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dc.contributor.authorMichalik, Lukasz Sergiusz
dc.contributor.authorAnshus, Otto
dc.contributor.authorBjørndalen, John Markus
dc.date.accessioned2018-10-11T09:10:34Z
dc.date.available2018-10-11T09:10:34Z
dc.date.issued2017-11-26
dc.description.abstract<p>Devices for observing the environment range from basic sensor systems, like step-counters, through wild-life cameras, with limited processing capabilities, to more capable devices with significant processing, memory and storage resources. Individual usage domains can benefit from a range of functionalities in these devices including flexibility in prototyping, on- device analytics, network roaming, reporting of data, and keeping the devices and services available in spite of failures and disconnections. The problem is that either the devices are too resource limited to support the range of functionalities, or they use too much energy.</p> <p>An important usage domain is COAT – Climate-Ecological Obser- vatory for Arctic Tundra. Presently, best practice includes deploying wild-life cameras in the Arctic Tundra, and visiting them to manually collect the recorded observations. This is a problem because such devices can only be rarely visited, and manual approaches to fetching data and storing it do not scale with regards to number of cameras, handling of human mistakes, and with freshness of observations.</p> <p>We present a prototype for observing the environment composed of a general purpose computer, a Raspberry PI, in combination with an ARM-based microcontroller. The combination enables us to create a more energy efficient prototype while supporting the needed functionality.</p> <p>The prototype improves on currently applied methods of observing the Arctic tundra. The prototype automatically observes the arctic tundra through camera, humidity and temperature sensors. It monitors itself for failures. The data is stored locally on the prototype until it can be automatically reports to a backend service over a wireless network.</p> <p>We have conducted experiments that show that task scheduling can reduce power consumption, and we identify some additional points that need to be addressed before we can run the device for long periods on battery power.</p>en_US
dc.descriptionSource at <a href=http://ojs.bibsys.no/index.php/NIK/article/view/435> http://ojs.bibsys.no/index.php/NIK/article/view/435</a>.en_US
dc.identifier.citationMichalik, L.S., Anshus, O.J. & Bjørndalen, J.M. (2017). Flexible Devices for Arctic Ecosystems Observations. NIK: Norsk Informatikkonferanse.en_US
dc.identifier.cristinIDFRIDAID 1522945
dc.identifier.issn1892-0713
dc.identifier.issn1892-0721
dc.identifier.otherhttp://ojs.bibsys.no/index.php/NIK/article/view/435
dc.identifier.urihttps://hdl.handle.net/10037/13945
dc.language.isoengen_US
dc.publisherNorsk Informatikkonferanseen_US
dc.relation.journalNIK: Norsk Informatikkonferanse
dc.relation.projectIDinfo:eu-repo/grantAgreement/RCN/IKTPLUSS/270672/Norway/Distributed Arctic Observatory: A Cyber-Physical System for Ubiquitous Data and Services Covering the Arctic Tundra/DAO/en_US
dc.rights.accessRightsopenAccessen_US
dc.subjectVDP::Mathematics and natural science: 400::Information and communication science: 420en_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420en_US
dc.titleFlexible Devices for Arctic Ecosystems Observationsen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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