We are a research group based in the School of Computer Science and Engineering, Nanyang Technological University. We focus on the research, design, and evaluation of networked, energy-efficient, and secure sensing systems found in the Internet of Things (IoT) and its AI-empowered generation (AIoT). Our research has two main sub-directions of IoT sensing systems/applications and security/privacy of AIoT sensing. In the first sub-direction, with a strong experimental focus, we study a number of sensing modalities (e.g., powerline radiation, radio frequency, acoustics, image, thermal, and energy), exploit them to construct system functions and applications. In the second sub-direction, we study the security and privacy of AIoT sensing systems that use machine learning to process the sensed data. Due to the immediate application potential, our research has been funded externally by government authorities and companies in the ICT, energy, and manufacturing sectors.
The BubCam system presented in this paper estimates the volume of the air bubbles in the ink bags moving on the conveyor belt of the manufacturing line. It employs one main camera and several wireless cameras, whose operations are controlled by a deep reinforcement learning agent. BubCam achieves 1.34 accuracy improvement and 260x latency reduction, compared with manual inspection.
This paper associates the sensing results of mmWave radar and user-held IMUs to track multiple users moving in the same space and estimate the interpersonal distances.
This paper uses a smartphone’s loudspeaker to emit near-inaudible chirps and its microphone to record the acoustic echoes from the indoor environment for localizing the smartphone. Contrasive learning is applied to learn effective location-discriminative features for constructing a SLAM system.
The PriMask system uses personalized lightweight neural networks to obfuscate the data generated by individual users to protect their privacy against curious cloud inference services. At the same time, the obfuscation does not degrade the original inference service quality.
The Telesonar system employs an active sensing approach to probe the existence of the echo channel at the remote end of a phone call as well as a passive sensing approach to analyze the breath timing. This helps decide whether the call is a robocall.
This is the repo of paper “Physics-Directed Data Augmentation for Deep Sensing Model Transfer in Cyber-Physical Systems”.
This is the repo for the EWSN'22 paper “Sardino Ultra-Fast Dynamic Ensemble for Secure Visual Sensing at Mobile Edge”.
This repository provides code of “Lightweight and Unobtrusive Data Obfuscation at IoT Edge for Remote Inference”.
This dataset includes sensor measurements collected from an air free-cooled data center testbed in 2018 and 2019.
This dataset includes sensor measurement traces collected from an air-cooled data center testbed in 2022 and 2023.
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Our group have a good history of success in working with undergraduate students. We have produced many novel research publications driven by undergrads.
Our group has research positions available regularly. We invite motivated researchers to apply.
