Otto Johan Anshus

• Guegan, Loic; Rais, Issam & Anshus, Otto Johan (2023). Towards Data Dissemination Policy Prediction for Constrained Environments Using Analytics. In Kanhere, Salil; Silvestri, Simone; Saukh, Olga & Nikoletseas, Sotiris (Ed.), Proceeding of the 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things - DCOSS-IoT 2023. IEEE Computer Society Digital Library. ISSN 979-8-3503-4649-7. p. 68–70. doi: 10.1109/DCOSS-IoT58021.2023.00019. Full text in Research Archive
• Guegan, Loic; Rais, Issam & Anshus, Otto Johan (2023). Validation of ESDS Using Epidemic-Based Data Dissemination Algorithms. In Kanhere, Salil; Silvestri, Simone; Saukh, Olga & Nikoletseas, Sotiris (Ed.), Proceeding of the 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things - DCOSS-IoT 2023. IEEE Computer Society Digital Library. ISSN 979-8-3503-4649-7. p. 277–284. doi: 10.1109/DCOSS-IoT58021.2023.00054. Full text in Research Archive
• Tofaily, Salma; Rais, Issam & Anshus, Otto Johan (2023). Quantifying the variability of power and energy consumption for IoT edge nodes . In Kanhere, Salil; Silvestri, Simone; Saukh, Olga & Nikoletseas, Sotiris (Ed.), Proceeding of the 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things - DCOSS-IoT 2023. IEEE Computer Society Digital Library. ISSN 979-8-3503-4649-7. p. 577–584. doi: 10.1109/DCOSS-IoT58021.2023.00094. Full text in Research Archive
• Kristensen, Steffen Ole Randrup; Bjørndalen, John Markus; Rais, Issam; Ha, Hoai Phuong & Anshus, Otto Johan (2023). Weather-aware Wake-up of Sleeping Cyber-Physical IoT Nodes. In Kanhere, Salil; Silvestri, Simone; Saukh, Olga & Nikoletseas, Sotiris (Ed.), Proceeding of the 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things - DCOSS-IoT 2023. IEEE Computer Society Digital Library. ISSN 979-8-3503-4649-7. p. 445–452. doi: 10.1109/DCOSS-IoT58021.2023.00076. Full text in Research Archive
• Michalik, Lukasz Sergiusz; Guegan, Loic; Rais, Issam; Anshus, Otto Johan & Bjørndalen, John Markus (2022). LoRaLitE: LoRa protocol for Energy-Limited environments. In Calzarossa, Maria Carla; Gelenbe, Erol; Grochla, Krzysztof & Massari, Luisa (Ed.), Proceedings of the 30th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems - MASCOTS 2022. IEEE Computer Society Digital Library. ISSN 978-1-6654-5580-0. p. 73–80. doi: 10.1109/MASCOTS56607.2022.00018.
• Tollefsen, Roberth & Anshus, Otto Johan (2022). Detecting the Arrival of an Update at Mostly Sleeping Cyber-Physical IoT Nodes. IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS). ISSN 2325-2936. p. 175–182. doi: 10.1109/DCOSS54816.2022.00040. Show summary

  IoT and cyber-physical nodes with long operational lifetimes will be updated with some frequency after deployment. However, nodes deployed to environments like the arctic tundra are faced with several resource constraints, including a very small energy budget. To reduce energy consumption, the nodes are mostly sleeping. This makes updates more difficult because the update procedure can be interrupted by the nodes deciding to sleep or shut down. In addition, the updating system itself should be energy efficient.We report on an approach and a prototype system for a single phase of the updating, concerned only with the problem of detecting that an update has arrived locally at a node.The nodes can expect to receive updates at different update schedules. A series of performance measuring experiments were conducted on the update detection system to document how it behaves for different update schedules.We conclude that to find a sweet spot for different update schedules with regard to the number of activations of the detection mechanism, and by implication also the energy consumption, and the time it takes before and update is detected, the update detection system must adapt to the different update schedules. While several factors impact this, the most significant effect comes from varying the time between activations of the detection mechanism.

• Rais, Issam; Guegan, Loic & Anshus, Otto Johan (2022). Impact of loosely coupled data dissemination policies for resource challenged environments. In Fazio, Maria; Panda, Dhabaleswar K.; Prodan, Radu; Cardellini, Valeria; Kantarci, Burak; Rana, Omer & Villari, Massimo (Ed.), Proceedings of the 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid 2022). IEEE conference proceedings. ISSN 978-1-6654-9957-6. doi: 10.1109/CCGrid54584.2022.00062.
• Kristensen, Steffen Ole Randrup; Rais, Issam; Bjørndalen, John Markus; Ha, Hoai Phuong & Anshus, Otto Johan (2021). Impact of image compression on CNN performance metrics for CPS nodes at the Arctic Tundra. In Zheng, James; Liu, Xiao; Luan, Tom Hao; Jayaraman, Prem Prakash; Dai, Haipeng; Mitra, Karan; Qin, Kai; Ranjan, Rajiv & Wen, Sheng (Ed.), Proceedings of Cybermatics 2021 iThings 2021 GreenCom 2021 CPSCom 2021 SmartData 2021. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-7281-7647-5. p. 290–297.
• Dinh, Thi Thuy Nga; Tveito, Øystein; Ha, Hoai Phuong & Anshus, Otto (2021). Implementation and Performance Analysis for Energy Harvesting-based Wireless Sensor Networks, IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-6654-3554-3. doi: 10.1109/IECON48115.2021.9589684.
• Tollefsen, Roberth; Rais, Issam; Bjørndalen, John Markus; Ha, Hoai Phuong & Anshus, Otto (2021). Distribution of Updates to IoT Nodes in a Resource-Challenged Environment. In Lefèvre, Laurent; Patterson, Stacy; Lee, Young Choon; Shen, Haiying; Ilager, Shashikant; Goudarzi, Mohammad; Toosi, Adel N. & Buyya, Rajkumar (Ed.), Proceedings of the 21st IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid 2021). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-7281-9586-5. p. 684–689. doi: 10.1109/CCGrid51090.2021.00082. Show summary

  IoT nodes need to be updated after deployment. However, doing so for nodes deployed to resource-challenged environments, like the arctic tundra, is a challenge. Because humans as the common case cannot physically visit the nodes, updating must be done from a remote update service over a back-haul data network. However, most nodes are not in range of a back-haul data network. Even when nodes are in range, they probably sleep to conserve energy and the remote cloud back-end therefore cannot communicate with them.We report on an approach and a prototype system for distributing updates from a cloud update distribution service to nodes. We assume that nodes carry one or several local area network technologies supporting short-lived point-to-point ad-hoc communication between two nodes at a time in range of each other. We assume that a single node in each neighborhood has a back-haul network, and delegate to this node to further distribute updates inside the neighborhood.A series of performance measuring experiments were conducted on the update distribution system when it executes on nodes with behaviors from always-on to mostly-off. We document how the distribution system behaves through a set of performance metrics. The results are very sensitive to the behavior of the nodes.

• Murphy, Michael J.; Tveito, Øystein; Kleiven, Eivind Flittie; Rais, Issam; Soininen, Eeva M & Bjørndalen, John Markus [Show all 7 contributors for this article] (2021). Experiences Building and Deploying Wireless Sensor Nodes for the Arctic Tundra. In Lefèvre, Laurent; Patterson, Stacy; Lee, Young Choon; Shen, Haiying; Ilager, Shashikant; Goudarzi, Mohammad; Toosi, Adel N. & Buyya, Rajkumar (Ed.), Proceedings of the 21st IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid 2021). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-7281-9586-5. p. 376–385. doi: 10.1109/CCGrid51090.2021.00047. Full text in Research Archive Show summary

  The arctic tundra is most sensitive to climate change. The change can be quantified from observations of the fauna, flora and weather conditions. To do observations at sufficient spatial and temporal resolution, ground-based observation nodes with sensors are needed.However, the arctic tundra is resource-limited with regards to energy, data networks, and humans. There are also regulatory and practical obstacles. Consequently, observation nodes must be small and unobtrusive, have a year or longer operational lifetime from small batteries, and be able to report results and receive software updates over scarce back-haul networks.We describe the architecture, design, and implementation of prototype observation nodes deployed to the arctic tundra for the periods August 2019 to July 2020 and August 2020 to July 2021.For the 2019 deployment, ten nodes were each placed inside ten existing camera traps. A camera trap is a box with a wildlife camera taking pictures of rodents when they enter the box from tunnels under snow and ice. For the 2020 deployment, eight nodes were located pairwise inside four camera traps.Each node measures carbon dioxide level and temperature inside the camera trap during the winter season. A node reports its state and observational data each night over a commercial low power IoT telecom back-haul network, if available.We report on the issues encountered doing actual deployments of the prototype nodes. For each issue, we describe the reason for why it happened, relate it to the architecture, design and implementation, and explain what we did about it.

• Rais, Issam; Anshus, Otto; Bjørndalen, John Markus; Balouek-Thomert, Daniel & Parashar, Manish (2020). Trading Data Size and CNN Confidence Score for Energy Efficient CPS Node Communications. In Buyya, Rajkumar; Lefevre, Laurent & Varela, Carlos A. (Ed.), Proceedings of the 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-7281-6095-5. p. 469–478. doi: DOI%2010.1109/CCGrid49817.2020.00-46.
• Rais, Issam; Bjørndalen, John Markus; Ha, Hoai Phuong; Jensen, Ken-arne; Michalik, Lukasz Sergiusz & Mjøen, Håvard [Show all 8 contributors for this article] (2019). UAVs as a leverage to provide energy and network for Cyber-Physical Observation Units on the Arctic Tundra. In Nikoletseas, Sotiris; Trigoni, Niki; Abdelzaher, Tarek & Picco, Gian Pietro (Ed.), Proceedings of the 15th Annual International Conference on Distributed Computing in Sensor Systems - DCOSS 2019. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-7281-0570-3. p. 625–632. doi: 10.1109/dcoss.2019.00114.
• Bjørndalen, John Markus; Vinter, Brian & Anshus, Otto (2019). aPyCSP - Asynchronous PyCSP Using Python Coroutines and Asyncio. In Pedersen, Jan Bækgaard; Broenink, Jan F.; Vella, Kevin; Chalmers, Kevin; Vinter, Brian; Welch, Peter h.; Smith, Marc L. & Skovhede, Kenneth (Ed.), Proceedings of the Communicating Process Architectures 2017 & 2018. IOS Press. ISSN 978-1-61499-948-5. p. 281–297. doi: 10.3233/978-1-61499-949-2-281.
• Michalik, Lukasz Sergiusz; Murphy, Michael J.; Bjørndalen, John Markus & Anshus, Otto (2019). CSP at the Cyber-Physical Edge. In Pedersen, Jan Bækgaard; Broenink, Jan F.; Vella, Kevin; Chalmers, Kevin; Vinter, Brian; Welch, Peter h.; Smith, Marc L. & Skovhede, Kenneth (Ed.), Proceedings of the Communicating Process Architectures 2017 & 2018. IOS Press. ISSN 978-1-61499-948-5. p. 377–388. doi: 10.3233/978-1-61499-949-2-377. Full text in Research Archive
• Ha, Hoai Phuong; Anshus, Otto & Umar, Ibrahim (2019). Efficient concurrent search trees using portable fine-grained locality. IEEE Transactions on Parallel and Distributed Systems. ISSN 1045-9219. 30(7), p. 1580–1595. doi: 10.1109/TPDS.2019.2892968. Full text in Research Archive
• Bjørndalen, John Markus; Anshus, Otto; Brox-Larsen, Tore; Ha, Hoai Phuong; Ims, Rolf Anker & Jepsen, Jane Uhd [Show all 11 contributors for this article] (2018). Building a Sensor System for a Large Scale Arctic Observatory. In Chalmers, Kevin; Pedersen, Jan Bækgaard; Barnes, Frederick R.M.; Broenink, Jan F.; Ivimey-Cook, Ruth; Sampson, Adam T.; Welch, Peter h.; Skovhede, Kenneth & Vinter, Brian (Ed.), Communicating Process Architectures 2015 & 2016. IOS Press. ISSN 978-1-61499-885-3. p. 445–449. doi: 10.3233/978-1-61499-886-0-445. Show summary

  The Climate-ecological Observatory for Arctic Tundra (COAT) is a long-term research initiative for real time detection, documentation and understanding of climate impacts on terrestrial arctic ecosystems. COAT is a collaboration of several Norwegian research institutions under the umbrella of FRAM - High North Centre for Climate and Environment. The study areas include the bioclimatic extremes of the terrestrial Arctic, low arctic coast of Norway and high arctic Svalbard. An important part of the observatory is sensors placed in the environment to observe wildlife and plants. Current sensor packages are fairly robust and work well for small to medium scale deployment. For larger scales, however, there is a clear demand for better management and control. This paper summarises some current experiences with deploying cameras and some of the challenges that we intend to address in an up-coming project where we aim to increase the capability of scientists to handle a larger number and diversity of sensor types and variation in deployment while minimising human traffic and impact in the monitored environments. To build this type of observatory at increasing scales, we expect to use robust programming architectures, open modular sensor packages, on-line processing, monitoring and configuration management and a range of communication technologies to cope with variations in connectivity.

• Michalik, Lukasz Sergiusz; Anshus, Otto & Bjørndalen, John Markus (2017). Flexible Devices for Arctic Ecosystems Observations. NIKT: Norsk IKT-konferanse for forskning og utdanning. ISSN 1892-0713. Full text in Research Archive
• Murphy, Michael J.; Bjørndalen, John Markus & Anshus, Otto (2017). Scalability of Distributed Version Control Systems. NIKT: Norsk IKT-konferanse for forskning og utdanning. ISSN 1892-0713. Full text in Research Archive
• Thom, Håvard; Bjørndalen, John Markus; Kleiven, Eivind Flittie; Soininen, Eeva M; Killengreen, Siw Turid & Ehrich, Dorothee [Show all 9 contributors for this article] (2017). Using machine learning to provide automatic image annotation for wildlife camera traps in the Arctic. In Melbye, Hasse (Eds.), Proceedings from the 42nd annual conference of the International Lung Sound Association, ILSA 2017. Institutt for samfunnsmedisin - ISM skriftserie. ISSN 978-82-8378-003-1. p. 17–20. Full text in Research Archive
• Haro, Peter Halland & Anshus, Otto (2016). Seadrive - Remote file synchronization for offshore fleets. NIKT: Norsk IKT-konferanse for forskning og utdanning. ISSN 1892-0713. Show summary

  Seadrive is a novel file synchronizations framework and file hosting service for offshore naval fleets. Offshore vessels outside the range of cellular networks employ volatile satellite-based networks unsuited for file synchronization. Seadrive aspires to provide naval fleets with usable file hosting services over low bandwidth, high latency network links with high loss rate and large variability in the overall network stability. We provide the rationale for Seadrive; present the architecture, and design of the framework. We present an initial evaluation of the correctness and performance, preliminary results shows that Seadrive outperforms existing system in the test environment.

• Umar, Ibrahim; Anshus, Otto & Ha, Hoai Phuong (2016). Effect of portable fine-grained locality on energy efficiency and performance in concurrent search trees. SIGPLAN notices. ISSN 0362-1340. 51(8), p. 375–376. doi: 10.1145/3016078.2851186.
• Umar, Ibrahim; Anshus, Otto & Ha, Hoai Phuong (2016). Effect of portable fine-grained locality on energy efficiency and performance in concurrent search trees, PPoPP '16 Proceedings of the 21st ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. Association for Computing Machinery (ACM). ISSN 978-1-4503-4092-2. doi: 10.1145/2851141.2851186.
• Umar, Ibrahim; Anshus, Otto & Ha, Hoai Phuong (2016). GreenBST: Energy-efficient concurrent search tree. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 9833, p. 502–517. doi: 10.1007/978-3-319-43659-3_37. Full text in Research Archive
• Umar, Ibrahim; Anshus, Otto & Ha, Hoai Phuong (2015). DeltaTree: A Locality-aware Concurrent Search Tree, Proceedings of the 2015 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems. Association for Computing Machinery (ACM). ISSN 978-1-4503-3486-0. p. 457–458. doi: 10.1145/2745844.2745891.
• Umar, Ibrahim; Anshus, Otto & Ha, Hoai Phuong (2015). DeltaTree: A locality-aware concurrent search tree. Performance Evaluation Review. ISSN 0163-5999. 43(1), p. 457–458. doi: 10.1145/2745844.2745891. Full text in Research Archive
• Tartari, Giacomo; Stødle, Daniel; Bjørndalen, John Markus; Ha, Hoai Phuong & Anshus, Otto (2014). Controlling and Coordinating Computers in a Room with In-Room Gestures. In Hippe, Zdzisław S.; Mroczek, Teresa; Kulikowski, Juliusz L. & Wtorek, Jerzy (Ed.), Human-Computer Systems Interaction: Backgrounds and Applications 3. Springer. ISSN 978-3-319-08491-6. p. 103–116.
• Su, Fei; Bjørndalen, John Markus; Ha, Hoai Phuong & Anshus, Otto (2014). Masking the Effects of Delays in Human-to-Human Remote Interaction. In Ganzha, Maria; Maciaszek, Leszek A & Paprzycki, Marcin (Ed.), Proceedings of the 2014 Federated Conference on Computer Science and Information Systems. IEEE conference proceedings. ISSN 978-83-60810-58-3. p. 719–728. doi: 10.15439/2014F137. Full text in Research Archive
• Tartari, Giacomo; Stødle, Daniel; Bjørndalen, John Markus; Ha, Phuonghoai & Anshus, Otto (2014). Controlling and Coordinating Computers in a Room with In-Room Gestures. Advances in Intelligent Systems and Computing. ISSN 2194-5357. 300, p. 103–116. doi: 10.1007/978-3-319-08491-6_9.
• Fjukstad, Bård; Bjørndalen, John Markus & Anshus, Otto (2014). Uncertainty Estimation and Visualization of Wind in Weather Forecasts. In Laramee, Robert S.; Kerren, Andreas & Braz, Jose (Ed.), Proceedings of the 5th International Conference on Information Visualization Theory and Applications, IVAPP 2014. SciTePress. ISSN 978-989-758-005-5. p. 321–328. doi: 10.5220/0004660603210328. Full text in Research Archive
• Fjukstad, Bård; Bjørndalen, John Markus & Anshus, Otto (2013). Accurate Weather Forecasting Through Locality Based Collaborative Computing. In Bertino, Elisa & Georgakopoulos, Dimitrios (Ed.), Proceedings of the 9th International Conference on Collaborative Computing: Networking, Applications and Worksharing (Collaboratecom 2013). The Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (ICST). ISSN 978-1-936968-92-3. p. 571–578. doi: 10.4108/icst.collaboratecom.2013.254178.
• Holsbø, Einar Jakobsen; Ha, Hoai Phuong & Anshus, Otto (2013). The Big Digger & Puzzler System for Harvesting & Analyzing Data from Social Networks. In Tøssebro, Erlend & Meling, Hein (Ed.), Norsk informatikkonferanse NIK 2013, Universitetet i Stavanger, 18. - 20.november 2013. Akademika forlag. ISSN 978-82-321-0365-2. p. 1–12.
• Tiede, Lars; Bjørndalen, John Markus & Anshus, Otto (2013). Cloud Displays for Mobile Users in a Display Cloud. In Agarwal, Sharad & Varshavsky, Alexander (Ed.), HotMobile '13: Proceedings of the 14th Workshop on Mobile Computing Systems and Applications. Association for Computing Machinery (ACM). ISSN 978-1-4503-1421-3. doi: 10.1145/2444776.2444793.
• Fjukstad, Bård; Bjørndalen, John Markus & Anshus, Otto (2013). Embarrassingly Distributed Computing for Symbiotic Weather Forecasts. Procedia Computer Science. ISSN 1877-0509. 18, p. 1217–1225. doi: 10.1016/j.procs.2013.05.288. Full text in Research Archive
• Su, Fei; Bjørndalen, John Markus; Ha, Hoai Phuong & Anshus, Otto (2013). pVD - Personal Video Distribution. IEEE International Conference on Wireless and Mobile Computing, Networking, and Communications. ISSN 2160-4886. CFP13609-PRT, p. 687–692. doi: 10.1109/WiMOB.2013.6673431. Full text in Research Archive
• Anshus, Otto; Stødle, Daniel; Hagen, Tor-Magne Stien; Fjukstad, Bård; Bongo, Lars Ailo & Bjørndalen, John Markus [Show all 8 contributors for this article] (2013). Nine Years of the Tromsø Display Wall. In Mackay, Wendy E.; Brewster, Stephen & Bødker, Susanne (Ed.), CHI EA '13 CHI '13 Extended Abstracts on Human Factors in Computing Systems. Association for Computing Machinery (ACM). ISSN 978-1-4503-1952-2.
• Tartari, Giacomo; Stødle, Daniel; Bjørndalen, John Markus; Ha, Hoai Phuong & Anshus, Otto (2013). Global interaction space for user interaction with a room of computers. Conference on Human System Interaction. ISSN 2158-2246. IEEE Catalog Number: CFP1321D-ART, p. 84–89. doi: 10.1109/HSI.2013.6577806.
• Su, Fei; Tartari, Giacomo; Bjørndalen, John Markus; Ha, Hoai Phuong & Anshus, Otto (2013). MultiStage: Acting across Distance. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 7990, p. 227–239. doi: 10.1007/978-3-642-40050-6_20.
• Ha, Hoai Phuong; Tsigas, Philippas & Anshus, Otto (2012). Wait-free Programming for General Purpose Computations on Graphics Processors. IEEE transactions on computers. ISSN 0018-9340. PP(99). doi: 10.1109/TC.2012.274.
• Fjukstad, Bård; Hagen, Tor-Magne Stien; Stødle, Daniel; Ha, Hoai Phuong; Bjørndalen, John Markus & Anshus, Otto (2012). Interactive Weather Simulation and Visualization on a Display Wall with Many-Core Compute Nodes. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 7133(Part 1), p. 142–151. doi: 10.1007/978-3-642-28151-8_14.
• Hagen, Tor-Magne Stien; Stødle, Daniel; Bjørndalen, John Markus & Anshus, Otto (2011). A Step towards Making Local and Remote Desktop Applications Interoperable with High-Resolution Tiled Display Walls. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 6723, p. 194–207. doi: 10.1007/978-3-642-21387-8_15.
• Su, Fei; Stødle, Daniel; Ha, Hoai Phuong; Bjørndalen, John Markus & Anshus, Otto (2011). Video Distribution Systems for Interactive Collaboration. NIKT: Norsk IKT-konferanse for forskning og utdanning. ISSN 1892-0713. p. 247–250.
• Hagen, Tor-Magne Stien; Stødle, Daniel & Anshus, Otto (2010). On-demand High-performance Visualization of Spatial Data on High-resolution Tiled Display Walls. In Richard, Paul & Braz, Jose (Ed.), IMAGAPP 2010 - Proceedings of the International Conference on Imaging Theory and Applications and IVAPP 2010 - Proceedings of the International Conference on Information Visualization Theory and Applications. Institute for Systems and Technologies of Information, Control and Communication. ISSN 978-989-674-027-6. p. 112–119.
• Fjukstad, Bård; Hagen, Tor-Magne Stien; Stødle, Daniel; Ha, Hoai Phuong; Bjørndalen, John Markus & Anshus, Otto (2010). Interactive Weather Simulation and Visualization on a Display Wall with Many-Core Compute Nodes, State of the Art in Scientific and Parallel Computeing (Para 2010) - Programme Schedule and Short Abstracts (Updated version published in LNCS 7133, February 2012). University of Iceland. ISSN 0000000000000. p. 29–29.
• Ha, Hoai Phuong; Tsigas, P & Anshus, Otto (2010). The Synchronization Power of Coalesced Memory Accesses. IEEE Transactions on Parallel and Distributed Systems. ISSN 1045-9219. 21(7), p. 939–953.

View all works in Cristin

• Tofaily, Salma; Rais, Issam & Anshus, Otto Johan (2023). Representing Power Variability of an Idle IoT Edge Node in the Power State Model.
• Guegan, Loic; Rais, Issam & Anshus, Otto Johan (2023). Towards Data Dissemination Policy Prediction for Constrained Environments Using Analytics.
• Guegan, Loic; Rais, Issam & Anshus, Otto Johan (2023). Validation of ESDS Using Epidemic-Based Data Dissemination Algorithms.
• Tofaily, Salma; Rais, Issam & Anshus, Otto Johan (2023). Quantifying the variability of power and energy consumption for IoT edge nodes.
• Kristensen, Steffen Ole Randrup; Bjørndalen, John Markus; Rais, Issam; Ha, Hoai Phuong & Anshus, Otto Johan (2023). Weather-aware Wake-up of Sleeping Cyber-Physical IoT Nodes.
• Michalik, Lukasz Sergiusz; Guegan, Loic; Rais, Issam; Anshus, Otto Johan & Bjørndalen, John Markus (2022). LoRaLitE: LoRa protocol for Energy-Limited environments.
• Kristensen, Steffen Ole Randrup; Rais, Issam; Bjørndalen, John Markus; Ha, Hoai Phuong & Anshus, Otto Johan (2021). Impact of image compression on CNN performance metrics for CPS nodes at the Arctic Tundra.
• Tollefsen, Roberth; Rais, Issam; Bjørndalen, John Markus; Ha, Hoai Phuong & Anshus, Otto (2021). Distribution of Updates to IoT Nodes in a Resource-Challenged Environment.
• Murphy, Michael J.; Tveito, Øystein; Kleiven, Eivind Flittie; Rais, Issam; Soininen, Eeva M & Bjørndalen, John Markus [Show all 7 contributors for this article] (2021). Experiences Building and Deploying Wireless Sensor Nodes for the Arctic Tundra.
• Rais, Issam; Anshus, Otto; Ha, Hoai Phuong & Bjørndalen, John Markus (2020). Challenges Monitoring the Arctic Tundra: The Distributed Arctic Observatory Cyber-Physical System. [Internet]. Newsletter IEEE.
• Rais, Issam; Anshus, Otto; Bjørndalen, John Markus; Balouek-Thomert, Daniel & Parashar, Manish (2020). Trading Data Size and CNN Confidence Score for Energy Efficient CPS Node Communications.
• Bjørndalen, John Markus; Ha, Hoai Phuong; Michalik, Lukasz Sergiusz; Bamal, Roopam; Tollefsen, Roberth & Tveito, Øystein [Show all 9 contributors for this article] (2019). Lessons from Automating observations of Arctic Eco-systems.
• Rais, Issam; Bjørndalen, John Markus; Ha, Hoai Phuong; Jensen, Ken-arne; Michalik, Lukasz Sergiusz & Mjøen, Håvard [Show all 8 contributors for this article] (2019). UAVs as a leverage to provide energy and network for Cyber-Physical Observation Units on the Arctic Tundra.
• Michalik, Lukasz Sergiusz; Murphy, Michael J.; Bjørndalen, John Markus & Anshus, Otto (2018). CSP at the Cyber-Physical Edge.
• Bjørndalen, John Markus; Vinter, Brian & Anshus, Otto (2018). aPyCSP - Asynchronous PyCSP Using Python Coroutines and Asyncio.
• Thom, Håvard; Bjørndalen, John Markus; Kleiven, Eivind Flittie; Soininen, Eeva M; Killengreen, Siw Turid & Ehrich, Dorothee [Show all 9 contributors for this article] (2017). Using machine learning to provide automatic image annotation for wildlife camera traps in the Arctic.
• Michalik, Lukasz Sergiusz; Anshus, Otto & Bjørndalen, John Markus (2017). Flexible Devices for Arctic Ecosystems Observations.
• Murphy, Michael J.; Bjørndalen, John Markus & Anshus, Otto (2017). Scalability of Distributed Version Control Systems.
• Anshus, Otto (2016). Center for Bioinformatics at UiT.
• Haro, Peter Halland & Anshus, Otto (2016). Seadrive - Remote file synchronization for offshore fleets.
• Bjørndalen, John Markus; Anshus, Otto; Brox-Larsen, Tore; Ha, Hoai Phuong; Ims, Rolf Anker & Jepsen, Jane Uhd [Show all 11 contributors for this article] (2016). Building a Sensor System for a Large Scale Arctic Observatory.
• Umar, Ibrahim; Anshus, Otto & Ha, Hoai Phuong (2016). GreenBST: Energy-Efficient Concurrent Search Tree.
• Umar, Ibrahim; Anshus, Otto & Ha, Hoai Phuong (2016). Effect of Portable Fine-Grained Locality on Energy Efficiency and Performance in Concurrent Search Trees.
• Umar, Ibrahim; Tran, Ngoc Nha Vi; Shariati, Saeed; Anshus, Otto & Ha, Hoai Phuong (2015). The Arctic Green Computing Group.
• Umar, Ibrahim; Anshus, Otto & Ha, Hoai Phuong (2015). Energy-efficient and Locality-aware Concurrent Search Tree.
• Umar, Ibrahim; Anshus, Otto & Ha, Hoai Phuong (2015). DeltaTree: A Locality-aware Concurrent Search Tree.
• Su, Fei; Bjørndalen, John Markus; Ha, Hoai Phuong & Anshus, Otto (2014). Masking the Effects of Delays in Human-to-Human Remote Interaction.
• Fjukstad, Bård; Bjørndalen, John Markus & Anshus, Otto (2014). Uncertainty Estimation and Visualization of Wind in Weather Forecasts.
• Fjukstad, Bård; Bjørndalen, John Markus & Anshus, Otto (2013). Accurate Weather Forecasting Through Locality Based Collaborative Computing.
• Tiede, Lars; Bjørndalen, John Markus & Anshus, Otto (2013). Cloud Displays for Mobile Users in a Display Cloud.
• Holsbø, Einar Jakobsen; Ha, Hoai Phuong & Anshus, Otto (2013). The Bigger & Puzzler System for Harvesting & Analyzing Data from Social Networks.
• Fjukstad, Bård; Bjørndalen, John Markus & Anshus, Otto (2013). Embarrassingly Distributed Computing for Symbolic Weather Forecasts.
• Su, Fei; Tartari, Giacomo; Bjørndalen, John Markus; Ha, Hoai Phuong & Anshus, Otto (2013). MultiStage: Acting across Distance. Full text in Research Archive
• Anshus, Otto; Stødle, Daniel; Hagen, Tor-Magne Stien; Fjukstad, Bård; Bjørndalen, John Markus & Bongo, Lars Ailo [Show all 8 contributors for this article] (2013). Nine Years of the Tromsø Display Wall.
• Su, Fei; Bjørndalen, John Markus; Ha, Hoai Phuong & Anshus, Otto (2013). pVD - Personal Video Distribution.
• Tartari, Giacomo; Stødle, Daniel; Bjørndalen, John Markus; Ha, Hoai Phuong & Anshus, Otto (2013). Global Interaction Space for User Interaction with a Room of Computers.
• Fjukstad, Bård; Bjørndalen, John Markus & Anshus, Otto (2012). SAFE-WEATHER: User Specified, Rapidly Produced, On-Demand, Very High-Resolution Numerical Weather Forecasts. Show summary

  In northern areas weather and environmental conditions are challenging. The ability to rapidly compute local very high resolution forecasts on-demand can be critical for safety and control of activities when operating in extreme environments. National meteorological services collect diverse observational weather data from a distributed set of sources. The data sets are centrally stored. After processing for quality assurance and filtering, sets are routinely made available over the Internet to a large number of diverse users. To receive fresh data, the recommended use is to retrieve data on-demand. Meteorological services have one or a few large data centers for computing numerical meteorological and oceanographic forecasts. A pre-selected set of result parameters from the forecasts are made available, either as standardized data files, or as ready made images for visualization. The spatial resolution of the most detailed forecasts are determined by the size of the forecast region, the forecast duration and the available compute resources. A forecast that includes the Barents Sea implies a forecast region the size of Northern Europe. The drawbacks of the described approach to weather forecasting include: (i) The user is limited to the data collected by the national meteorological service which may not include locally observed data. (ii) Both access to observational and forecast data demands that the user must have Internet access whenever the data is needed. (iii) The user only has access to parameters determined by the meteorological services. (iv) The user is limited to the numerical models used by the meteorological services. (v) All users receive the same spatial resolution. (vi) The spatial resolution is too coarse for some users. SafeWeather is a system for producing user specified, on-demand, very high-resolution numerical weather forecasts for a small region and a short duration using background meteorological data from the meteorological services. These characteristics allow SafeWeather to execute on a personal computer local to the user. SafeWeather produces the forecasts quickly enough for an interactive user experience. In SafeWeather all the limitations above are addressed. We have created prototypes supporting visualization on two very different devices: (i) Visualizations produced for the Tromsø Display Wall, a wall sized 22 megapixel tiled display, enables both a regional overview and a high level of details at the same time. (ii) Meteorological and other data is visualized on a tablet with built-in GPS, compass and accelerometers. SafeWeather overlays the camera image with the relevant weather visualization.

• Fjukstad, Bård; Bjørndalen, John Markus & Anshus, Otto (2012). Global weather forecasts on supercomputers vs. Many local weather forecasts on personal computers.
• Hagen, Tor-Magne Stien; Stødle, Daniel; Bjørndalen, John Markus & Anshus, Otto (2011). A Step towards Making Local and Remote Desktop Applications Interoperable with High-Resolution Tiled Display Walls.
• Su, Fei; Stødle, Daniel; Ha, Hoai Phuong; Bjørndalen, John Markus & Anshus, Otto (2011). Video Distribution Systems for Interactive Collaboration.
• Anshus, Otto; Stødle, Daniel; Hagen, Tor-Magne Stien; Urke, Eirik Helland & Solstad, Øyvind (2010). Verdens største multitouchskjerm er norsk. [Internet]. http://nrkbeta.no/2010/03/23/verdens-stoerste-multitouchskje.
• Anshus, Otto; Stødle, Daniel; Hagen, Tor-Magne Stien & Simonsen, Bente (2010). Vekker oppsikt. [Newspaper]. http://www.itromso.no/nyheter/article345765.ece.
• Stødle, Daniel; Fjukstad, Bård & Anshus, Otto (2010). Morgendagens spåkuler. Nordlys. ISSN 0805-5440. p. 3–3.
• Fjukstad, Bård; Hagen, Tor-Magne Stien; Stødle, Daniel; Ha, Hoai Phuong; Bjørndalen, John Markus & Anshus, Otto (2010). Interactive Weather Simulation and Visualization on a Display Wall with Many-Core Compute Nodes.
• Fjukstad, Bård; Anshus, Otto & Bjørndalen, John Markus (2014). Multi-Locality Based Local and Symbiotic Computing for Interactively fast On-Demand Weather Forecasting for Small Regions, Short Durations, and Very High-Resolutions . UiT Norges arktiske universitet. ISSN 978-82-8236-144-6. Show summary

  Modern weather forecasting is based on forecasts from huge numerical models executed on large supercomputer clusters in large computer centers. %The forecasts are produced according to a strict schedule. Production deadlines for forecast and available computational resources are the main factors for having to limit the forecast region size, spatial resolution, forecast duration and detail level of the weather model used. Modern numerical models currently used by meteorological services exhibit characteristics of weak scaling. The time to produce a forecast of fixed size decreases less and less with increasing number of CPU cores used. Disregarding communication costs, will how fast a computer cluster can compute a forecast be limited by how fast a single core on a node can compute one time-step for a single mesh point in the model. Adding more CPU cores allows a forecast for a larger region to be computed within the same time period. Forecasts for a small region can be computed using few CPU cores as fast or faster than a large region using many cores. The publicly available weather forecast from national weather services as well as from other public services has a set of characteristics. The forecasts cover large regions with a resolution not high enough to include important features and detailed flow patterns in regions with complex terrain. Excepting the National Weather Service in the USA, the services make public only a small subset of available forecast parameters. Forecasts for large areas are computed using batch systems at fixed intervals using whatever observations are available at the fixed starting times. When a user request a forecasts, the latest forecast may typically be up to 6 - 12 hour old. It is not computed on-demand when the user requests the forecast. Visualizations from weather services are often from a static set of types, styles and features and are created for a web browser or apps on commodity devices. Most visualization are not suitable for specialized devices, like a display wall or a smart watch. In this dissertation, tradeoffs are made in model parameters to identify the highest resolution being meteorologically sound and still computable on commodity hardware. The dissertation identifies also the benefits resulting from combining multiple overlapped very high-resolution forecasts. The architecture, design and implementation for commodity platforms for two systems are described. The first system is for computing on-demand very high-resolution interactively fast forecasts. Forecasts can be produced with a resolution better than what is publicly available, for small regions and with short duration, on modern personal desktop computers. The forecasts can be produced for any location on the globe, with a user specified resolution. The second system is for combining local weather forecasts done by multiple users with overlapping regions. The forecasts can be shared between neighbors using a developed collaborative system. Combining shared forecasts enables estimation of forecast uncertainty, thereby increasing the value of the individual forecast. The combined forecasts also allows for computing a measurement of trust between peers. The produced forecast allows for individual visualization, and may contain all possible parameters and vertical levels from the model. A set of experiments was conducted on the prototype systems to document their characteristics. By reducing the problem size, a numerical forecast computation can be moved from a supercomputer to a personal computer. This dissertation documents that the forecasts are produced faster, on-demand, at a higher resolution and are meteorologically sound. The forecasts can realistically be produced on-demand for any location on the globe. This dissertation presents the principle of short traveled data and computations. One realization of this principle is locally produced weather forecasts. The produced forecasts .....

• Bjørndalen, Karen E Hough & Anshus, Otto (2014). DPC: The Distributed Personal Computer. UiT Norges arktiske universitet.
• Holsbø, Einar; Anshus, Otto & Ha, Hoai Phuong (2014). Large Multiples Exploring the large-scale scattergun approach to visualization and analysis. UiT Norges arktiske universitet.
• Umar, Ibrahim; Anshus, Otto & Ha, Hoai Phuong (2013). DeltaTree: A Practical Locality-aware Concurrent Search Tree. Institutt for informatikk. Full text in Research Archive
• Svendby, Andreas; Bjørndalen, John Markus & Anshus, Otto (2012). Evaluation of shared tuple spaces as communication model for semi-autonomous robots in a mobile environment. Masteroppgave. Institutt for informatikk (trykkeri: Tromsprodukt).
• Martinsen, Gaute Gjøvik; Anshus, Otto; Ha, Hoai Phuong & Bjørndalen, John Markus (2012). Analysis Service for Temporal Social Data. Masteroppgave. Institutt for informatikk (trykkeri: Tromsprodukt).
• Kreutzer, Tor; Anshus, Otto; Bjørndalen, John Markus & Ha, Hoai Phuong (2012). Harvest: A Collaborative System for Distributed Retrieval of Social Data. Masteroppgave. Institutt for informatikk (trykkeri: Tromsprodukt).
• Hagen, Tor-Magne Stien; Ha, Hoai Phuong & Anshus, Otto (2012). Experimental Fault-Tolerant Synchronization for Reliable Computation on Graphics Processors. Institutt for informatikk. 2012(71). Full text in Research Archive
• Nilsen, Arild; Anshus, Otto & Stødle, Daniel (2011). WallMon: Interactive distributed monitoring of process-level resource usage on display and compute clusters. Masteroppgave. Institutt for informatikk (trykkeri: Tromsprodukt).
• Liu, Yong; Anshus, Otto & Bjørndalen, John Markus (2011). Principle and Practice of Distributing Low and High Resolution Display Content from One Computer to Many Computers in Stand-alone or Display Wall Configurations. Institutt for informatikk (trykkeri: Tromsprodukt).
• Hagen, Tor-Magne Stien; Anshus, Otto; Bjørndalen, John Markus & Larsen, Tore (2011). Interactive Visualization on High-Resolution Tiled Display Walls with Network Accessible Compute- and Display-Resources. Institutt for informatikk (trykkeri: Tromsprodukt).

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