Asynchronous distributed clustering algorithms for wireless mesh network
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Date
2021-04-10
Authors
Qiao, Cheng
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Publisher
University College Cork
Published Version
Abstract
Wireless 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.
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Keywords
Asynchronous , Wireless mesh network , Distributed learning , Clustering algorithm
Citation
Qiao, C. 2021. Asynchronous distributed clustering algorithms for wireless mesh network. PhD Thesis, University College Cork.