  Nowadays, the Internet of Things (IoT) has become one of the most important
technologies which enables a variety of connected and intelligent applications
in smart cities. The smart decision making process of IoT devices not only
relies on the large volume of data collected from their sensors, but also
depends on advanced optimization theories and novel machine learning
technologies which can process and analyse the collected data in specific
network structure. Therefore, it becomes practically important to investigate
how different optimization algorithms and machine learning techniques can be
leveraged to improve system performance.
  As one of the most important vertical domains for IoT applications, smart
transportation system has played a key role for providing real-world
information and services to citizens by making their access to transport
facilities easier and thus it is one of the key application areas to be
explored in this thesis.
  In a nutshell, this thesis covers three key topics related to applying
mathematical optimization and deep learning methods to IoT networks. In the
first topic, we propose an optimal transmission frequency management scheme
using decentralized ADMM-based method in a IoT network and introduce a
mechanism to identify anomalies in data transmission frequency using an
LSTM-based architecture. In the second topic, we leverage graph neural network
(GNN) for demand prediction for shared bikes. In particular, we introduce a
novel architecture, i.e., attention-based spatial temporal graph convolutional
network (AST-GCN), to improve the prediction accuracy in real world datasets.
In the last topic, we consider a highway traffic network scenario where
frequent lane changing behaviors may occur with probability. A specific GNN
based anomaly detector is devised to reveal such a probability driven by data
collected in a dedicated mobility simulator.
