The arctic tundra is most sensitive to climate change. The change is quantified by observations on the tundra of animals, vegetation, wind, humidity, CO2, temperature, and more. However, much less than 1% of the arctic tundra is observed today. To get more data, observations must be done in-situ on the tundra, more frequently, at more places, over larger areas, and both above and below snow. The observations must allow for identification of plants, birds and small animals. The data must also be made available when and where needed.
It is necessary to deploy a set of nodes (small computers with sensors) to the arctic tundra to do all of this. A node must operate for a long time doing observations, and taking care of, process and report the data. However, on the arctic tundra the common case is that the necessary resources (humans, energy, back-haul data networks) are very limited or not available. We research how a distributed system of observation nodes can be done and will behave under these challenging conditions.
A node must be very frugal with energy to operate for many months and years. A node must presently rely on a single battery charge because energy from solar, wind and humans are not presently practical to deploy at scale on the arctic tundra or not allowed for regulatory reasons. However, having energy-harvesting nodes provide for interesting new functionality and research was done to document the impact of mixing battery-dependent nodes with energy-harvesting nodes.
The life of a node is to sleep most of the time, wake up for a few seconds or minutes to do observations, processing and reporting of observational data, coordinate with other nodes on when to wake up again, receive and install software updates, and then sleep again until the next coordinated wake-up or a sensor triggers an unscheduled wakeup. A range of approaches have been investigated to document the impact on performance metrics (like energy consumption and success rate for completion of data dissemination to and from nodes) of when nodes wake up and the length of the time they are awake. This includes coordinated wake-ups of nodes in relation to each other to increase the probability of nodes being awake simultaneously to collaborate as well as saving energy by avoiding to wake up nodes when the probability of reaching a network is low due to bad weather reducing the radio signal strength.
The project has research a range of energy-efficient functionalities and ways of composing them into an energy-efficient distributed system. Research was done to document the impact sleeping and waking up have on energy consumption and observational, processing, and reporting tasks. A range of approaches were investigated from none to coordinated sleeping and waking up of the nodes. Even the weather was considered when deciding on wake up times for the nodes because in bad weather a node’s radio-based data network can experience reduced signal levels leading to disconnection resulting in unnecessary energy usage.
For the individual observation nodes the project researched functionalities including automated observations and reporting of the corresponding data, automated over-the-air distribution and installation of software updates to the observations nodes, on-node processing of observational data to compress the data to reduce energy usage and the time it takes to transmit the data to remote servers while still preserving the information content of the data such that artificial intelligence computations achieve about the same results as if they were using the original data.
The methodology applied is systems research in computer science and includes doing prototypes and conducting experiments on them to document their behavior under a range of parameters and factors including the number of nodes, size of the data disseminated between nodes, and time, frequency, and length of sleep and uptime for the individual nodes. Three types of prototypes were developed: physical, virtual and simulated. Physical prototypes (multiple nodes built from microcontrollers and Raspberry Pi computers with sensors, and software) are for practical and cost reasons of small scale with, say, 10 observation nodes. They are time-consuming and costly to do and experiment with. However, they provide for collecting actual deployment experiences and experimental data not otherwise available. Virtual prototypes (many identical programs running on one or several computers emulating physical prototypes) are less costly to develop and experiment with, and suitable for every-day laboratory use. Simulated prototypes (use of a simulator to simulate the actions of physical and virtual prototypes) allow for large-scale experiments exploring many factors of a large distributed observation system and useful for informing what to include in the virtual and physical prototypes.
The project has moved the research front for cyber-physical systems forward, and it has contributed to essential competence building amongst the participants and students.
The project has through physical, virtual, and simulated prototypes documented a distributed approach to do large area high resolution observation series over time of the arctic tundra needed for climate research. The results from the project can inform a potential future practical large-scale distributed observation system composed of many small nodes with suitable sensors and data networks operating from small batteries.
Such a system will make it possible to do observations for larger areas and observe the covered areas in higher detail, and more robustly, than what is possible with current state of the art technology for wildlife and environmental monitoring.
Automating the gathering and reporting of observational data in combination with automating some of the necessary processing of the data to extract relevant information, like the species of observed flora and fauna, will provide for timely data and increased efficiency in capturing the state of the arctic tundra without needing a large number of humans to do such tedious tasks.
The project has attracted two international postdocs to the University of Tromsø. Both have subsequently qualified for tenured faculty positions in computer science at the University of Tromsø, and they are continuing the research they started when funded by the project, including applying for research and instrument funding from the Research Council of Norway and elsewhere.
The project has inspired international collaboration on cyber physical systems between the University of Tromsø and universities in France (Lyon), including a shared Ph.D. project. The collaboration is anticipated to expand.
The project has defined and advised many successful bachelor and master students subsequently working in the public sector and in industry where they can apply the latest cyber physical and IoT models and techniques researched by the project.
This interdisciplinary project will for the first time provide for in-situ observations and ubiquitous data and services covering the arctic tundra that scales with the size of the observed area, the resolution of the observations, and the volume and freshness of data. This is a direct response to the Climate-ecological Observatory for Arctic Tundra (COAT) science plan stating that the circumpolar arctic tundra is the earth's terrestrial biome most challenged by climate change, but that there presently are too few observations of the arctic tundra. Therefore, there is a high demand for establishing scientifically robust observation systems to enable timely detection, documentation and understanding of climate impacts.
The arctic tundra is a demanding region with severe weather, low temperatures, limited network services and energy, and often being physically inaccessible. This project advances the state of the art for cyber-physical systems being exposed to such extreme conditions. The Distributed Arctic Observatory is a novel next-generation scalable, energy sensitive, configurable, and robust observation system enabling many in-situ observations at high resolutions and at many locations throughout the arctic tundra, and with services making the data available and explorable by researchers and the public.
There are many challenges facing such a system. The in-situ observation units must be made autonomous so they continue operation despite network limitations, faults, failures, and malware. Observation units have to use their limited resources in an energy sensitive way. Especially the analytics processing to find interesting objects in the observed data requires increased energy efficiency. To be useful in practise the system must be adaptable to new needs, and provide for access to data and for practical analytics and visualizations.
