USTC, Hefei, 2018
Previously: at West Lake, Hangzhou.

Xumiao Zhang
Ph.D. student at University of Michigan

4917 BBB Building
2260 Hayward Street
University of Michigan, Ann Arbor
Ann Arbor, MI 48109

Email: xumiao [at] umich.edu

I am a fifth-year Ph.D. student in Computer Science and Engineering at the University of Michigan, Ann Arbor, where I am advised by Prof. Z. Morley Mao. Prior to UMich, I received my B.E. from the University of Science and Technology of China, under the supervision of Prof. Xiangyang Li. I was in the School of the Gifted Young and was enrolled in the Talent Program in Computer and Information Science and Technology.

I am broadly interested in networked systems, mobile networking, and mobile/edge computing. My research strives to develop systems and networking support for characterizing and improving next-generation network infrastructures (e.g., 5G) and applications (e.g., connected and autonomous vehicles, VR/AR). You can find my CV here.

I am seeking both academic and industry positions and welcome opportunities to collaborate on interesting projects!

What's New

May 2022
I joined Alibaba as a research intern this summer.
June 2021
Edge-assisted Multi-vehicle Perception was accepted to ACM MobiCom 2021.
May 2021
I served on the Artifact Evaluation Committee for ACM SIGCOMM 2021.
May 2021
Our paper on 5G network and power performance was accepted to ACM SIGCOMM 2021.
June 2020
I started my internship at General Motors, Warren.
March 2020
MPbond was accepted to ACM MobiSys 2020.
May 2019
I served on the Shadow Program Committee for ACM IMC 2019.
August 2018
I moved to Ann Arbor and started my Ph.D. life at UMich!
July 2017
I started my internship supervised by Prof. Chunyi Peng at the Ohio State University.
September 2015
I was enrolled in the Talent Program in Computer and Information Science & Technology, USTC.

Research Projects

EMP
EMP is an edge-assisted multi-vehicle collaboration system enabling scalable, adaptive, and efficient sensor data sharing to enhance the local processing (e.g., perception) of individual vehicles.
5G Measurement
We examine the effects of 5G deployment strategies, radio bands, and protocol specific properties on network performance, power usage, and application QoE.
MPBond
MPBond is an efficient network-level collaboration system allowing multiple personal mobile devices to collaboratively fetch content from the Internet.
The Sound of
Silence
The Sound of Silence is a portable SmartWatch-based American sign language (ASL) recognition system. This system is based on the intuitive idea that each sign has its specific motion pattern which can be transformed into unique signals and then analyzed by neural networks.

Publications

6

A Cooperative Perception Environment for Traffic Operations and Control arXiv

Hanlin Chen, Brian Liu, Xumiao Zhang, Feng Qian, Z. Morley Mao, Yiheng Feng

Existing data collection methods for traffic operations and control usually rely on infrastructure-based loop detectors or probe vehicle trajectories. Connected and automated vehicles (CAVs) not only can report data about themselves but also can provide the status of all detected surrounding vehicles. Integration of perception data from multiple CAVs as well as infrastructure sensors (e.g., LiDAR) can provide richer information even under a very low penetration rate. This paper aims to develop a cooperative data collection system, which integrates LiDAR point cloud data from both infrastructure and CAVs to create a cooperative perception environment for various transportation applications. The state-of-the-art 3D detection models are applied to detect vehicles in the merged point cloud. We test the proposed cooperative perception environment with the max pressure adaptive signal control model in a co-simulation platform with CARLA and SUMO. Results show that very low penetration rates of CAV plus an infrastructure sensor are sufficient to achieve comparable performance with 30% or higher penetration rates of connected vehicles (CV). We also show the equivalent CV penetration rate (E-CVPR) under different CAV penetration rates to demonstrate the data collection efficiency of the cooperative perception environment.
@article{chen2022cooperative, title={A Cooperative Perception Environment for Traffic Operations and Control}, author={Chen, Hanlin and Liu, Brian and Zhang, Xumiao and Qian, Feng and Mao, Z Morley and Feng, Yiheng}, journal={arXiv preprint arXiv:2208.02792}, year={2022} }
5

EMP: Edge-assisted Multi-vehicle Perception MobiCom '21

Xumiao Zhang, Anlan Zhang, Jiachen Sun, Xiao Zhu, Yihua Guo, Feng Qian, Z. Morley Mao
Proceedings of the 27th Annual International Conference on Mobile Computing and Networking (MobiCom '21)

Connected and Autonomous Vehicles (CAVs) heavily rely on 3D sensors such as LiDARs, radars, and stereo cameras. However, 3D sensors from a single vehicle suffer from two fundamental limitations: vulnerability to occlusion and loss of details on far-away objects. To overcome both limitations, in this paper, we design, implement, and evaluate EMP, a novel edge-assisted multi-vehicle perception system for CAVs. In EMP, multiple nearby CAVs share their raw sensor data with an edge server which then merges CAVs' individual views to form a more complete view with a higher resolution. The merged view can drastically enhance the perception quality of the participating CAVs. Our core methodological contribution is to make the sensor data sharing scalable, adaptive, and resource-efficient over oftentimes highly fluctuating wireless links through a series of novel algorithms, which are then integrated into a full-fledged cooperative sensing pipeline. Extensive evaluations demonstrate that EMP can achieve real-time processing at 24 FPS and end-to-end latency of 93 ms on average. EMP reduces the end-to-end latency by 49% to 65% compared to the traditional vehicle-to-vehicle (V2V) sharing approach without edge support. Our case studies show that cooperative sensing powered by EMP can detect hazards such as blind spots faster by 0.5 to 1.1 seconds, compared to a single vehicle's perception.
@inproceedings{zhang2021emp, title={EMP: Edge-assisted Multi-vehicle Perception}, author={Zhang, Xumiao and Zhang, Anlan and Sun, Jiachen and Zhu, Xiao and Guo, Y Ethan and Qian, Feng and Mao, Z Morley}, booktitle={Proceedings of the 27th Annual International Conference on Mobile Computing and Networking}, year={2021} }
4

A Variegated Look at 5G in the Wild: Performance, Power, and QoE Implications SIGCOMM '21

Arvind Narayanan*, Xumiao Zhang* (*: co-primary), Ruiyang Zhu, Ahmad Hassan, Shuowei Jin, Xiao Zhu, Xiaoxuan Zhang, Denis Rybkin, Zhengxuan Yang,
Z. Morley Mao, Feng Qian, Zhi-Li Zhang
Proceedings of the Annual conference of the ACM Special Interest Group on Data Communication (SIGCOMM '21)

Motivated by the rapid deployment of 5G, we carry out an in-depth measurement study of the performance, power consumption, and application quality-of-experience (QoE) of commercial 5G networks in the wild. We examine different 5G carriers, deployment schemes (Non-Standalone, NSA vs. Standalone, SA), radio bands (mmWave and sub 6-GHz), protocol configurations (e.g. Radio Resource Control state transitions), mobility patterns (stationary, walking, driving), client devices (i.e. User Equipment), and upper-layer applications (file download, video streaming, and web browsing). Our findings reveal key characteristics of commercial 5G in terms of throughput, latency, handover behaviors, radio state transitions, and radio power consumption under the above diverse scenarios, with detailed comparisons to 4G/LTE networks. Furthermore, our study provides key insights into how upper-layer applications should best utilize 5G by balancing the critical tradeoff between performance and energy consumption, as well as by taking into account the availability of both network and computation resources. We have released the datasets and tools of our study at https://github.com/SIGCOMM21-5G/artifact.
@inproceedings{narayanan2021variegated, title={A variegated look at 5G in the wild: performance, power, and QoE implications}, author={Narayanan, Arvind and Zhang, Xumiao and Zhu, Ruiyang and Hassan, Ahmad and Jin, Shuowei and Zhu, Xiao and Zhang, Xiaoxuan and Rybkin, Denis and Yang, Zhengxuan and Mao, Zhuoqing Morley and others}, booktitle={Proceedings of the 2021 ACM SIGCOMM 2021 Conference}, pages={610--625}, year={2021} }
3

MPBond: Efficient Network-level Collaboration Among Personal Mobile Devices MobiSys '20

Xiao Zhu, Jiachen Sun, Xumiao Zhang, Yihua Guo, Feng Qian, Z. Morley Mao
Proceedings of the 18th ACM International Conference on Mobile Systems, Applications, and Services (MobiSys '20)

MPBond is an efficient system allowing multiple personal mobile devices to collaboratively fetch content from the Internet. For example, a smartwatch can assist its paired smartphone with downloading data. Inspired by the success of MPTCP, MPBond applies the concept of distributed multipath transport where multiple subflows can traverse different devices. We develop device/connection management schemes, a buffering strategy, a packet scheduling algorithm, and a policy framework tailored to MPBond’s architecture. We implement MPBond on commodity mobile devices such as Android smartphones and smartwatches. Our real-world evaluations using different workloads under various network conditions demonstrate the efficiency of MPBond. Compared to state-of-the-art collaboration frameworks, MPBond reduces file download time by 5% to 46%, and improves the video streaming bitrate by 2% to 118%. Meanwhile, it improves the energy efficiency by 10% to 57%.
@inproceedings{10.1145/3386901.3388943, author = {Zhu, Xiao and Sun, Jiachen and Zhang, Xumiao and Guo, Y. Ethan and Qian, Feng and Mao, Z. Morley}, title = {MPBond: Efficient Network-Level Collaboration among Personal Mobile Devices}, booktitle = {Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services}, series = {MobiSys ’20}, year = {2020}, isbn = {9781450379540}, location = {Toronto, Ontario, Canada}, pages = {364–376}, numpages = {13}, url = {https://doi.org/10.1145/3386901.3388943}, doi = {10.1145/3386901.3388943}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, }
2

Poster: Characterizing Performance and Power for mmWave 5G on Commodity Smartphones S3 '19

Xumiao Zhang, Xiao Zhu, Yihua Ethan Guo, Feng Qian, Z. Morley Mao
Proceedings of the 2019 on Wireless of the Students, by the Students, and for the Students Workshop (S3 '19)

During the first half of this year, three major operators in the US have announced their 5G deployment, which indicates the advent of next generation networks. To reduce the time to market, carriers utilize 5G NR for data plane operations while retaining the existing 4G infrastructure for control plane operations in what is called NSA (Non-Standalone) deployment mode defined as 5G system Phase I. EN-DC is a core technology of NSA 5G which supports the introduction of 5G services under 4G infrastructure. It enables a UE to connect to LTE and NR at the same time whereas the control plane connection is handled by LTE infrastructure. In this case, the UE will have only one single RRC state machine. Note that LTE RRC state machine contains RRC_CONNECTED and RRC_IDLE with different DRX settings while NR RRC has an additional state called RRC_INACTIVE. In this work, we aim to explore network and power characteristics for NSA 5G which introduces a surprisingly high data rate.
@inproceedings{Zhang:2019:PCP:3349621.3355729, author = {Zhang, Xumiao and Zhu, Xiao and Guo, Yihua Ethan and Qian, Feng and Mao, Z. Morley}, title = {Poster: Characterizing Performance and Power for mmWave 5G on Commodity Smartphones}, booktitle = {Proceedings of the 2019 on Wireless of the Students, by the Students, and for the Students Workshop}, series = {S3'19}, year = {2019}, isbn = {978-1-4503-6929-9}, location = {Los Cabos, Mexico}, pages = {14--14}, numpages = {1}, url = {http://doi.acm.org/10.1145/3349621.3355729}, doi = {10.1145/3349621.3355729}, acmid = {3355729}, publisher = {ACM}, address = {New York, NY, USA}, keywords = {4g, 5g, lte, nr, power model, rrc state machine, smartphone}, }
1

Demo: The Sound of Silence: End-to-End Sign Language Recognition Using SmartWatch MobiCom '17

Qian Dai, Jiahui Hou, Panlong Yang, Xiangyang Li, Fei Wang, Xumiao Zhang
Proceedings of the 23rd Annual International Conference on Mobile Computing and Networking (MobiCom '17)

Sign Language is a natural and fully-fledged communication method for deaf and hearing-impaired people. In this demo, we propose the first SmartWatch-based American sign language (ASL) recognition system, which is more comfortable, portable and user-friendly and offers accessibility anytime, anywhere. This system is based on the intuitive idea that each sign has its specific motion pattern which can be transformed into unique gyroscope and accelerometer signals and then analyzed and learned by using Long-Short term memory recurrent neural network (LSTM-RNN) trained with connectionist temporal classification (CTC). In this way, signs and context information can be correctly recognized based on an off-the-shelf device (e.g. SmartWatch, Smartphone). The experiments show that, in the Known user split task, our system reaches an average word error rate of 7.29% to recognize 73 sentences formed by 103 ASL signs and achieves detection ratio up to 93.7% for a single sign. The result also shows our system has a good adaptation, even including new users, it can achieve an average word error rate of 21.6% at the sentence level and reach an average detection ratio of 79.4%. Moreover, our system performs real time ASL translation, outputting the speech within 1.69 seconds for a sentence of 12 signs in average.
@inproceedings{Dai:2017:DSS:3117811.3119853, author = {Dai, Qian and Hou, Jiahui and Yang, Panlong and Li, Xiangyang and Wang, Fei and Zhang, Xumiao}, title = {Demo: The Sound of Silence: End-to-End Sign Language Recognition Using SmartWatch}, booktitle = {Proceedings of the 23rd Annual International Conference on Mobile Computing and Networking}, series = {MobiCom '17}, year = {2017}, isbn = {978-1-4503-4916-1}, location = {Snowbird, Utah, USA}, pages = {462--464}, numpages = {3}, url = {http://doi.acm.org/10.1145/3117811.3119853}, doi = {10.1145/3117811.3119853}, acmid = {3119853}, publisher = {ACM}, address = {New York, NY, USA}, keywords = {activity recognition, mobile sensing, wearable computing}, }

Invited Talks

October 2022
Guest Lecture for UMich EECS 589
October 2021
Guest Lecture for UMN CSCI 8980 "Mobile Computing"

Teaching

Fall 2017
Fundamentals of Database Systems

Awards

2019 – 2021
Student (Travel) Grant, MobiCom 2021, MobiSys 2021, SIGMETRICS 2021, HotMobile 2019
April 2018
Outstanding Graduate, Provincial Department of Education of Anhui
2014 – 2017
Outstanding Student Scholarship, USTC
January 2017
Honorable Mention of Mathematical Contest in Modeling 2017 (MCM)
June 2016
Second Prize of USTC Electronic Design Contest, Institute of Electronics, CAS
October 2015
Outstanding Student Leadership, USTC
May 2015
Social Responsibility Scholarship, USTC
September 2014
Outstanding Freshman Scholarship, USTC

Services


You are the No. Web Counters th vistor of my homepage.