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Power analysis and optimization of wireless sensor nodes
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Wireless sensors offer the possibility to monitor critical parameters in our environment, which enables applications to optimize processes or anticipate and detect critical events. Environmental monitoring and predictive maintenance of non-electric systems are two important application domains that have different computational requirements but similar power constraints. In this thesis is presented an iterative wireless sensor node design that supports both applications equally well. In particular, the platform is useful for building demonstrators and evaluating proof of concept designs because the system can be used for the rapid prototyping of models out of standard machine learning frameworks with reasonable performance. At the same time, the platform can run for several years during the environmental monitoring with a battery. Additionally can the system be powered by solar harvesting to enable its use in a "deploy and forget" manner. For this purpose, the system hardware has been optimized and a radio module was selected which enables the transmission of measurements over several kilometers. To recommend the radio configuration for a minimal energy consumption, different settings have been compared in terms of their required energy and transmission range. The power budget of the platform has been generated and optimized, to increase the system run-time and enable the maximal amount of measurements within its energy constraints. Finally, the illumination in greenhouses has been analyzed which showed to provide enough energy to power the platform with a 45x15mm photovoltaic module. In combination with a single coin cell battery could be achieved a continuous system run-time of more than ten years for environmental monitoring applications with this platform.

Place, publisher, year, edition, pages
2019. , p. 105
Series
IT ; 19049
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:uu:diva-396825OAI: oai:DiVA.org:uu-396825DiVA, id: diva2:1369200
Educational program
Masters Programme in Embedded Systems
Supervisors
Examiners
Available from: 2019-11-11 Created: 2019-11-11 Last updated: 2019-11-11Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • de-DE
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Output format
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