# Topics in Deep Learning and Optimization Algorithms for IoT Applications in Smart Transportation

arXiv: https://arxiv.org/abs/2210.07246 · HTML: https://mlsanity.com/paper/2210.07246/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 109 references that are in the map.
- First posted on arXiv 13 Oct 2022.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Multivariate Time-series Anomaly Detection via Graph Attention Network](https://mlsanity.com/paper/2009.02040/) (2020)
- [Coupled Layer-wise Graph Convolution for Transportation Demand Prediction](https://mlsanity.com/paper/2012.08080/) (2020)
- [Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting](https://mlsanity.com/paper/1707.01926/) (2017)
- [A Survey on Anomaly Detection for Technical Systems using LSTM Networks](https://mlsanity.com/paper/2105.13810/) (2021)
- [Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework](https://mlsanity.com/paper/1612.01022/) (2016)
- [A Survey on Security and Privacy Issues in Edge Computing-Assisted Internet of Things](https://mlsanity.com/paper/2008.03252/) (2020)
- [Driving Intention Recognition and Lane Change Prediction on the Highway](https://mlsanity.com/paper/1908.10820/) (2019)
- [Security and Privacy for Mobile Edge Caching: Challenges and Solutions](https://mlsanity.com/paper/2012.03165/) (2020)
- [Graph Convolutional Networks for traffic anomaly](https://mlsanity.com/paper/2012.13637/) (2020)
- [Lane-GNN: Integrating GNN for Predicting Drivers' Lane Change Intention](https://mlsanity.com/paper/2207.00824/) (2022)
- [On the Design of an Intelligent Speed Advisory System for Cyclists](https://mlsanity.com/paper/1804.08282/) (2018)

## Abstract (arXiv)

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.