To do in-situ observations of the arctic tundra, many small sensor nodes are used. Reporting the data to remote back-ends is hard because backhaul networks are scarce, and a node cannot expect to see one. Also, there are typically no humans physically close to the nodes to fetch the data and to replace batteries. Consequently, the nodes need a highly available, energy-efficient long range network. While LoRaWAN provides energy-efficient communication for nodes, it does so at the cost of needing an always-on gateway with a high energy consumption. The gateway is also a single point of failure. Instead we propose LoRaLitE for Energy-Limited environments where the gateway enters sleep phases to reduce energy consumption. The sleep phases of the nodes are coordinated accordingly. For high availability, any end-node with a backhaul-network can be elected to become the gateway. We conducted a series of simulation experiments to document the performance behavior of both LoRaWanand LoRaLitE. The results show that the LoRaLitE gateway spends from 10 to 10,000 times less energy than a LoRaWAN gateway. A LoRaLitE node spends from 13% to 42% more energy than a LoRaWAN node. The achievable bandwidth for LoRaLitE is only insignificantly smaller than for LoRaWAN. A LoRaWangateway needs a relatively large, heavy battery. If the gateway fails, assuming a new gateway can be elected, several nodes must have similar large batteries to be able to take over as the gateway. This is impractical to achieve for more than a few nodes because the nodes become more expensive, harder to camouflage, and impractical to deploy. A LoRaLitE gateway on the other hand only needs a battery like any other node. Even if all nodes need larger batteries than for the LoRaWancase, the increase is in practical terms insignificant. We conclude that LoRaLitE makes it realistic to use LoRa for nodes on the arctic tundra, providing for insignificant increased energy usage at the nodes, insignificant reduced bandwidth, but with significant increase in gateway availability. LoRaLitE also provides for an easier deployment in practice because all nodes can be kept small and light,

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.

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.

In a context of Cyber-Physical Systems (CPS), energy-efficiency is a critical factor to achieve long operational life-time. The constraint of using battery-powered devices adds degrees of complexity, especially in a hard to reach environment with scarce network and energy resources. The reporting of data consumes large amount of energy, reducing the life-time of both individual nodes and the CPS as a whole. One way to reduce the energy cost of communication is to reduce the number of Bits to transmit. However, this is a viable approach only if the transmitted data remain suitable for further analysis.In this paper, we report on the effect of reducing the image dimensions on the confidence score computed by a convolutional neural network (CNN) determining the species of animals present in images. We also report on the energy consumption of transmitting full vs. reduced dimensions of images. CPS devices and CNNs developed by the Distributed Arctic Observatory (DAO) project are used as experimental platforms.The results show that the energy needed to report the images can be reduced by up to 98% while only reducing the average confidence of determining the species correctly by 0.10%.

Observing an environment is essential to its understanding. This is certainly true for the arctic tundra as it is extremely sensitive to climate change. Satellites are used to observe large areas of the arctic tundra. However, the measurements are not at sufficient spatial resolution to be used to determine the species of animals, and to measure humidity, temperature, and CO2 levels for small areas. Ground-based measurements, which represent less that one percent of the experiments carried on the arctic tundra, must be done. Deploying and maintaining ground-based instruments is hard to do because visiting the arctic tundra is expensive, time consuming, and dangerous. Instead, automation is needed, where instruments can operate independently for long periods of time, and allow interaction with remote users for reporting of data and control of the instruments. However, the arctic tundra also has, as a common case, limited data network and energy coverage. We present the Distributed Arctic Observatory, where instruments called Observation Units (OUs) are distributed across the arctic tundra to measure a range of environmental state variables, and report them to where they are needed for further analysis. The DAO project uses Unmanned Aerial Vehicles (UAVs) to provide backhaul network access and energy to OUs. The modifications applied to the UAV and to OUs are presented. We describe the experiences gathered from practical use of the UAV to provide network access and energy to OUs located on the ground at 70°N. We show that it is feasible to apply UAVs to provide network access and energy to OUs on the arctic tundra, albeit practical restrictions.

Humans can interact remotely with each other through computers.Systems supporting this include teleconferencing, games and virtual environments.There are delays from when a human does an action until it is reflected remotely.When delays are too large, they will result in inconsistencies in what the state of the interaction is as seen by each participant.The delays can be reduced, but they cannot be removed.When delays become too large the effects they create on the humanto-human remote interaction can be partially masked to achieve an illusion of insignificant delays.The MultiStage system is a human-to-human interaction system meant to be used by actors at remote stages creating a common virtual stage.Each actor is remotely represented by a remote presence created based on a stream of data continuously recorded about the actor and being sent to all stages.We in particular report on the subsystem of MultiStage masking the effects of delays.The most advanced masking approach is done by having each stage continuously look for late data, and when masking is determined to be needed, the system switches from using a live stream to a pre-recorded video of an actor.The system can also use a computable model of an actor creating a remote presence substituting for the live stream.The present prototype uses a simple human skeleton model.

Visualization is a key step in scientific analysis and understanding in many fields. Scientific studies often require development of software that produces visualizations. However, as a study proceeds, the software evolves, and both developers and expert users have to periodically ascertain how code modifications affect visualization output and hence the results of the study. To our knowledge, no current visualization framework enables tracking and comparison of the lineage of scientific visualizations. We describe an approach for comparing and maintaining the code for scientific analysis and modeling through interactive comparison of visualization output. We have realized this approach in a tool called Mr. Clean. This tool provides a framework for combining different visualization tools, interaction devices, and display middleware for visual comparisons on high-resolution displays. Mr. Clean also provides user-configurable interactions supported by many devices. We provide use cases and a requirement analysis for our approach, and we describe the design and implementation of Mr. Clean. Source code is available at: https://github.com/UniversityofTromso/mrclean.

The Collaborative Symbiotic Weather Forecasting (CSWF) system lets a user compute a short time, high-resolution forecast for a small region around the user, in a few minutes, on-demand, on a PC. A collaborated forecast giving better uncertainty estimation is then created using forecasts from other u

To interact with a computer, a user can walk up to it and interact with it through its local interaction space defined by its input devices. With multiple computers in a room, the user can walk up to each computer and interact with it. However, this can be logistically impractical and forces the user to learn each computers local interaction space. Interaction involving multiple computers also becomes hard or even impossible to do. We propose to have a global interaction space, letting users, through in-room gestures, select and issue commands to one or multiple computers in the room. A global interaction space has functionality to sense and record the state of a room, including location of computers, users, and gestures, and use this to issue commands to each computer. A prototype has been implemented in a room with multiple computers and a wall-sized large display. The global interaction space is used to issue commands, moving display output from on-demand selected computers to the large display and back again. It is also used to select multiple computers and concurrently execute commands on them.

A user has several personal computers, including mobile phones, tablets, and laptops, and needs to watch live camera feeds from and videos stored at any of these computers at one or more of the others. Industry solutions designed for many users, computers, and videos can be complicated and slow to apply. The user must typically rely on a third party service or at least log in. The Personal Video Distribution (pVD) system supports sending and viewing live and stored videos between any of a single user's computers, and allows for a smooth handover of play back between computers. The system avoids any third parties, and relies only on the user's personal computers. We present the architecture, design and implementation of the pVD prototype. The architecture is comprised of functionality for sending videos, subscribing to videos, and maintaining the video play-back state. The design has a local side sending and viewing videos, and a global side coordinating the switching and distribution of videos, and maintaining subscriptions and video state. The prototype is primarily done in Python. A set of experiments was conducted to document the performance of the prototype. The results show that pVD global side has low CPU usage, and supports a handful of simultaneous exchanges of videos on a wireless network.

The CoSMoS project aims to develop reusable tools and techniques for complex systems modelling and simulation. Using process-oriented software design techniques, we have built a concurrent model of continuous space, usable in a variety of complex systems simulations. In this paper, we describe how we refactored our space model to allow our simulations to run in an efficient and highly-scalable manner across clusters of commodity machines-and, in particular, to support distributed simulation and visualisation on the Tromsoslash Display Wall.

We report on a system supporting cross-platform mirroring of user-selectable regions from one or multiple computer desktops onto nearby network accessible projectors and displays (NADs). The purpose is a simple and flexible use of nearby display resources requiring no permanent installation of new software on the desktop computer. The NAD system architecture consists of a NAD side and a desktop side. The desktop software is downloaded to the desktop computer on demand, from a web server running on the NAD. The desktop and NAD software handle the integration of user-selectable desktop regions and remote control between the desktop computer and the NAD. The system is implemented in Java 1.6. At a resolution of 800 by 600 pixels the system supports mirroring of dynamic content at 38.6 fps. At 1600 by 1200 pixels the refresh rate is 12.85 fps. For static content such as images and slide show presentations the system's bandwidth usage is within the capacity of a 11 Mbit/s wireless network. For dynamic content such as videos and games the system requires at least a 100 Mbit/s connection.

Scalability of parallel applications on clusters and multi-clusters is often limited by communication performance. Message tracing can provide data for understanding bottlenecks, and for performance tuning. However, it requires collecting, storing, analyzing, and transferring potentially gigabytes of data. We have designed the EventSpace system for low overhead and high performance runtime collective communication trace analysis. EventSpace separates the perturbation and performance requirements of data collection, analysis, gathering sand visualization. Data collection overhead is low since the minimum amount of data is recorded and stored temporarily in main memory. The recorded data is either discarded or analyzed on demand using available cluster resources. Analysis is distributed for high performance, and coscheduled with the computation and communication system threads for low perturbation. Gathering of analyzed data is done using extensible collective communication operations, which can be tuned to trade off between performance and monitoring overhead. EventSpace was used to do run-time monitoring and analysis of collective communication micro-benchmarks run on clusters, multi-clusters, and multi-clusters with emulated WAN links. Performance data was collected, analyzed and gathered with 0-3% monitoring overhead.