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Permanent link (DOI): https://doi.org/10.7939/R38C9RJ0T

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Synchronisation and Wavelet Compression for Background Interference Classification in Wireless Environments Open Access

Descriptions

Other title
Subject/Keyword
Compression
Wireless
Wavelet
Interference
Synchronisation
Classification
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Vlachaki, Aikaterini
Supervisor and department
Janelle J. Harms
Ioanis Nikolaidis
Examining committee member and department
Stewart, Lorna (Computing Science)
Harms, Janelle J.(Computing Science)
Nikolaidis, Ioanis (Computing Science)
Department
Department of Computing Science
Specialization

Date accepted
2017-04-26T08:10:20Z
Graduation date
2017-11:Fall 2017
Degree
Master of Science
Degree level
Master's
Abstract
Dense wireless deployment environments are increasingly facing Radio Frequency (RF) spectrum congestion and increased levels of interference. Addressing the interference will require a distributed decision-making application on wireless nodes, that characterize the state of the channel with respect to the presence (or not) of interference, allowing the nodes to adopt mitigation strategies. In this thesis, through a study of empirical data, we examine if each node was to separately characterize the state of the channel and then form consensus with other nodes, whether this consensus correctly reflects the similarity of the per-node sensed background interference. Towards this goal we develop a distributed synchronization scheme that reduces the inaccuracy inherent in distributed data sampling in local node clocks. As an alternative to consensus of per-node characterization we also examine the effectiveness of the Discrete Wavelet Transform (DWT) to communicate to other nodes the state of the channel, as sampled by a node, in a compressed, denoised form. Our early results show that wavelet compression of the sampled time series may produce significant data volume savings, to allow the full time series to be communicated e.g. to a cloud infrastructure where large scale planning of spectrum usage can take place.
Language
English
DOI
doi:10.7939/R38C9RJ0T
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.
Citation for previous publication
A. Vlachaki, I. Nikolaidis, and J. J. Harms. Wavelet-based analysis of interference in WSNs. In 41st IEEE Conference on Local Computer Networks (LCN), November 2016A. Vlachaki, I. Nikolaidis, and J. J. Harms. A study of channel classification agreement in urban wireless sensor network environments. In O. Postolache, M.Sinderen, F.H. Ali, and C. Benavente-Peces, editors, SENSORNETS 2014, pages 249–259. SciTePress, 2014

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