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Optimizing Hidden Markov Models for the Internet of Things
KTH, School of Computer Science and Communication (CSC).
2017 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Optimering av dolda Markovmodeller för sakernas Internet (Swedish)
Abstract [en]

The Internet of Things is a new and emerging branch of control systems where gatheredsensor data is analyzed using computer algorithms. This report applies Markov models, astatistical model based on states with transitions between them, to temperature datagathered from an office. In particular, hidden Markov models are used, which infer the statesand their probable transitions from the temperature observations. The purpose of this studyis to find the optimal number of states in a hidden Markov model for Internet of Thingstemperature data. Evaluation is made by comparing the difference between data predictedby the Markov model and actual temperature measurements. 10, 20, 24, 30, 40, 50 and 168states are evaluated. All number of states perform poorly and differ greatly from the actualmeasurements. It is concluded that a more thorough study is needed to accurately answerthe problem statement.

Abstract [sv]

Sakernas Internet eller Internet of Things är en ny typ av kontrollsystem där information om verkligheten samlas in och bearbetas algoritmiskt. Denna studie applicerar Markovmodeller, en statistisk modell som bygger på tillstånd med övergångar emellan dem, på temperaturdata från en kontorsmiljö. Mer specifikt används dolda Markovmodeller, där tillstånd och övergångar räknas ut baserat på de observerade temperaturvärdena. Studiens syfte är att hitta det antal tillstånd som ger en optimal Markovmodell. Detta utvärderas genom att låta modellen förutspå temperaturdata, vars skillnad mot verkliga mätningar summeras till en poäng. 10, 20, 24, 30, 40, 50 och 168 antal tillstånd utvärderas. Alla antal tillstånd ger hög avvikelse och skiljer sig kraftigt från den verkliga mätdatan. Slutsatsen är att en djupare studie krävs för att med säkerhet besvara frågeställningen.

Place, publisher, year, edition, pages
2017.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-208882OAI: oai:DiVA.org:kth-208882DiVA: diva2:1108474
Supervisors
Examiners
Available from: 2017-06-18 Created: 2017-06-12 Last updated: 2017-06-18Bibliographically approved

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Citation style
  • apa
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