THESIS
2023
1 online resource (xvi, 192 pages) : color illustrations
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
computatio...[
Read more ]
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
computation and communication 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 resources of IoT devices, one work proposes an efficient and privacy-preserving way to adapt the well pre-trained model to the target data
on the edge device without access to the original training 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.
Post a Comment