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Permanent link (DOI): https://doi.org/10.7939/R3PN8XV5F

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Predictive Energy Management for Wireless Sensor Nodes Open Access

Descriptions

Other title
Subject/Keyword
Fuzzy Controller Tuning
Wireless Sensor Networks
Energy Management
Solar Energy Forecast
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Rodway, James E A
Supervisor and department
Musilek, Petr (Electrical and Computer Engineering)
Examining committee member and department
Miller, James (Electrical and Computer Engineering)
Reformat, Marek (Electrical and Computer Engineering)
Pizzi, Nick (InfoMagnetics Technologies)
Musilek, Petr (Electrical and Computer Engineering)
Robinson, Aminah (Civil and Environmental Engineering)
Department
Department of Electrical and Computer Engineering
Specialization
Software Engineering and Intelligent Systemcs
Date accepted
2017-09-08T15:24:28Z
Graduation date
2017-11:Fall 2017
Degree
Doctor of Philosophy
Degree level
Doctoral
Abstract
A wireless sensor network is a tool that can collect data, aiding in answering a number of different questions in research and industrial environments. When deployed in remote locations, it is often beneficial to use of energy harvesting technologies, allowing sensor nodes to replenish energy while in the field. This permits longer deployment times while keeping node size small. In order to make the best use of harvested energy, controllers can be used to adapt node activities to available energy. In this thesis, energy forecasts based on measurements of atmospheric pressure are created and included as inputs to fuzzy controllers. These controllers are applied to simulated sensor nodes and used to control node activity levels for effective use of available energy. They were tuned using differential evolution and simulated using measured meteorological data. The results were examined in terms of the networks overall activity level and the usage of reserve energy. With respect to the solar energy forecasts, a number of applied methods were able to achieve error levels comparable to other methods where more variables were included. The tuned fuzzy controllers represented an improvement over both the uncontrolled and human-created cases.
Language
English
DOI
doi:10.7939/R3PN8XV5F
Rights
This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for the purpose of private, scholarly or scientific research. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
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