Zhenyu Yan

selected publications

1. SenSys ’23

   EdgeFM: Leveraging Foundation Model for Open-set Learning on the Edge

   Bufang Yang, Lixing He, Neiwen Ling, and 5 more authors

   In The 21st ACM Conference on Embedded Networked Sensor Systems (2023)(Acceptance ratio: 35/179=19.6%)

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   Deep Learning (DL) models have been widely deployed on IoT devices with the help of advancements in DL algorithms and chips. However, the limited resources of edge devices make these on-device DL models hard to be generalizable to diverse environments and tasks. Although the recently emerged foundation models (FMs) show impressive generalization power, how to effectively leverage the rich knowledge of FMs on resource-limited edge devices is still not explored. In this paper, we propose EdgeFM, a novel edge-cloud cooperative system with open-set recognition capability. EdgeFM selectively uploads unlabeled data to query the FM on the cloud and customizes the specific knowledge and architectures for edge models. Meanwhile, EdgeFM conducts dynamic model switching at run-time taking into account both data uncertainty and dynamic network variations, which ensures the accuracy always close to the original FM. We implement EdgeFM using two FMs on two edge platforms. We evaluate EdgeFM on three public datasets and two self-collected datasets. Results show that EdgeFM can reduce the end-to-end latency up to 3.2x and achieve 34.3% accuracy increase compared with the baseline.

2. MobiCom ’23

   VI-Map: Infrastructure-Assisted Real-Time HD Mapping for Autonomous Driving

   Yuze He, Chen Bian, Jingfei Xia, and 4 more authors

   In The 29th Annual International Conference on Mobile Computing and Networking (2023)(Acceptance ratio: 92/377=24.4%)
   Best Community Contributions Award

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   HD map is a key enabling technology towards fully autonomous driving. We propose VI-Map, the first system that leverages roadside infrastructure to enhance real-time HD mapping for autonomous driving. The core concept of VI-Map is to exploit the unique cumulative observations made by roadside infrastructure to build and maintain an accurate and current HD map. This HD map is then fused with on-vehicle HD maps in real time, resulting in a more comprehensive and up-to-date HD map. By extracting concise bird-eye-view features from infrastructure observations and utilizing vectorized map representations, VI-Map incurs low compute and communication overhead. We conducted end-to-end evaluations of VI-Map on a real-world testbed and a simulator. Experiment results show that VI-Map can construct decentimeter-level (up to 0.3 m) HD maps and achieve real-time (up to a delay of 42 ms) map fusion between driv- ing vehicles and roadside infrastructure. This represents a significant improvement of 2.8x and 3x in map accuracy and coverage compared to the state-of-the-art online HD map- ping approaches. A video demo of VI-Map on our real-world testbed is available at https://youtu.be/p2RO65R5Ezg.

3. MobiSys ’23

   Towards Bone-Conducted Vibration Speech Enhancement on Head-Mounted Wearables

   Lixing He, Haozheng Hou, Shuyao Shi, and 2 more authors

   In The 21st ACM International Conference on Mobile Systems, Applications, and Services (2023)(Acceptance ratio: 41/198=20.7%)

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   Head-mounted wearables are rapidly growing in popularity. However, a gap exists in providing robust voice-related applications like conversation or command control in complex environments such as competing speakers and strong noises. The compact design of HMWs introduces non-trivial challenges to existing speech enhancement systems that use microphone recording only. In this paper, we handle this problem by using bone vibration conducted through the head skull. The principle is that the accelerometer is widely installed on head-mounted wearables and can capture the clean user’s voice. Hence, we develop VibVoice, a lightweight multi-modal speech enhancement system for head-mounted wearables. We design a two-branch encoder-decoder deep neural network to fuse the high-level features of the two modalities and reconstruct clean speech. To address the insufficient training data of paired data, we extensively measure the bone conduction effect from a limited dataset to extract the physical impulse function for cross-modal data augmentation. We evaluate VibVoice on a real-world dataset and compare it with two state-of-the-art baselines. Results show that VibVoice yields up to 21% better performance in PESQ and up to 26% better performance in SNR compared with the baseline with 72 times less paired data required. We also validate VibVoice’s performance through a user study with 35 participants, where 87% participants prefer VibVoice compared with the baseline. In addition, VibVoice requires 4 to 31 times less execution time compared with baselines on mobile devices. The demo audio of VibVoice is available at https://www.youtube.com/watch?v=8_-s_C_NGRI.

4. SenSys ’22

   Telesonar: Robocall Alarm System by Detecting Echo Channel and Breath Timing

   Zhenyu Yan, Qun Song, Rui Tan, and 1 more author

   In The 20th ACM Conference on Embedded Networked Sensor Systems (2022)(Acceptance ratio: 52/208=25%)

   Abs PDF

   Massive fraudulent and phishing robocalls present threats to societies. The integration of artificial intelligence technologies, including dialogue and voice generation systems, renders the robocalls more deceptive. Existing countermeasures such as caller ID, call provenance, voiceprint, and fake voice detection have respective limitations and are heavyweight for end users’ smartphones. This paper studies detecting the acoustic echo channel on the remote end of a call based on the received voice. The positive detection result evidencing the physical setup of an audio system is indicative of a human caller. However, the acoustic echo cancellation mechanisms of most audio systems and the use of earphone/headset diminish echoes significantly. To address these issues, the proposed Telesonar transmits short chirps during the vulnerable time of echo cancellation, detects the tiny echo remnants from the received voice, and passively analyzes the timing of caller’s breath sounds to confirm a human caller. Extensive real experiments under a wide range of settings show that Telesonar correctly recognizes human callers with a rate of over 95%, while wrongly recognizing voice robots as human with a rate of 3.8%.

5. MobiCom ’22

   VIPS: Real-Time Perception Fusion for Infrastructure-Assisted Autonomous Driving

   Shuyao Shi, Jiahe Cui, Zhehao Jiang, and 4 more authors

   In The 28th Annual International Conference on Mobile Computing and Networking (2022)(Acceptance ratio: 56/314=17.8%)
   Best Paper Award Runner-Up

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   Infrastructure-assisted autonomous driving is an emerging paradigm that expects to significantly improve the driving safety of autonomous vehicles. The key enabling technology for this vision is to fuse LiDAR results from the roadside infrastructure and the vehicle to improve the vehicle’s perception in real time. In this work, we propose VIPS, a novel lightweight system that can achieve decimeter-level and real-time (up to 100 ms) perception fusion between driving vehicles and roadside infrastructure. The key idea of VIPS is to exploit highly efficient matching of graph structures that encode objects’ lean representations as well as their relationships, such as locations, semantics, sizes, and spatial distribution. Moreover, by leveraging the tracked motion trajectories, VIPS can maintain the spatial and temporal consistency of the scene, which effectively mitigates the impact of asynchronous data frames and unpredictable communication/compute delays. We implement VIPS end-to-end based on a campus smart lamppost testbed. To evaluate the performance of VIPS under diverse situations, we also collect two new multi-view point cloud datasets using the smart lamppost testbed and an autonomous driving simulator, respectively. Experiment results show that VIPS can extend the vehicle’s perception range by 140% within 58 ms on average, and delivers a 4X improvement in perception fusion accuracy and 47X data transmission saving over existing approaches. A video demo of VIPS based on the lamppost dataset is available at https://youtu.be/zW4oi_EWOu0.

6. IPSN ’21

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

   Wenjie Luo, Zhenyu Yan, Qun Song, and 1 more author

   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.

7. MobiCom ’19

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

   Zhenyu Yan, Qun Song, Rui Tan, and 2 more authors

   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.

8. SenSys ’17

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

   Zhenyu Yan, Yang Li, Rui Tan, and 1 more author

   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.