Frank Eliassen

• 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. s. 1–6. doi: 10.1109/ISGTEUROPE56780.2023.10407292. Vis sammendrag

  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.

• Foroughi, Mehdi; Maharjan, Sabita; Zhang, Yan & Eliassen, Frank (2023). Autonomous Peer-to-Peer Energy Trading in Networked Microgrids: A Distributed Deep Reinforcement Learning Approach. I Harid, Noureddine (Red.), 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.
• Mohammadi, Sara; Eliassen, Frank & Jacobsen, Hans-Arno (2023). Applying Energy Justice Principles to Renewable Energy Trading and Allocation in Multi-Unit Buildings. Energies. ISSN 1996-1073. 16(3). doi: 10.3390/en16031150. Fulltekst i vitenarkiv
• Yang, Yang; Ma, Mulei; Wu, Hequan; Yu, Quan; Zhang, Ping & You, Xiaohu [Vis alle 53 forfattere av denne artikkelen] (2022). 6G Network AI Architecture for Everyone-Centric Customized Services. IEEE Network. ISSN 0890-8044. 37(5), s. 71–80. doi: 10.1109/MNET.124.2200241.
• Lan, Dapeng; Taherkordi, Amirhosein; Eliassen, Frank; Liu, Lei; Delbruel, Stephane & Dustdar, Schahram [Vis alle 7 forfattere av denne artikkelen] (2022). Task Partitioning and Orchestration on Heterogeneous Edge Platforms: The Case of Vision Applications. IEEE Internet of Things Journal. ISSN 2327-4662. 9(10), s. 7418–7432. doi: 10.1109/JIOT.2022.3153970.
• 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. Fulltekst i vitenarkiv Vis sammendrag

  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.

• 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), s. 17–30. doi: 10.1109/TCC.2021.3118563.
• Zhang, Min; Eliassen, Frank; Taherkordi, Amirhosein; Jacobsen, Hans-Arno; Chung, Hwei-Ming & Zhang, Yan (2021). Demand Response Games for Peer-to-Peer Energy Trading with the Hyperledger Blockchain. IEEE Transactions on Systems, Man & Cybernetics. Systems. ISSN 2168-2216. doi: 10.1109/TSMC.2021.3111135.
• 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), s. 3417–3431. doi: 10.1109/TDSC.2021.3096213.
• 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, s. 1–17. doi: 10.1007/s42452-021-04610-8. Fulltekst i vitenarkiv Vis sammendrag

  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.

• 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), s. 12695–12700. doi: 10.1016/j.ifacol.2020.12.1859.
• Chung, Hwei-Ming; Maharjan, Sabita; Zhang, Yan; Eliassen, Frank & Yuan, Tingting (2020). Edge Intelligence Empowered UAVs for Automated Wind Farm Monitoring in Smart Grids. I NN, NN (Red.), 2020 IEEE Global Communications Conference (GLOBECOM). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-7281-8298-8. doi: 10.1109/GLOBECOM42002.2020.9322269.
• 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. s. 31–35. doi: 10.1109/ISGT-Europe47291.2020.9248761. Vis sammendrag

  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.

• 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. Vis sammendrag

  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.

• Lan, Dapeng; Taherkordi, Amir; Eliassen, Frank; Chen, Zhuang & Liu, Lei (2020). Deep Reinforcement Learning for Intelligent Migration of Fog Services in Smart Cities. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 12453, s. 230–244. doi: 10.1007/978-3-030-60239-0_16.
• 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. Fulltekst i vitenarkiv
• 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. Fulltekst i vitenarkiv
• 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), s. 7760–7771. doi: 10.1109/TITS.2020.3008279.
• 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.
• 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), s. 3032–3043. doi: 10.1109/TII.2020.3004816.
• Zhang, Min; Eliassen, Frank; Taherkordi, Amirhosein; Jacobsen, Hans-Arno; Chung, Hwei-Ming & Zhang, Yan (2019). Energy Trading with Demand Response in a Community-based P2P Energy Market, 2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-5386-8099-5. doi: 10.1109/SmartGridComm.2019.8909798.
• Lan, Dapeng; Pang, Zhibo; Fischione, Carlo; Liu, Yu; Taherkordi, Amirhosein & Eliassen, Frank (2019). Latency Analysis of Wireless Networks for Proximity Services in Smart Home and Building Automation: The Case of Thread. IEEE Access. ISSN 2169-3536. 7, s. 4856–4867. doi: 10.1109/ACCESS.2018.2888939. Fulltekst i vitenarkiv
• 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, s. 1–6. doi: 10.1109/ICME.2018.8486565.
• Taherkordi, Amirhosein; Eliassen, Frank; McDonald, Michael & Horn, Geir (2018). Context-Driven and Real-Time Provisioning of Data-Centric IoT Services in the Cloud. ACM Transactions on Internet Technology. ISSN 1533-5399. 19(1), s. 7:1–7:24. doi: 10.1145/3151006.
• Velazquez-Garcia, Francisco Javier; Halvorsen, Pål; Stensland, Håkon Kvale & Eliassen, Frank (2018). Autonomic adaptation of multimedia content adhering to application mobility. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 10853 LNCS, s. 153–168. doi: 10.1007/978-3-319-93767-0_11. Fulltekst i vitenarkiv
• 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), s. 1342–1356. doi: 10.1109/TPDS.2018.2791599.
• Taherkordi, Amirhosein; Eliassen, Frank & Horn, Geir Henrik (2017). From IoT big data to IoT Big Services. I Shin, Sung Y. (Red.), Proceedings of the Symposium on Applied Computing. Association for Computing Machinery (ACM). ISSN 978-1-4503-4486-9. s. 485–491. doi: 10.1145/3019612.3019700.
• Taherkordi, Amirhosein & Eliassen, Frank (2017). Data-Centric IoT Services Provisioning in Fog-Cloud Computing Systems. I Bilof, Randall (Red.), Second International Conference on Internet-of-Things Design and Implementation. Association for Computing Machinery (ACM). ISSN 978-1-4503-4966-6. s. 317–318. doi: 10.1145/3054977.3057295.
• Provensi, Lucas Luiz; Eliassen, Frank & Vitenberg, Roman (2017). A Cloud-Assisted Tree-Based P2P System for Low Latency Streaming. I Hariri, Salim (Red.), 2017 International Conference on Cloud and Autonomic Computing. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-5386-1939-1. s. 172–183. doi: 10.1109/ICCAC.2017.24. Fulltekst i vitenarkiv
• Velazquez-Garcia, Francisco Javier & Eliassen, Frank (2017). DAMPAT: Dynamic Adaptation of Multimedia Presentations in Application Mobility. I Bulterman, Dick C.A.; Kankanhalli, Mohan; Sheu, Phillip C.-Y. & Tsai, Jeffrey J.P. (Red.), 2017 IEEE International Symposium on Multimedia. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-5386-2937-6. s. 312–317. doi: 10.1109/ISM.2017.56.
• 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.
• Taherkordi, Amirhosein & Eliassen, Frank (2016). Scalable modeling of cloud-based IoT services for smart cities. I Kumar, Mohan & Seneviratne, Aruna (Red.), 2016 IEEE International Conference on Pervasive Computing and Communication Workshops. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-5090-1941-0. doi: 10.1109/PERCOMW.2016.7457098.
• Dar, Kashif Sana; Taherkordi, Amirhosein & Eliassen, Frank (2016). Enhancing Dependability of Cloud-Based IoT Services through Virtualization. I Abdelzaher, Tarek; Cao, Jiannong & Marron, Pedro Jose (Red.), 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. s. 106–116. doi: 10.1109/IoTDI.2015.38.
• Horn, Geir Henrik; Eliassen, Frank; Taherkordi, Amirhosein; Venticinque, Salvatore; Di Martino, Beniamino & Bücher, Monika [Vis alle 7 forfattere av denne artikkelen] (2016). An architecture for using commodity devices and smart phones in health systems. Proceedings of the IEEE Symposium on Computers and Communications. ISSN 1530-1346. 2016-August, s. 255–260. doi: 10.1109/ISCC.2016.7543749.
• Orfanus, Dalimir; De Freitas, Edison Pignaton & Eliassen, Frank (2016). Self-Organization as a Supporting Paradigm for Military UAV Relay Networks. IEEE Communications Letters. ISSN 1089-7798. 20(4), s. 804–807. doi: 10.1109/LCOMM.2016.2524405.
• 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), s. 976–982. doi: 10.1109/CloudCom.2014.158.
• Taherkordi, Amirhosein; Johansen, Christian; Eliassen, Frank & Römer, Kay (2015). Tokenit: Designing State-Driven Embedded Systems through Tokenized Transitions. I OConner, Lisa (Red.), Distributed Computing in Sensor Systems (DCOSS). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-4799-8856-3. s. 52–61. doi: 10.1109/DCOSS.2015.36.
• Maharjan, Sabita; Zhang, Yan; Gjessing, Stein; Ulleberg, Øystein & Eliassen, Frank (2015). Providing Microgrid Resilience during Emergencies using Distributed Energy Resources. I Tiedemann, Ed; Krishnaswamy, Dilip & Prasad, Neeli R. (Red.), 2015 IEEE Global Communications Conference. IEEE Press. ISSN 978-1-4673-9526-7. doi: 10.1109/GLOCOMW.2015.7414031.
• Taherkordi, Amirhosein & Eliassen, Frank (2014). Models@run.time for Creating in-Cloud Dynamic Cyber-Physical Ecosystems. I Cai, Wentong (Red.), CloudCom 2014, IEEE 6th International Conference on Cloud Computing Technology and Science, Singapore, 15-18. December, 2014 . IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-4799-4093-6. s. 976–982. doi: 10.1109/CloudCom.2014.158.
• Taherkordi, Amirhosein & Eliassen, Frank (2014). Towards Independent in-Cloud Evolution of Cyber-Physical Systems. I Azumi, Takuya & Lee, Victor (Red.), The 2nd IEEE International Conference on Cyber-Physical Systems, Networks, and Applications. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-4799-5387-5. s. 19–24. doi: 10.1109/CPSNA.2014.12.
• Orfanus, Dalimir; Eliassen, Frank & Pignaton de Freitas, Edison (2014). Self-Organizing Relay Network Supporting Remotely Deployed Sensor Nodes in Military Operations. I Koucheryavy, Yevgeni & Makarov, Sergey (Red.), Proceedings of 6th IEEE International Congress on Ultra Modern Telecommunications and Control Systems (ICUMT). IEEE conference proceedings. ISSN 978-1-4799-5291-5. s. 326–333. doi: 10.1109/ICUMT.2014.7002122.
• 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, s. 145–159. doi: 10.1016/j.pmcj.2014.11.005. Vis sammendrag

  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.

• Orfanus, Dalimir; Janacik, Peter & Eliassen, Frank (2013). Integration Framework for Simulation Tools to Engineer Emergent Self-Organizing Behavior. I Al-Dabass, David; Orsoni, Alessandra; Yunus, Jasmy; Cant, Richard & Ibrahim, Zuwairie (Red.), UKSim 15th International Conference on Computer Modelling and Simulation. IEEE Press. ISSN 978-1-4673-6421-8. s. 335–340. doi: 10.1109/UKSim.2013.53.
• 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), s. 1205–1218. doi: 10.1002/spe.1125.
• 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), s. 26–37. doi: 10.1145/2537728.2537731.
• Provensi, Lucas Luiz; Eliassen, Frank; Vitenberg, Roman & Rouvoy, Romain (2013). Improving context interpretation by using fuzzy policies: the case of adaptive video streaming. I Sung Y., Shin & José Carlos, Maldonado (Red.), Proceedings of the 28th Annual ACM Symposium on Applied Computing. Association for Computing Machinery (ACM). ISSN 978-1-4503-1656-9. s. 415–422. doi: 10.1145/2480362.2480447. Vis sammendrag

  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.

• Taherkordi, Amirhosein; Loiret, Frederic; Rouvoy, Romain & Eliassen, Frank (2013). Optimizing Sensor Network Reprogramming via In Situ Reconfigurable Components. ACM transactions on sensor networks. ISSN 1550-4867. 9(2). doi: 10.1145/2422966.2422971.
• Hallsteinsen, Svein Olav; Geihs, Kurt; Paspallis, Nearchos; Eliassen, Frank; Horn, Geir Henrik & Lorenzo, Jorge [Vis alle 8 forfattere av denne artikkelen] (2012). A development framework and methodology for self-adapting applications in ubiquitous computing environments. Journal of Systems and Software. ISSN 0164-1212. 85(12), s. 2840–2859. doi: 10.1016/j.jss.2012.07.052. Vis sammendrag

  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.

• Le-Trung, Quan; Engelstad, Paal Einar; Skeie, Tor; Eliassen, Frank & Taherkordi, Amirhosein (2011). Mobility Management for All-IP Mobile Networks. I Makaya, Christian & Pierre, Samuel (Red.), Emerging Wireless Networks - Concepts, Techniques, Applications. Taylor & Francis. ISSN 9781439821350.
• Orfanus, Dalimir; Janacik, Peter; Eliassen, Frank & Orten, Pål (2011). High-Level Construction of Emergent Self-Organizing Behavior in Massively Distributed Embedded Systems. I de Carvalho, Andre; Zomaya, Albert & Yager, Ronald (Red.), Third World Congress on Nature and Biologically Inspired Computing (NaBIC 2011). IEEE conference proceedings. ISSN 978-1-4577-1122-0. s. 335–341. doi: 10.1109/nabic.2011.6089467.
• Dar, Kashif Sana; Taherkordi, Amirhosein; Vitenberg, Roman; Rouvoy, Romain & Eliassen, Frank (2011). Adaptable Service Composition for Very-Large-Scale Internet of Things Systems. I Ben Mokhtar, Sonia (Red.), Middleware 2011: Proceedings of the Workshop on Posters and Demos Track. ACM Publications. ISSN 978-1-4503-1073-4.
• Taherkordi, Amirhosein; Eliassen, Frank; Romero, Daniel & Rouvoy, Romain (2011). RESTful Service Development for Resource-constrained Environments. I Wilde, Erik (Red.), REST: From Research to Practice. Springer. ISSN 978-1-4419-8302-2. s. 221–236. doi: 10.1007/978-1-4419-8303-9_9.
• Nguyen, Anh Tuan; Eliassen, Frank & Welzl, Michael (2011). Quality-aware Membership Management for Layered Peer-to-Peer Streaming. I N, N (Red.), CCNC 2011: IEEE Consumer Communications and Networking Conference; 9-12 Jan. 2011, Las Vegas. IEEE conference proceedings. ISSN 978-1-4244-8790-5. s. 720–724. doi: 10.1109/ccnc.2011.5766585.
• Nguyen, Anh Tuan; Li, Baochun; Welzl, Michael & Eliassen, Frank (2011). Stir: Spontaneous Social Peer-to-Peer Streaming. I Ni, Lionel & Zhang, WenJun (Red.), 2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE Sarnoff Symposium. ISSN 978-1-4577-0248-8. s. 816–821. doi: 10.1109/infcomw.2011.5928925.
• Dar, Kashif Sana; Taherkordi, Amirhosein; Rouvoy, Romain & Eliassen, Frank (2011). Adaptable Service Composition for Very-Large-Scale Internet of Things Systems. I Eyers, David (Red.), Proceedings of the 8th Middleware Doctoral Symposium of the 12th ACM/IFIP/USENIX International Middleware Conference. ACM Publications. ISSN 978-1-4503-1072-7. doi: 10.1145/2093190.2093192.
• Provensi, Lucas Luiz & Eliassen, Frank (2011). Towards a Flexible and Evolvable Framework for Self-Adaptation. Electronic Communications of the EASST. ISSN 1863-2122. 43. Vis sammendrag

  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.

• 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), s. 1248–1266. doi: 10.1093/comjnl/bxq102.
• 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.
• Anh, Tuan Nguyen; Li, Baochun & Eliassen, Frank (2010). Chameleon: Adaptive Peer-to-Peer Streaming with Network Coding. IEEE Infocom. Proceedings. ISSN 0743-166X.
• 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. I Chen, Hsiao-Hwa & Daneshmand, Mahmoud (Red.), Proceedings of the 2010 The 5th Annual ICST Wireless Internet Conference WICON 2010. Central European University Press. ISSN 978-963-9799-86-8.
• Bruaset, Are Magnus; Eide, Viktor S. W.; Eliassen, Frank; Gallis, Hans Enger & Tarrou, Christian (2010). Spinning off from Simula. I Tveito, Aslak; Bruaset, Are Magnus & Lysne, Olav (Red.), Simula Research Laboratory - by thinking constantly about it. Springer. ISSN 978-3-642-01155-9. s. 613–626.
• Taherkordi, Amirhosein & Eliassen, Frank (2010). An event-driven approach to address reflectivity in sensornets. I Venkatasubramanian, Nalini (Red.), Proceedings of the 9th International Workshop on Adaptive and Reflective Middleware. ACM Publications. ISSN 978-1-4503-0455-9. s. 52–53.
• Taherkordi, Amirhosein; Rouvoy, Romain & Eliassen, Frank (2010). A component-based approach for service distribution in sensor networks. I Hughes, Danny (Red.), Proceedings of the 5th International Workshop on Middleware Tools, Services and Run-Time Support for Sensor Networks. ACM Publications. ISSN 978-1-4503-0454-2. s. 22–28.
• Alia, Mourad; Beauvois, Mikael; Davin, Yann; Rouvoy, Romain & Eliassen, Frank (2010). Components and Aspects Composition Planning for Ubiquitous Adaptive Services. I O'Conner, Lisa (Red.), Proceedring of the 36th EUROMICRO Conference on Software Engineering and Advanced Applications. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-0-7695-4170-9. s. 231–234.
• Nguyen, Anh Tuan; Li, Baochun & Eliassen, Frank (2010). Quality- and Context-Aware Neighbor Selection for Layered Peer-to-Peer Streaming. I Pandor, N. (Red.), 2010 IEEE International Conference on Communications (ICC 2010). IEEE conference proceedings. ISSN 978-1-4244-6404-3. s. 1–5.
• Nguyen, Anh Tuan; Li, Baochun & Eliassen, Frank (2010). Chameleon: Adaptive Peer-to-Peer Streaming with Network Coding. I Mandyam, Giridhar (Red.), The 29th IEEE Conference on Computer Communications (INFOCOM 2010). IEEE conference proceedings. ISSN 978-1-4244-5838-7. s. 1–9.
• Romero, Daniel; Hermosillo, Gabriel; Taherkordi, Amirhosein; Nzekwa, Russel; Rouvoy, Romain & Eliassen, Frank (2010). RESTful Integration of Heterogeneous Devices in Pervasive Environments. I Eliassen, Frank & Kapitza, Rüdiger (Red.), Distributed Applications and Interoperable Systems. Springer. ISSN 3-642-13644-3. s. 1–14.
• Taherkordi, Amirhosein; Loiret, Frederic; Abdolrazaghi, Azadeh; Rouvoy, Romain; Le-Trung, Quan & Eliassen, Frank (2010). Programming Sensor Networks Using REMORA Component Model, Distributed Computing in Sensor Systems. Springer. ISSN 3-642-13650-8. s. 45–62.
• Paspallis, Nearchos; Eliassen, Frank; Hallsteinsen, Svein & Papadopoulos, George A. (2009). Developing Self-Adaptive Mobile Applications and Services with Separation-of-Concerns. I Di NItto, Elisabetta; Sassen, Anne-Marie; Traverso, Paolo & Zwegers, Arian (Red.), At Your Service: Service-Oriented Computing from an EU Perspective. MIT Press. ISSN 9780262042536. s. 129–158.
• Taherkordi, Amirhosein; Le-Trung, Quan; Rouvoy, Romain & Eliassen, Frank (2009). WiSeKit: A Distributed Middleware to Support Application-Level Adaptation in Sensor Networks. I Goos, Gerhard (Red.), Distributed Applications and Interoperable Systems. Springer. ISSN 978-3-642-02163-3. s. 44–58.
• Taherkordi, Amirhosein; Rouvoy, Romain; Le-Trung, Quan & Eliassen, Frank (2009). Supporting lightweight adaptations in context-aware wireless sensor networks. I Bouroche, Mélanie (Red.), Proceedings of the 1st International Workshop on Context-Aware Middleware and Services. ACM Publications. ISSN 978-1-60558-525-3. s. 43–48.
• Nguyen, Anh Tuan & Eliassen, Frank (2009). An Efficient Solution for Max-min Fair Rate Allocation in P2P Simulation. I Sokolov, B. (Red.), 2009 International Conference on Ultra Modern Telecommunications ICUMT 2009 and Workshops Proceedings. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-4244-3941-6. s. 1–5.
• 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. I Awan, Irfan; Younas, Muhammad; Hara, Takahiro & Durresi, Arjan (Red.), 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. s. 898–905.
• Rouvoy, Romain; Eliassen, Frank & Beauvois, Mikael (2009). Dynamic Planning and Weaving of Dependability Concerns for Self-Adaptive Ubiquitous Services. I Göschka, Karl; Hallsteinsen, Svein; Oliveira, Rui & Romanovsky, Alexander (Red.), Track on Dependable and Adaptive Distributed Systems (DADS) of the 24th ACM Symposium on Applied Computing (SAC). ACM Publications. ISSN 978-1-60558-166-8. s. 1021–1028.
• Rouvoy, Romain; Barone, Paolo; Ding, Yun; Eliassen, Frank; Hallsteinsen, Svein & Mamelli, Alessandro [Vis alle 7 forfattere av denne artikkelen] (2009). MUSIC: Middleware Support for Self-Adaptation in Ubiquitous and Service-Oriented Environments. I Cheng, Betty; de Lemos, Rogerio; Giese, Holger; Inverardi, Paola & Magee, Jeff (Red.), Software Engineering for Self-Adaptive Systems (SEfSAS). Springer. ISSN 978-3-642-02160-2. s. 164–182.
• Pham, Hai Ngoc; Zhang, Y.; Engelstad, Paal Einar; Skeie, Tor & Eliassen, Frank (2009). Optimal Cooperative Spectrum Sensing in Cognitive Sensor Networks. I IWCMC, IWCMC (Red.), The 5th International Wireless Communications and Mobile Computing Conference (IWCMC '09). ACM Publications. ISSN 978-1-60558-569-7. s. 1073–1079.
• Geihs, K; Barone, P; Eliassen, Frank; Floch, J; Fricke, R & Gjorven, E [Vis alle 14 forfattere av denne artikkelen] (2009). A comprehensive solution for application-level adaptation. Software, Practice & Experience. ISSN 0038-0644. 39(4), s. 385–422. doi: 10.1002/spe.900.
• Le-Trung, Quan; Engelstad, Paal Einar; Pham, Vinh; Skeie, Tor; Taherkordi, Amirhosein & Eliassen, Frank (2009). Providing internet connectivity and mobility management for MANETs. International Journal of Web Information Systems. ISSN 1744-0084. 5(2), s. 239–263. doi: 10.1108/17440080910968472.
• Rouvoy, Romain; Eliassen, Frank; Floch, Jacqueline; Hallsteinsen, Svein & Stav, Erlend (2008). Composing Components and Services using a Planning-based Adaptation Middleware. I Pautasso, Cesare & Tanter, Eric (Red.), Proceedings of the 7th International Symposium on Software Composition (SC). Springer. ISSN 978-3-540-78788-4. s. 52–67.
• Rouvoy, Romain; Beauvois, Mikael & Eliassen, Frank (2008). Dynamic Aspect Weaving using a Planning-based Adaptation Middleware. I Kapitza, Rüdiger & Reiser, Hans P. (Red.), Proceedings of the 2nd International DisCoTec Workshop on Middleware-Application Interaction (MAI). Association for Computing Machinery (ACM). ISSN 978-1-60558-204-7. s. 31–36.
• Oudenstad, Johannes; Rouvoy, Romain; Eliassen, Frank & Gjørven, Eli (2008). Brokering Planning Metadata in a P2P Environment. I Meier, René & Terzis, Sotirios (Red.), Proceedings of the 8th IFIP WG 6.1 International Conference on Distributed Applications and Interoperable Systems (DAIS). Springer. ISSN 978-3-540-68639-2. s. 168–181.
• 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. I Little, Mark; Montresor, Alberto & Pavlik, Greg (Red.), Proceedings of the 10th International Symposium on Distributed Objects, Middleware, and Applications (DOA). Springer. ISSN 978-3-540-88870-3. s. 553–570.
• Rouvoy, Romain; Beauvois, Mikael; Lozano, Laura; Lorenzo, Jorge & Eliassen, Frank (2008). MUSIC: an Autonomous Platform Supporting Self-Adaptive Mobile Applications. I Riva, Oriana & Veiga, Luìs (Red.), Proceedings of the 1st International Middleware Workshop on Mobile Middleware: Embracing the Personal Communication Device (MobMid). Association for Computing Machinery (ACM). ISSN 978-1-60558-362-4.
• Gjørven, Eli; Eliassen, Frank & Rouvoy, Romain (2008). Experiences from Developing a Component Technology Agnostic Adaptation Framework. I Chaudron, Michel; Szyperski, Clemens & Reussner, R (Red.), Component-Based Software Engineering 11th International Symposium, CBSE 2008. Springer. ISSN 978-3-540-87890-2. s. 230–245.
• Gjørven, Eli; Eliassen, Frank & Rouvoy, Romain (2008). Cross-layer Self-adaptation of Service-oriented Architectures. I Göschka, Karl; Dustdar, Schahram; Leymann, Frank & Tosic, Vladimir (Red.), Proceedings of the 3rd Workshop on Middleware for Service Oriented Computing (MW4SOC 2008). ACM Publications. ISSN 978-1-60558-368-6. s. 37–42.
• Taherkordi, Amirhosein; Rouvoy, Romain; Le Trung, Quan & Eliassen, Frank (2008). A self-adaptive context processing framework for wireless sensor networks. I Michiels, Sam (Red.), Proceedings of the 3rd International Workshop on Middleware for Sensor Networks. ACM Publications. ISSN 978-1-60558-366-2. s. 7–13.
• Taherkordi, Amirhosein; Eliassen, Frank; Rouvoy, Romain & Le Trung, Quan (2008). ReWiSe: A New Component Model for Lightweight Software Reconfiguration in Wireless Sensor Networks. I Meersman, Robert (Red.), On the Move to Meaningful Internet Systems: OTM2008Workshops. Springer. ISSN 978-3-540-88874-1. s. 415–425.
• Taherkordi, Amirhosein; Mohammadi, Reza & Eliassen, Frank (2008). A Communication-Efficient Distributed Clustering Algorithm for Sensor Networks. I Conner, Lisa (Red.), 22nd International Conference on Advanced Information Networking and Applications Workshops/Symposia. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-0-7695-3096-3.
• Rouvoy, Romain; Vitenberg, Roman & Eliassen, Frank (2008). Enhancing Planning-Based Adaptation Middleware with Support for Dependability: a Case Study. Electronic Communications of the EASST. ISSN 1863-2122. 11.
• Geihs, Kurt; Barone, Paolo; Eliassen, Frank; Floch, Jacqueline; Fricke, Rolf & Gjørven, Eli [Vis alle 13 forfattere av denne artikkelen] (2008). A Comprehensive Solution for Application-Level Adaptation. Software, Practice & Experience. ISSN 0038-0644. doi: 10.1002/spe.900.
• Amundsen Lundesgaard, Sten & Eliassen, Frank (2008). A Resource and Context Model for Mobile Middleware. Personal and Ubiquitous Computing. ISSN 1617-4909. 12, s. 143–153.
• Mourad, Alia; Eide, Viktor S W; Paspallis, Nearchos; Eliassen, Frank; Hallsteinsen, Svein & Papadopoulos, George A. (2007). A Utility-based Adaptivity Model for Mobile Applications. I Yang, L.T.; Chaudhary, V. & Huang, R. (Red.), 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. s. 556–563.
• Amundsen, Sten Lundesgaard; Solberg, Arnor; Oldevik, Jon; France, Robert; Aagedal, Jan Øivind & Eliassen, Frank (2007). Construction of and Execution of Adaptable Applications using an Aspect-Oriented and Model Driven Approach. I Indulska, J & Raymond, K (Red.), Distributed Applications and Interoperable Systems (DAIS). Springer. ISSN 3-540-72881-3. s. 76–89.

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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 s.
• Eliassen, Frank & Kapitza, Rüdiger (2010). Distributed Applications and Interoperable Systems. Springer. ISBN 3-642-13644-3. 243 s.

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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. [Internett]. https://ieeexplore.ieee.org/document/8909798.
• 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. Vis sammendrag

  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.

• Eliassen, Frank (2008). Grensebyer - en innledning. Heimen - Lokal og regional historie. ISSN 0017-9841. 54(3), s. 195–196.
• 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. Fulltekst i vitenarkiv
• Taherkordi, Amirhosein; Eliassen, Frank & Skeie, Tor (2011). Programming Wireless Sensor Networks: From Static to Adaptive Models. Unipub forlag.

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