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Anomaly detection based on multiple streaming sensor data
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Systems and Technology.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Today, the Internet of Things is widely used in various fields, such as factories, public facilities, and even homes. The use of the Internet of Things involves a large number of sensor devices that collect various types of data in real time, such as machine voltage, current, and temperature. These devices will generate a large amount of streaming sensor data. These data can be used to make the data analysis, which can discover hidden relation such as monitoring operating status of a machine, detecting anomalies and alerting the company in time to avoid significant losses. Therefore, the application of anomaly detection in the field of data mining is very extensive. This paper proposes an anomaly detection method based on multiple streaming sensor data and performs anomaly detection on three data sets which are from the real company. First, this project proposes the state transition detection algorithm, state classification algorithm, and the correlation analysis method based on frequency. Then two algorithms were implemented in Python, and then make the correlation analysis using the results from the system to find some possible meaningful relations which can be used in the anomaly detection. Finally, calculate the accuracy and time complexity of the system, and then evaluated its feasibility and scalability. From the evaluation result, it is concluded that the method

Place, publisher, year, edition, pages
2019. , p. 60
Keywords [en]
Anomaly detection, streaming sensor data, state transition detection, state classification, correlation analysis, Python.
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:miun:diva-36275Local ID: DT-V18-A2-008OAI: oai:DiVA.org:miun-36275DiVA, id: diva2:1323458
Subject / course
Computer Engineering DT1
Educational program
International Master's Programme in Computer Engineering TDAAA 120 higher education credits
Supervisors
Examiners
Available from: 2019-06-12 Created: 2019-06-12 Last updated: 2019-06-12Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf