Towards Ubiquitous Internet of Things Applications with Deep Domain Adaptation and Generalization

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


PhD Thesis Defence


Title: "Towards Ubiquitous Internet of Things Applications with Deep Domain
Adaptation and Generalization"

By

Miss Hua KANG


Abstract:

The rapid progression of the Internet-of-Things (IoT) has equipped the physical
world with capabilities for sensing, computation, and communication, thereby
altering human-centered interactions and fostering promising applications. The
recent advancements in deep learning have revolutionized numerous fields and
are now being adopted in IoT sensing applications. Nonetheless, there still
exist some unresolved issues with regards to ubiquitous IoT applications. We
identify four primary challenges encountered across IoT systems. Firstly, there
is the problem of data heterogeneity due to diverse data collection contexts.
Secondly, the labeling burden can be time-consuming. Thirdly, there are privacy
concerns surrounding the distributed collection of data. Lastly, there are
limited communication bandwidth resources for IoT devices. This dissertation
elaborates on these four issues and their potential solutions to take a step
towards ubiquitous IoT applications.

For the first two issues, we consider how to swiftly adapt the model to
unlabeled data with different distributions via unsupervised domain adaptation
and how to generalize the model for different data distributions via domain
generalization. We present innovative approaches for two specific applications,
namely wireless-based gesture recognition and human activity recognition with
partial sensor sets. Regarding the privacy issue of distributed collected data
and limited communication bandwidth, one work proposes an efficient way to
adapt the well pre-trained model to the target data on the edge device without
access to the original training data, which can protect the privacy of the
source data. Another work adopts a federated learning scheme to alleviate
computation and communication overhead while safeguarding privacy. The last
work considers the network layer for efficient data communication. We design
deep learning models for wireless channel prediction, leveraging the stripe
features present in the CSI matrix to reduce bandwidth overheads caused by the
transmission of large downlink CSI matrix.


Date:                   Friday, 18 August 2023

Time:                   10:00am - 12:00noon

Venue:                  Room 5501
                        Lifts 25/26

Chairman:               Prof. Mansun CHAN (ECE)

Committee Members:      Prof. Qian ZHANG (Supervisor)
                        Prof. Gary CHAN
                        Prof. Yangqiu SONG
                        Prof. Jun ZHANG (ECE)
                        Prof. Jianping WANG (CityU)


**** ALL are Welcome ****