Asynchronous distributed clustering algorithms for wireless mesh network

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dc.availability.bitstreamopenaccess
dc.contributor.advisorBrown, Kennethen
dc.contributor.advisorO'Sullivan, Barryen
dc.contributor.authorQiao, Cheng
dc.contributor.funderScience Foundation Irelanden
dc.contributor.funderEuropean Regional Development Funden
dc.date.accessioned2021-05-13T08:35:39Z
dc.date.available2021-05-13T08:35:39Z
dc.date.issued2021-04-10
dc.date.submitted2021-04-10
dc.description.abstractWireless Mesh Networks are becoming increasingly important in many applications. In many cases, data is acquired by devices that are distributed in space, but effective actions require a global view of that data across all devices. Transmitting all data to the centre allows strong data analytics algorithms to be applied, but consumes battery power for the nodes, and may cause data overload. To avoid this, distributed methods try to learn within the network, allowing each agent to learn a global picture and take appropriate actions. For distributed clustering in particular, existing methods share simple cluster descriptions until the network stabilises. The approaches tend to require either synchronous behaviour or many cycles, and omit important information about the clusters. In this thesis, we develop asynchronous methods that share richer cluster models, and we show that they are more effective in learning the global data patterns. Our underlying method describes the shape and density of each cluster, as well as its centroid and size. We combine cluster models by re-sampling from received models, and then re-clustering the new data sets. We then extend the approach, to allowing clustering methods that do not require the final number of clusters as input. After that, we consider the cases that there might be sub-groups of agents that are receiving different patterns of data. Finally, we extend our approaches to scenarios where each agent has no idea about whether there is a single pattern or are multiple patterns. We demonstrate that the approaches can regenerate clusters that are similar to the distributions that were used to create the test data. When the number of clusters are not available, the learned number of clusters is close to the ground truth. The proposed algorithms can learn how data points are clustered even when there are multiple patterns in the network. When the number of patterns (single or multiple) is not known in advance, the proposed methods Optimised KDE and DBSCAN preform well in detecting multiple patterns. Although they perform worse in detecting the single pattern, they can still learn how data points are clustered.en
dc.description.statusNot peer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationQiao, C. 2021. Asynchronous distributed clustering algorithms for wireless mesh network. PhD Thesis, University College Cork.en
dc.identifier.endpage218en
dc.identifier.urihttps://hdl.handle.net/10468/11300
dc.language.isoenen
dc.publisherUniversity College Corken
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/en
dc.rights© 2021, Cheng Qiao.en
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectAsynchronousen
dc.subjectWireless mesh networken
dc.subjectDistributed learningen
dc.subjectClustering algorithmen
dc.titleAsynchronous distributed clustering algorithms for wireless mesh networken
dc.typeDoctoral thesisen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD - Doctor of Philosophyen
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