Distributed learning-based localization schemes for wireless sensor networks are studied in
this thesis. Here, we first review the theory on distributed learning with abstention and
then apply the theory to distributed localization applications that utilize only the hop-count
information between nodes to perform the localization task. Specifically, we partition the
network into a number of partitions based on the sensors' locations and determine which
class each sensor falls into. We consider a network with a number of beacon nodes that
have perfect knowledge of its own coordinates and utilize their knowledge as training data to
perform the above classification. In this work, we propose three approaches for distributed
learning based on the different features that is used to determine the class of each node,
namely, the hop-count (HC) method, the density-aware hop-count length (DHL) method,
and the distance vector (DV) method. These methods are compared under different system
parameters and also compared with the triangulation method that is often employed in the
literature. The importance of beacon placement as well as the effect of transmission errors
is also discussed.

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