publications

2024

1. 

   Known Knowns and Unknowns: Near-realtime Earth Observation Via Query Bifurcation in Serval

   Bill Tao, Om Chabra, Ishani Janveja, and 2 more authors

   In 21st USENIX Symposium on Networked Systems Design and Implementation (NSDI 24), Apr 2024

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2023

1. 

   Transmitting, Fast and Slow: Scheduling Satellite Traffic through Space and Time

   Bill Tao, Maleeha Masood, Indranil Gupta, and 1 more author

   In Proceedings of the 29th Annual International Conference on Mobile Computing and Networking, Apr 2023

   Abs PDF Code Slides

   Earth observation Low Earth Orbit (LEO) satellites collect enormous amounts of data that needs to be transferred first to ground stations and then to the cloud, for storage and processing. Satellites today transmit data greedily to ground stations, with full utilization of bandwidth during each contact period. We show that due to the layout of ground stations and orbital characteristics, this approach overloads some ground stations and underloads others, leading to lost throughput and large end-to-end latency for images. We present a new end-to-end scheduler system called Umbra, which plans transfers from large satellite constellations through ground stations to the cloud, by accounting for both spatial and temporal factors, i.e., orbital dynamics, bandwidth constraints, and queue sizes. At the heart of Umbra is a new class of scheduling algorithms called withhold scheduling, wherein the sender (i.e., satellite) selectively under-utilizes some links to ground stations. We show that Umbra’s counter-intuitive approach increases throughput by 13–31% & reduces P90 latency by 3–6 \texttimes.

2. 

   Magnetic Backscatter for In-body Communication and Localization

   Bill Tao, Emerson Sie, Jay Shenoy, and 1 more author

   In Proceedings of the 29th Annual International Conference on Mobile Computing and Networking, Apr 2023

   Abs PDF Code Slides

   Implantable and edible medical devices promise to provide continuous, directed, and comfortable healthcare treatments. Communicating with such devices and localizing them is a fundamental, but challenging, mobile networking problem. Recent work has focused on leveraging near field magnetism-based systems to avoid the challenges of attenuation, refraction, and reflection experienced by radio waves. However, these systems suffer from limited range, and require fingerprinting-based localization techniques. We present InnerCompass, a magnetic backscatter system for in-body communication and localization. InnerCompass relies on new magnetism-native design insights that enhance the range of these devices. We design the first analytical model for magnetic-field-based localization, that generalizes across different scenarios. We’ve implemented InnerCompass and evaluated it in porcine tissue. Our results show that Inner-Compass can communicate at 5 Kbps at a distance of 25 cm, and localize with an accuracy of 5 mm.

2022

1. 

   RF-Protect: Privacy against Device-Free Human Tracking

   Jayanth Shenoy, Zikun Liu,  Tao, Bill, and 2 more authors

   In Proceedings of the ACM SIGCOMM 2022 Conference, Apr 2022

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   The advent of radio sensing that works through walls & obstacles challenges the notion of indoor privacy. An eavesdropper can deploy such sensing to snoop on their neighbors and a smart sensor embedded with such sensing capabilities can perform large scale behavioral and health data mining. We present RF-Protect, a new framework that enables privacy by injecting fake humans in the sensed data. RF-Protect consists of a novel hardware reflector design that modifies radio waves to create reflections at arbitrary locations in the environment and a new generative mechanism to create realistic human trajectories. RF-Protect’s design doesn’t require any high bandwidth hardware or physical motion. We implement RF-Protect using commodity hardware and validate its ability to generate fake human trajectories.

2020

1. Improving Quality of Experience by Adaptive Video Streaming with Super-Resolution

   Y. Zhang, Y. Zhang, Y. Wu, and 5 more authors

   In IEEE INFOCOM 2020 - IEEE Conference on Computer Communications, Apr 2020

2019

1. Learning at the Edge: Smart Content Delivery in Real World Mobile Social Networks

   K. Bian, C. Gao,  Y. Tao, and 4 more authors

   IEEE Network, Apr 2019
2. Trajectory-Based User Encounter Prediction Over Wireless Sensor Networks

   Meng Tong,  Tao, Yu, Yuanxing Zhang, and 2 more authors

   Wireless Personal Communications, Apr 2019
3. Attentive Context-Aware Music Recommendation

   Y. Tao, Y. Zhang, and K. Bian

   In 2019 IEEE Fourth International Conference on Data Science in Cyberspace (DSC), Apr 2019
4. Machine Learning Assisted Content Delivery at Edge of Mobile Social Networks

   Y. Tao, K. Bian, C. Gao, and 3 more authors

   In 2019 IEEE Fourth International Conference on Data Science in Cyberspace (DSC), Apr 2019
5. Addressing the Conflict of Negative Feedback and Sampling for Online Ad Recommendation in Mobile Social Networks

   Y. Tao, Y. Zhang, J. Lin, and 1 more author

   In 2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN), Apr 2019
6. Language Support for Navigating Architecture Design in Closed Form

   Weilong Cui, Georgios Tzimpragos,  Tao, Yu, and 6 more authors

   J. Emerg. Technol. Comput. Syst., Oct 2019

   Abs

   As computer architecture continues to expand beyond software-agnostic microarchitecture to specialized and heterogeneous logic or even radically different emerging computing models (e.g., quantum cores, DNA storage units), detailed cycle-level simulation is no longer presupposed. Exploring designs under such complex interacting relationships (e.g., performance, energy, thermal, frequency) calls for a more integrative but higher-level approach. We propose Charm, a modeling language supporting closed-form high-level architecture modeling. Charm enables mathematical representations of mutually dependent architectural relationships to be specified, composed, checked, evaluated, reused, and shared. The language is interpreted through a combination of automatic symbolic evaluation, scalable graph transformation, and efficient compiler techniques, generating executable DAGs and optimized analysis procedures. Charm also exploits the advancements in satisfiability modulo theory solvers to automatically search the design space to help architects explore multiple design knobs simultaneously (e.g., different CNN tiling configurations). Through two case studies, we demonstrate that Charm allows one to define high-level architecture models in a clean and concise format, maximize reusability and shareability, capture unreasonable assumptions, and significantly ease design space exploration at a high level.