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dc.contributor.authorGonzález-Pérez, Alberto
dc.contributor.authorCasteleyn, Sven
dc.date.accessioned2022-05-17T10:26:55Z
dc.date.available2022-05-17T10:26:55Z
dc.date.issued2022
dc.identifier.citationA. González-Pérez and S. Casteleyn, "Hypnos: a Hardware and Software Toolkit for Energy-Aware Sensing in Low-Cost IoT Nodes," in IEEE Internet of Things Journal, doi: 10.1109/JIOT.2022.3145338.ca_CA
dc.identifier.issn2327-4662
dc.identifier.urihttp://hdl.handle.net/10234/197696
dc.description.abstractThrough the Internet of Things, autonomous sensing devices can be deployed to regularly capture environmental and other sensor measurements for a variety of usage scenarios. However, for the market segment of stand-alone, self-sustaining small IoT nodes, long term deployment remains problematic due to the energy-constrained nature of these devices, requiring frequent maintenance. This article introduces Hypnos, an open hardware and software toolkit that aims to balance energy intake and usage through adaptive sensing rate for low-cost Internetconnected IoT nodes. We describe the hardware architecture of the IoT node, an open hardware board based on the Arduino Uno form-factor packing the energy measurement circuitry, and the associated open source software library, that interfaces with the sensing node’s microcontroller and provides access to the low-level energy measurements. Hypnos comes equipped with a built-in, configurable, modified sigmoid function to regulate duty cycle frequency based on energy intake and usage, yet developers may also plug in their custom duty/sleep balancing function. An experiment was set up, whereby two identical boards ran for two months: one with the Hypnos software framework and built-in energy balancing function to regulate sensing rate and the other with fixed sensing rate. The experiment showed that Hypnos is able to successfully balance energy usage and sensing frequency within configurable energy ranges. Hereby, it increases reliability by avoiding complete shutdown, while at the same time optimizing performance in terms of average amount of sensor measurements.ca_CA
dc.format.extent18 p.ca_CA
dc.format.mimetypeapplication/pdfca_CA
dc.language.isoengca_CA
dc.publisherInstitute of Electrical and Electronics Engineersca_CA
dc.publisherIEEEca_CA
dc.rights2327-4662 (c) 2021 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more informationca_CA
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/ca_CA
dc.subjectconstrained devicesca_CA
dc.subjectenergy efficient devicesca_CA
dc.subjectenergy harvestingca_CA
dc.subjectlow cost sensors and devicesca_CA
dc.subjectin situ processingca_CA
dc.titleHypnos: a Hardware and Software Toolkit for Energy-Aware Sensing in Low-Cost IoT Nodesca_CA
dc.typeinfo:eu-repo/semantics/articleca_CA
dc.identifier.doihttp//orcid.org/10.1109/JIOT.2022.3145338
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_CA
dc.type.versioninfo:eu-repo/semantics/acceptedVersionca_CA
project.funder.nameMinisterio de Educación, Cultura y Deporteca_CA
project.funder.nameGobierno de Españaca_CA
oaire.awardNumberFPU17/03832ca_CA
oaire.awardNumberRYC-2014-16606ca_CA


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