume energy for spectrum sensing and therefore deplete their life-time, thus we study the key issue in minimizing the sensing energy consumed by such group of collaborating sensors. The IEEE P802.22 standard specifies spectrum sensing accuracy by the detection and false alarm probabilities, hence we address the energy minimization problem under this detection accuracy constraint. Firstly, we derive the bounds for the number of sensors to simultaneously guarantee the thresholds for high detection probability and low false alarm probability. With these bounds, we then formulate the optimization problem to find the optimal sensing interval and the optimal number of sensor that minimize the energy consumption. Thirdly, the approximated analytical solutions are derived to solve the optimization accurately and efficiently in polynomial time. Finally, numerical results show that the minimized energy is significantly lower than the energy consumed by a group of randomly selected sensors. The mean absolute error of the approximated optimal sensing interval compared with the exact value is less than 4% and 8% under good and bad SNR conditions, respectively. The approximated optimal number of sensors is shown to be very close to the exact number.
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Provensi, Lucas Luiz; Singh, Abhishek Kumar; Eliassen, Frank & Vitenberg, Roman
(2017).
Self-Organizing Media Streaming for Many-to-many Interaction.
University of Oslo.
ISSN 978-82-7368-433-2.
Full text in Research Archive
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Taherkordi, Amirhosein; Eliassen, Frank & Skeie, Tor
(2011).
Programming Wireless Sensor Networks: From Static to Adaptive Models.
Unipub forlag.
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Published
Nov. 4, 2010 1:51 PM
- Last modified
Jan. 27, 2023 11:57 AM