Zhenyu Yan

I am a Research Assistant Professor at The Chinese University of Hong Kong (CUHK), Department of Information Engineering. Before joining CUHK, I was a Research Fellow at School of Computer Science and Engineering, Nanyang Technological University. I received my Ph.D. from Nanyang Technological University, Singapore.

My research includes Internet-of-Things sensing, resilient AIoT systems, and cyber-physical systems.

🎓 I am looking for self-motivated Ph.D. students, Postdocs, Research Assistants in general topics of AIoT. More details can be found at here.

selected publications

SenSys ’17

Application-Layer Clock Synchronization for Wearables Using Skin Electric Potentials Induced by Powerline Radiation

Yan, Zhenyu, Li, Yang, Tan, Rui, and Huang, Jun

In The 15th ACM Conference on Embedded Network Sensor Systems (2017) (Acceptance ratio: 26/151=17%)

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Design of clock synchronization for networked nodes faces a fundamental trade-off between synchronization accuracy and universality for heterogeneous platforms, because a high synchronization accuracy generally requires platform-dependent hardware-level network packet timestamping. This paper presents TouchSync, a new indoor clock synchronization approach for wearables that achieves millisecond accuracy while preserving universality in that it uses standard system calls only, such as reading system clock, sampling sensors, and sending/receiving network messages. The design of TouchSync is driven by a key finding from our extensive measurements that the skin electric potentials (SEPs) induced by powerline radiation are salient, periodic, and synchronous on a same wearer and even across different wearers. TouchSync integrates the SEP signal into the universal principle of Network Time Protocol and solves an integer ambiguity problem by fusing the ambiguous results in multiple synchronization rounds to conclude an accurate clock offset between two synchronizing wearables. With our shared code, TouchSync can be readily integrated into any wearable applications. Extensive evaluation based on our Arduino and TinyOS implementations shows that TouchSync’s synchronization errors are below 3 and 7 milliseconds on the same wearer and between two wearers 10 kilometers apart, respectively.
MobiCom ’19

Towards Touch-to-Access Device Authentication Using Induced Body Electric Potentials

Yan, Zhenyu, Song, Qun, Tan, Rui, Li, Yang, and Kong, Adams Wai Kin

In The 25th Annual International Conference on Mobile Computing and Networking (2019) (Acceptance ratio: 55/290=18.9%)

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This paper presents TouchAuth, a new touch-to-access device authentication approach using induced body electric potentials (iBEPs) caused by the indoor ambient electric field that is mainly emitted from the building’s electrical cabling. The design of TouchAuth is based on the electrostatics of iBEP generation and a resulting property, i.e., the iBEPs at two close locations on the same human body are similar, whereas those from different human bodies are distinct. Extensive experiments verify the above property and show that TouchAuth achieves high-profile receiver operating characteristics in implementing the touch-to-access policy. Our experiments also show that a range of possible interfering sources including appliances’ electromagnetic emanations and noise injections into the power network do not affect the performance of TouchAuth. A key advantage of TouchAuth is that the iBEP sensing requires a simple analog-to-digital converter only, which is widely available on microcontrollers. Compared with existing approaches including intra-body communication and physiological sensing, TouchAuth is a low-cost, lightweight, and convenient approach for authorized users to access the smart objects found in indoor environments.
SenSys ’19

Moving Target Defense for Embedded Deep Visual Sensing against Adversarial Examples

Song, Qun, Yan, Zhenyu, and Tan, Rui

In The 17th ACM Conference on Embedded Networked Sensor Systems (2019) (Acceptance ratio: 28/144=19%)

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Deep learning-based visual sensing has achieved attractive accuracy but is shown vulnerable to adversarial example attacks. Specifically, once the attackers obtain the deep model, they can construct adversarial examples to mislead the model to yield wrong classification results. Deployable adversarial examples such as small stickers pasted on the road signs and lanes have been shown effective in misleading advanced driver-assistance systems. Many existing countermeasures against adversarial examples build their security on the attackers’ ignorance of the defense mechanisms. Thus, they fall short of following Kerckhoffs’s principle and can be subverted once the attackers know the details of the defense. This paper applies the strategy of moving target defense (MTD) to generate multiple new deep models after system deployment, that will collaboratively detect and thwart adversarial examples. Our MTD design is based on the adversarial examples’ minor transferability across different models. The post-deployment dynamically generated models significantly increase the bar of successful attacks. We also apply serial data fusion with early stopping to reduce the inference time by a factor of up to 5. Evaluation based on four datasets including a road sign dataset and two GPU-equipped Jetson embedded computing platforms shows the effectiveness of our approach.
IPSN ’21

PhyAug: Physics-Directed Data Augmentation for Deep Sensing Model Transfer in Cyber-Physical Systems

Luo, Wenjie, Yan, Zhenyu, Song, Qun, and Tan, Rui

In The 20th International Conference on Information Processing in Sensor Networks (2021) (Acceptance ratio: 26/105=24.8%)
Best Artifact Award Runner-Up

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Run-time domain shifts from training-phase domains are common in sensing systems designed with deep learning. The shifts can be caused by sensor characteristic variations and/or discrepancies between the design-phase model and the actual model of the sensed physical process. To address these issues, existing transfer learning techniques require substantial target-domain data and thus incur high post-deployment overhead. This paper proposes to exploit the first principle governing the domain shift to reduce the demand on target-domain data. Specifically, our proposed approach called PhyAug, uses the first principle fitted with few labeled or unlabeled source/target-domain data pairs to transform the existing source-domain training data into augmented data for updating the deep neural networks. In two case studies of keyword spotting and DeepSpeech2-based automatic speech recognition, with 5-second unlabeled data collected from the target microphones, PhyAug recovers the recognition accuracy losses due to microphone characteristic variations by 37% to 72%. In a case study of seismic source localization with TDoA fingerprints, by exploiting the first principle of signal propagation in uneven media, PhyAug only requires 3% to 8% of labeled TDoA measurements required by the vanilla fingerprinting approach in achieving the same localization accuracy.