| fullrecord |
[{"key": "dc.contributor.advisor", "value": "M\u00e4kitalo, Niko", "language": null, "element": "contributor", "qualifier": "advisor", "schema": "dc"}, {"key": "dc.contributor.author", "value": "Pihlajisto, Mikko", "language": null, "element": "contributor", "qualifier": "author", "schema": "dc"}, {"key": "dc.date.accessioned", "value": "2025-06-02T11:43:35Z", "language": null, "element": "date", "qualifier": "accessioned", "schema": "dc"}, {"key": "dc.date.available", "value": "2025-06-02T11:43:35Z", "language": null, "element": "date", "qualifier": "available", "schema": "dc"}, {"key": "dc.date.issued", "value": "2025", "language": null, "element": "date", "qualifier": "issued", "schema": "dc"}, {"key": "dc.identifier.uri", "value": "https://jyx.jyu.fi/handle/123456789/102944", "language": null, "element": "identifier", "qualifier": "uri", "schema": "dc"}, {"key": "dc.description.abstract", "value": "The application of artificial intelligence (AI) techniques for optimizing energy efficiency in wireless sensor networks (WSNs) is a rapidly growing research area, with particular relevance for managing distributed and resource-constrained environments. This master's thesis presents a systematic mapping study aimed at identifying which AI methods are currently used to promote energy efficiency in WSN contexts, what challenges hinder their implementation, and what research gaps and future directions can be identified.\nThe study analyzed 75 peer-reviewed articles to form a comprehensive overview of the connections between AI techniques, application areas, and energy-related constraints. The findings show that the most commonly applied methods were machine learning and reinforcement learning, particularly in the optimization of routing and energy management. The most significant challenges identified were limitations in computational capacity, real-time requirements, and the dynamic structure of networks. This mapping study provides a structured foundation for the further development of AI-based solutions and the strategic orientation of future research on energy efficiency in WSNs.", "language": "en", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.abstract", "value": "Teko\u00e4lymenetelmien (AI) hy\u00f6dynt\u00e4minen langattomien anturiverkkojen (WSN) energiatehokkuuden optimoinnissa on nopeasti kasvava tutkimusalue, jonka potentiaali kohdistuu erityisesti hajautettujen, resurssirajoitteisten ymp\u00e4rist\u00f6jen hallintaan. T\u00e4ss\u00e4 pro gradu -tutkielmassa suoritettiin systemaattinen kartoitustutkimus, jonka tavoitteena oli kartoittaa, millaisia teko\u00e4lytekniikoita t\u00e4ll\u00e4 hetkell\u00e4 hy\u00f6dynnet\u00e4\u00e4n energiatehokkuuden edist\u00e4miseen WSN-ymp\u00e4rist\u00f6iss\u00e4, mitk\u00e4 haasteet rajoittavat n\u00e4iden menetelmien k\u00e4ytt\u00f6\u00f6nottoa, sek\u00e4 tunnistaa tutkimusaukkoja ja tulevaisuuden suuntaviivoja. \nAineistona analysoitiin 75 vertaisarvioitua artikkelia, joiden pohjalta muodostettiin kokonaiskuva menetelmien, sovellusalueiden ja energiank\u00e4ytt\u00f6\u00f6n liittyvien rajoitteiden v\u00e4lisist\u00e4 yhteyksist\u00e4. Tulosten perusteella yleisimm\u00e4t menetelm\u00e4t olivat koneoppiminen ja vahvistusoppiminen, joita hy\u00f6dynnettiin erityisesti reitityksen ja energianhallinnan optimointiin. Merkitt\u00e4vimmiksi haasteiksi nousivat laskentatehon rajoitteet, reaaliaikaisuusvaatimukset sek\u00e4 verkon dynaaminen rakenne. Katsaus tarjoaa j\u00e4sennellyn perustan teko\u00e4lyratkaisujen jatkokehitykselle ja energiatehokkuuteen liittyv\u00e4n tutkimuksen suuntaamiselle WSN-ymp\u00e4rist\u00f6iss\u00e4.", "language": "fi", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Submitted by jyx lomake-julkaisija (jyx-julkaisija.group@korppi.jyu.fi) on 2025-06-02T11:43:35Z\nNo. of bitstreams: 0", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Made available in DSpace on 2025-06-02T11:43:35Z (GMT). No. of bitstreams: 0", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.format.extent", "value": "88", "language": null, "element": "format", "qualifier": "extent", "schema": "dc"}, {"key": "dc.format.mimetype", "value": "application/pdf", "language": null, "element": "format", "qualifier": "mimetype", "schema": "dc"}, {"key": "dc.language.iso", "value": "fin", "language": null, "element": "language", "qualifier": "iso", "schema": "dc"}, {"key": "dc.rights", "value": "In Copyright", "language": null, "element": "rights", "qualifier": null, "schema": "dc"}, {"key": "dc.title", "value": "Teko\u00e4lytekniikat energiatehokkuuden optimointiin langattomissa anturiverkoissa", "language": null, "element": "title", "qualifier": null, "schema": "dc"}, {"key": "dc.type", "value": "master thesis", "language": null, "element": "type", "qualifier": null, "schema": "dc"}, {"key": "dc.identifier.urn", "value": "URN:NBN:fi:jyu-202506024753", "language": null, "element": "identifier", "qualifier": "urn", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Informaatioteknologian tiedekunta", "language": "fi", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Faculty of Information Technology", "language": "en", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.organization", "value": "Jyv\u00e4skyl\u00e4n yliopisto", "language": "fi", "element": "contributor", "qualifier": "organization", "schema": "dc"}, {"key": "dc.contributor.organization", "value": "University of Jyv\u00e4skyl\u00e4", "language": "en", "element": "contributor", "qualifier": "organization", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Tietojenk\u00e4sittelytieteen maisteriohjelma", "language": "fi", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Master's Degree Programme in Computer Science", "language": "en", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "dc.type.coar", "value": "http://purl.org/coar/resource_type/c_bdcc", "language": null, "element": "type", "qualifier": "coar", "schema": "dc"}, {"key": "dc.rights.copyright", "value": "\u00a9 The Author(s)", "language": null, "element": "rights", "qualifier": "copyright", "schema": "dc"}, {"key": "dc.rights.accesslevel", "value": "openAccess", "language": null, "element": "rights", "qualifier": "accesslevel", "schema": "dc"}, {"key": "dc.type.publication", "value": "masterThesis", "language": null, "element": "type", "qualifier": "publication", "schema": "dc"}, {"key": "dc.format.content", "value": "fulltext", "language": null, "element": "format", "qualifier": "content", "schema": "dc"}, {"key": "dc.rights.url", "value": "https://rightsstatements.org/page/InC/1.0/", "language": null, "element": "rights", "qualifier": "url", "schema": "dc"}, {"key": "dc.description.accessibilityfeature", "value": "ei tietoa saavutettavuudesta", "language": "fi", "element": "description", "qualifier": "accessibilityfeature", "schema": "dc"}, {"key": "dc.description.accessibilityfeature", "value": "unknown accessibility", "language": "en", "element": "description", "qualifier": "accessibilityfeature", "schema": "dc"}]
|