Publications

2026

1. TIFS

   Human Behavior Anonymization for Secure Teleoperation

   Rongyu Yu , Yufeng Diao, Burak Kizilkaya , and 2 more authors

   IEEE Transactions on Information Forensics and Security, 2026

   Abs Bib HTML

   Teleoperated robotics, which translates human behavior into robotic actions, remains a critical area of modern robotics. Although autonomous systems have advanced rapidly, they still struggle in complex and unstructured environments, making human-in-the-loop control indispensable for many real-world tasks. Teleoperation platforms commonly rely on motion-tracking technologies to capture detailed operator behavior, which is subsequently converted into robot control commands. However, these rich behavioral signals can also encode operator-specific biometrics, posing privacy risks such as user re-identification. While prior work shows that behavioral biometrics can be leveraged for reliable authentication, privacy leakage in teleoperation-centric motion streams has received comparatively less attention. To address this gap, we introduce a disentangled representation-learning framework based on a Variational Autoencoder (VAE) to suppress identity-revealing cues while retaining task-relevant motion patterns. We evaluate the proposed approach offline on reconstructed trajectories collected from a tele-robotic prototype, where multiple users perform a set of manipulation tasks. Our results demonstrate a substantial reduction in re-identification risk and a favorable privacy–utility trade-off in terms of task utility. More broadly, our findings highlight the need for robust privacy protections in future robotic teleoperation systems.

   @article{Yu_2026_HBA,
     title = {Human Behavior Anonymization for Secure Teleoperation},
     author = {Yu, Rongyu and Diao, Yufeng and Kizilkaya, Burak and Zhao, Philip Guodong and Li, Emma Liying},
     journal = {IEEE Transactions on Information Forensics and Security},
     volume = {21},
     number = {},
     pages = {5297-5311},
     year = {2026},
   }

2025

1. JSAC

   Task-Oriented Co-Design of Communication, Computing, and Control for Edge-Enabled Industrial Cyber-Physical Systems

   Yufeng Diao, Yichi Zhang , Daniele De Martini , and 2 more authors

   IEEE Journal on Selected Areas in Communications, 2025

   Abs Bib HTML

   This paper proposes a task-oriented co-design framework that integrates communication, computing, and control to address the key challenges of bandwidth limitations, noise interference, and latency in mission-critical industrial Cyber-Physical Systems (CPS). To improve communication efficiency and robustness, we design a task-oriented Joint Source-Channel Coding (JSCC) using Information Bottleneck (IB) to enhance data transmission efficiency by prioritizing task-specific information. To mitigate the perceived End-to-End (E2E) delays, we develop a Delay-Aware Trajectory-Guided Control Prediction (DTCP) strategy that integrates trajectory planning with control prediction, predicting commands based on E2E delay. Moreover, the DTCP is co-designed with task-oriented JSCC, focusing on transmitting task-specific information for timely and reliable autonomous driving. Experimental results in the CARLA simulator demonstrate that, under an E2E delay of 1 second (20 time slots), the proposed framework achieves a driving score of 48.12, which is 31.59 points higher than using Better Portable Graphics (BPG) while reducing bandwidth usage by 99.19%.

   @article{Diao_2025_TOC,
     title = {Task-Oriented Co-Design of Communication, Computing, and Control for Edge-Enabled Industrial Cyber-Physical Systems},
     author = {Diao, Yufeng and Zhang, Yichi and De Martini, Daniele and Zhao, Philip Guodong and Li, Emma Liying},
     journal = {IEEE Journal on Selected Areas in Communications},
     volume = {43},
     number = {9},
     pages = {3041-3055},
     year = {2025},
   }

2. JSAC

   Aligning Task- and Reconstruction-Oriented Communications for Edge Intelligence

   Yufeng Diao, Yichi Zhang , Changyang She , and 2 more authors

   IEEE Journal on Selected Areas in Communications, 2025

   Abs Bib HTML

   Existing communication systems aim to reconstruct the information at the receiver side, and are known as reconstruction-oriented communications. This approach often falls short in meeting the real-time, task-specific demands of modern AI-driven applications such as autonomous driving and semantic segmentation. As a new design principle, task-oriented communications have been developed. However, it typically requires joint optimization of encoder, decoder, and modified inference neural networks, resulting in extensive cross-system redesigns and compatibility issues. This paper proposes a novel communication framework that aligns reconstruction-oriented and task-oriented communications for edge intelligence. The idea is to extend the Information Bottleneck (IB) theory to optimize data transmission by minimizing task-relevant loss function, while maintaining the structure of the original data by an information reshaper. Such an approach integrates task-oriented communications with reconstruction-oriented communications, where a variational approach is designed to handle the intractability of mutual information in high-dimensional neural network features. We also introduce a joint source-channel coding (JSCC) modulation scheme compatible with classical modulation techniques, enabling the deployment of AI technologies within existing digital infrastructures. The proposed framework is particularly effective in edge-based autonomous driving scenarios. Our evaluation in the Car Learning to Act (CARLA) simulator demonstrates that the proposed framework significantly reduces bits per service by 99.19% compared to existing methods, such as JPEG, JPEG2000, and BPG, without compromising the effectiveness of task execution.

   @article{Diao_2025_ATR,
     title = {Aligning Task- and Reconstruction-Oriented Communications for Edge Intelligence},
     author = {Diao, Yufeng and Zhang, Yichi and She, Changyang and Zhao, Philip Guodong and Li, Emma Liying},
     journal = {IEEE Journal on Selected Areas in Communications},
     volume = {43},
     number = {7},
     pages = {2575-2588},
     year = {2025},
   }

3. J. Dent.

   Dynamic navigation-guided robotic placement of zygomatic implants

   Mohammed Y. Al-Jarsha , Yufeng Diao, Guodong Zhao , and 4 more authors

   Journal of Dentistry, 2025

   Abs Bib HTML

   Objectives To assess the feasibility and accuracy of a new prototype robotic implant system for the placement of zygomatic implants in edentulous maxillary models. Methods The study was carried out on eight plastic models. Cone beam computed tomographs were captured for each model to plan the positions of zygomatic implants. The hand-eye calibration technique was used to register the dynamic navigation system to the robotic spaces. A total of 16 zygomatic implants were placed, equally distributed between the anterior and the posterior parts of the zygoma. The placement of the implants (ZYGAN®, Southern Implants) was carried out using an active six-jointed robotic arm (UR3e, Universal Robots) guided by the dynamic navigation coordinate transformation matrix. The accuracy of the implant placement was assessed using EvaluNav and GeoMagicDesignX® software based on pre- and post-operative CBCT superimposition. Descriptive statistics for the implant deviations and Pearson’s correlation analysis of these deviations to force feedback recorded by the robotic arm were conducted. Results The 3D deviations at the entry and exit points were 1.80 ± 0.96 mm and 2.80 ± 0.95 mm, respectively. The angular deviation was 1.74 ± 0.92°. The overall registration time was 23.8 ± 7.0 min for each side of the model. Operative time excluding registration was 66.8 ± 8.8 min for each trajectory. The exit point and angular deviations of the implants were positively correlated with the drilling force perpendicular to the long axis of the handpiece and negatively correlated with the drilling force parallel to the long axis of the handpiece. Conclusion The errors of the dynamic navigation-guided robotic placement of zygomatic implants were within the clinically acceptable limits. Further refinements are required to facilitate the clinical application of the tested integrated robotic-dynamic navigation system. Clinical significance Robotic placement of zygomatic implants has the potential to produce a highly predictable outcome irrespective of the operator’s surgical experience or fatigue. The presented study paves the way for clinical applications.

   @article{Al-Jarsha_2025_DNG,
     title = {Dynamic navigation-guided robotic placement of zygomatic implants},
     journal = {Journal of Dentistry},
     volume = {153},
     pages = {105463},
     year = {2025},
     issn = {0300-5712},
     doi = {https://doi.org/10.1016/j.jdent.2024.105463},
     author = {Al-Jarsha, Mohammed Y. and Diao, Yufeng and Zhao, Guodong and Imran, Muhammad A. and Ayoub, Ashraf F. and Robertson, Douglas P. and Naudi, Kurt B.},
     keywords = {Robotic surgical procedures, Zygoma, Feedback, Calibration, Feasibility studies},
   }

2024

1. JSAC

   Task-Oriented Cross-System Design for Timely and Accurate Modeling in the Metaverse

   Zhen Meng , Kan Chen , Yufeng Diao, and 4 more authors

   IEEE Journal on Selected Areas in Communications, 2024

   Abs Bib HTML

   In this paper, we establish a task-oriented cross-system design framework to minimize the required packet rate for timely and accurate modeling of a real-world robotic arm in the Metaverse, where sensing, communication, prediction, control, and rendering are considered. To optimize a scheduling policy and prediction horizons, we design a Constraint Proximal Policy Optimization (C-PPO) algorithm by integrating domain knowledge from relevant systems into the advanced reinforcement learning algorithm, Proximal Policy Optimization (PPO). Specifically, the Jacobian matrix for analyzing the motion of the robotic arm is included in the state of the C-PPO algorithm, and the Conditional Value-at-Risk (CVaR) of the state-value function characterizing the long-term modeling error is adopted in the constraint. Besides, the policy is represented by a two-branch neural network determining the scheduling policy and the prediction horizons, respectively. To evaluate our algorithm, we build a prototype including a real-world robotic arm and its digital model in the Metaverse. The experimental results indicate that domain knowledge helps to reduce the convergence time and the required packet rate by up to 50%, and the cross-system design framework outperforms a baseline framework in terms of the required packet rate and the tail distribution of the modeling error.

   @article{Meng_2023_TOC,
     author = {Meng, Zhen and Chen, Kan and Diao, Yufeng and She, Changyang and Zhao, Guodong and Imran, Muhammad Ali and Vucetic, Branka},
     journal = {IEEE Journal on Selected Areas in Communications},
     title = {Task-Oriented Cross-System Design for Timely and Accurate Modeling in the Metaverse},
     year = {2024},
     volume = {42},
     number = {3},
     pages = {752-766},
     keywords = {Metaverse;Task analysis;5G mobile communication;Computational modeling;Servers;Manipulators;Digital twins;Task-oriented cross-system design;scheduling;prediction;constraint deep reinforcement learning;Metaverse},
     doi = {10.1109/JSAC.2023.3345398},
   }

2. INFOCOM

   Task-Oriented Source-Channel Coding Enabled Autonomous Driving Based on Edge Computing

   Yufeng Diao, Zhen Meng , Xiangmin Xu , and 2 more authors

   In Proceedings of IEEE Conference on Computer Communications Workshops , 2024

   Abs Bib HTML

   The communication system is under a paradigm transformation that shifts from traditional bit-level transmission to semantic-level transmission. This transition lays the foundation for complex autonomous driving, necessitating instantaneous processing of substantial data within the constraints of computing capacity and communication bandwidth. In this paper, we propose a novel Task-oriented Source-Channel Coding (TSCC) framework that jointly optimizes source coding and channel coding in a task-oriented manner. Specifically, to reduce communication overhead and guarantee autonomous driving performance, we leverage an autonomous driving agent to guide source-channel coding based on a modified Conditional Variational Autoencoder (CVAE). We test the proposed framework on a well-known autonomous driving platform with different communication channel conditions. The results show that compared to traditional communication and state-of-the-art deep Joint Source-Channel Coding (JSCC), our proposed framework achieves superior performance by saving 98.36% communication overhead and maintains an 83.24% driving score even at 0 dB Signal-to-Noise Ratios (SNR).

   @inproceedings{Diao_2024_TOS,
     author = {Diao, Yufeng and Meng, Zhen and Xu, Xiangmin and She, Changyang and Zhao, Guodong},
     booktitle = {Proceedings of IEEE Conference on Computer Communications Workshops},
     title = {Task-Oriented Source-Channel Coding Enabled Autonomous Driving Based on Edge Computing},
     year = {2024},
     volume = {},
     number = {},
     pages = {1-6},
     keywords = {Joint source-channel coding, AI-driven communication, autonomous driving, edge computing},
     doi = {10.1109/INFOCOMWKSHPS61880.2024.10620735},
   }

3. INFOCOM

   TAGIC: Task-Guided Image Communication Framework for Seamless Teleoperation

   Yufeng Diao, Yichi Zhang , Guodong Zhao , and 1 more author

   In Proceedings of IEEE Conference on Computer Communications Workshops , 2024

   Abs Bib HTML

   Image-based teleoperation offers significant flexibility and efficiency in several applications, such as teleoperated driving; still, it highly depends on reliable communication band-width and high Signal-to-Noise Ratio (SNR), which is hard to guarantee in uncontrolled environments. This poster tackles the challenge of reliable communication under limited bandwidth. We propose to leverage the context and task knowledge to guide the compression to favor task performance rather than image fidelity. In particular, we jointly designed source-channel coding with a task performer to present an end-to-end TAsk-Guided Image Communication (TAGIC) framework, which uses Soft Introspective Variational Autoencoder (S-IntroVAE) and prioritizes the task-critical image information with limited communication bandwidth in the low SNR region. We demonstrate the effectiveness of TAGIC in a teleoperated driving scenario through the CARLA simulation platform - a widely used simulator in the autonomous driving community. Given the equivalent value of bandwidth compression ratio, TAGIC achieves a 202.6% improvement in the driving score over existing methods at low SNR.

   @inproceedings{Diao_2024_TTI,
     author = {Diao, Yufeng and Zhang, Yichi and Zhao, Guodong and De Martini, Daniele},
     booktitle = {Proceedings of IEEE Conference on Computer Communications Workshops},
     title = {TAGIC: Task-Guided Image Communication Framework for Seamless Teleoperation},
     year = {2024},
     volume = {},
     number = {},
     pages = {},
     keywords = {Joint source-channel coding; Teleoperated driving; Machine learning},
     doi = {},
   }

2022

1. ACSAC

   Drone Authentication via Acoustic Fingerprint

   Yufeng Diao, Yichi Zhang , Guodong Zhao , and 1 more author

   In Proceedings of the 38th Annual Computer Security Applications Conference , 2022

   Abs Bib HTML

   As drones become widely used in different applications, drone authentication becomes increasingly important due to various security risks, e.g., drone impersonation attacks. In this paper, we propose an idea of drone authentication based on Mel-frequency cepstral coefficient (MFCC) using an acoustic fingerprint that is physically embedded in each drone. We also point out that the uniqueness of the drone’s sound comes from the combination of bodies (motors) and propellers. In the experiment with 8 drones, we compare the authentication accuracy of different feature extraction settings. Three kinds of different sound features are used: MFCC, delta MFCC (DMFCC), and delta-delta MFCC (DDMFCC). We choose the feature extraction settings and the sound features according to the best authentication result. In the experiment with 24 drones, we compare the closed set authentication performance of eight machine learning methods in terms of recall under the influence of additive white Gaussian noise (AWGN) with different levels of signal-to-noise ratio (SNR). Furthermore, we conduct an open set drone authentication experiment. Our results show that Quadratic Discriminant Analysis (QDA) outperforms other methods in terms of the highest average recall (94.19%) in the authentication of registered drones and the third highest average recall (82.35%) in the authentication of unregistered drones.

   @inproceedings{Diao_2022_DAv,
     author = {Diao, Yufeng and Zhang, Yichi and Zhao, Guodong and Khamis, Mohamed},
     title = {Drone Authentication via Acoustic Fingerprint},
     year = {2022},
     isbn = {9781450397599},
     publisher = {Association for Computing Machinery},
     url = {https://doi.org/10.1145/3564625.3564653},
     doi = {10.1145/3564625.3564653},
     booktitle = {Proceedings of the 38th Annual Computer Security Applications Conference},
     pages = {658-668},
     numpages = {11},
     keywords = {Acoustic fingerprinting, Authentication, Drones, MFCC, Machine learning},
   }

2. IROS Poster

   Keyframe Selection, Communication, and Prediction for Teleoperated Driving Systems

   Yufeng Diao, Yichi Zhang , Guodong Zhao , and 1 more author

   In IEEE/RSJ International Conference on Intelligent Robots and Systems , 2022