I am broadly interested in experimental systems research in the field of mobile computing. My research interests have spanned from energy-efficient mobile computing, to edge computing, to mobile crowdsensing and to augmented/virtual reality. My research often takes the approach of building potentially large-scale prototype systems and then evaluating them experimentally.

My current work contains two parts. One is creating innovative mobile applications to solve real life problems, such as crowdsourced indoor mapping and AR-based assistance for mechanical assembly and maintenance. The other part of my research focuses on solving the performance, energy-efficiency and scalability challenges that the new applications (e.g. video crowdsourcing, autonomous driving) pose to the cloud and network architectures.

A brief introduction to my background and research interests can be found from here. Here is the link to our group website .

Feel free to contact me if you are interested in joining my group. We are looking for students who have strong programming skills and are interested in augmented/virtual reality, deep learning or computer vision.

Ongoing Projects

1) Reality Capture for Construction Management, funded by Tekes, October 2017 - September 2019 (joint work with department of civil engineering)

Keywords: 3D modelling, laser scanning, building information model, object recognition, augmented reality

2) Intelligent Construction Site, funded by Tekes, October 2016 - September 2018 (joint work with department of civil engineering)

Keywords: indoor tracking, mobile sensing

3) EIT HII Active - Advanced Connectivity Platform for Vertical Segments. September 2017 - June 2018.

Keywords: low-latency, wearable computing, edge computing

Completed Projects

1) Scientific Advisor, Mobile Crowdsensing in Ubiqutious Cloud Environment, funded by Academy of Finland (Grant number: 277498), September 2014–August 2017

Keywords: crowdsensing, edge computing, indoor mapping and navigation, analytics

2) Co-PI and technical lead, Image-based Indoor Product Navigation System, funded by Tekes: New Knowledge and Business from Research Ideas, August 2016 - May 2017

Keywords: indoor mapping, navigation

Our latest demo (March 2017) of AR navigation in a supermarket is here. We have founded VimAI Oy to commercialize our vision-based indoor mapping and augmented intelligence solutions.

3) PI, Collaborative Optimization for Networking Performance in Ubiquitous Cloud Computing, funded by Academy of Finland (Grant number: 268096), September 2013 – March 2017

Keywords: edge computing, network performance, measurement and analysis, optimization

Postdocs

Giancarlo Pastor, 2017.09 - , fog/edge computing, compressed sensing, reinforcement learning

Yuki Sato. 2017.09 - 2018.06. Hand gesture recognition, RFID mapping.

Doctoral students

Peter Byvshev (Aalto/COMNET), 2017.12- , activity recognition from video

Truong-an Pham (Aalto/COMNET), 2017.05 - , wearable cognitive assistance for mechanical assmebly and maintenance

Marius Noreikis (Aalto/COMNET), 2016.05 -, resource-efficient augmented reality navigation for indoor environment

Chao Zhu (Aalto/CS, co-supervising with Prof. Antti Ylä-Jääski), 2016.10 -, resource management in vehicular fog computing

Jiang Dong (Aalto/CS), 2012.10 - 2017.06 , towards efficient and sustainable mobile crowdsensing

Visiting Scholars

Professor Yong Li, Tsinghua University, China. 2017.07

Interns

Year 2018: Xuebing Li, Rong Xie, Tufail Muhammad, Jouni Ojala

Year 2017: Tufail Muhammad, Jouni Ojala, Jonathan Granskog, Seyoung Park, Jiyao Hu

Courses

ELEC-E7320 Internet Protocols (5 ETCS). Period III-IV. Spring 2018.

ELEC-E7320 Internet Protocols (5 ETCS). Period III-IV. Spring 2017.

CSE-E5440 Energy-efficient Mobile Computing (5 ECTS). Period V. Spring 2016.

T-110.5121 Mobile Cloud Computing(5ECTS). Autumn 2015.

CSE-E5440 Energy-efficint mobile computing(5 ECTS). Period V. Spring 2015.

T-110.6120 Energy-efficient mobile computing(5 ECTS). Period V. Spring 2014.

Most recent journal/conference papers

Noreikis, M., Xiao, Y., Hu, J., Chen, Y. SnapTask: towards efficient visual crowdsourcing for indoor mapping. accepted by IEEE ICDCS'18. 11 pages.

Zhu, C., Pastor, G., Xiao, Y., Li, Y., Ylä-Jääski. A. Fog Following me: Latency and Quality Balanced Task Allocation in Vehicular Fog Computing. accepted by IEEE SECON'18. 9 pages. (acceptance rate: 23.2%)

Xie, R., Chen, Y., Lin, S., Zhang, T., Xiao, Y., Wang, X. Understanding Skout Users’ Mobility Patterns on a Global Scale: A Data-driven Study. accepted by Springer World Wide Web: Internet and Web Information Systems. 2018.

Yang, C., Chen, Y., Gong, Q., He, X., Xiao, Y., Huang, Y., Fu, X. Understanding the Behavioural Differences Between American and German Users: A Data-driven Study. accepted by Big Data Mining and Analytics. 2018. DOI: 10.26599/BDMA.2018.9020024

Zhou, Q., Chen, Y., Ma, C., Li, F., Xiao,Y., Wang, X., and Fu X. Measurement and Analysis of the Reviews in Airbnb. To appear in Proceedings of the IFIP Networking 2018 Conference. 9 pages.

Pham, T., Xiao, Y., Unsupervised Workflow Extraction from First-person Video of Mechanical Assembly. in Proceedings of the 19^th International Workshop on Mobile Computing Systems and Applications (HotMobile'18). ACM, New York, NY, USA, 31-36. DOI: https://doi.org/10.1145/3177102.3177112

Noreikis, M., Xiao, Y. Crowdsourced Geocaching for Indoor Maps Reconstruction. Poster at ACM HotMobile'2018.

Xie, R., Chen, Y., Xie, Q., Xiao, Y., and Wang, X. We Know Your Preferences in New Cities: Mining and Modeling the Behavior of Travelers. IEEE Communications Magazine. 2018.

Chen, Y., Hu, J., Zhang, H., Xiao, Y., Hui, P. Measurement and Analysis of the Swarm Social Network with Tens of Millions of Nodes. IEEE Access. vol. 6, pp. 4547-4559, 2018. doi: 10.1109/ACCESS.2018.2789915

Huang, X., Li, Y., Wang, Y., Xiao, Y., Zhang, L. CTS: A Cellular-based Trajectory Tracking System with GPS-level Accuracy, in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT). 1, 4, Article 140 (January 2018), 29 pages. DOI: https://doi.org/10.1145/3161185

W. Feng, Z. Yan, H. Zhang, K. Zeng, Y. Xiao and Y. T. Hou, "A Survey on Security, Privacy and Trust in Mobile Crowdsourcing," in IEEE Internet of Things Journal, vol. PP, no. 99, pp. 1-1. Oct 2017. doi: 10.1109/JIOT.2017.2765699 (Impact Factor: 7.596)

Marius Noreikis, Yu Xiao, and Antti Ylä-Jääski. 2017. SeeNav: Seamless and Energy-Efficient Indoor Navigation using Augmented Reality. In Proceedings of the Thematic Workshops of ACM Multimedia 2017 (Thematic Workshops '17). ACM, New York, NY, USA, 186-193. DOI: https://doi.org/10.1145/3126686.3126733

Noreikis, M., Xiao, Y., and Ylä-Jääski, A. "QoS-oriented Capacity Planning for Edge Computing". IEEE ICC'17. (camera-ready version).

Xiao, Y., Zhu, C. Vehicular Fog Computing: Vision and Challenges. IEEE Percom'17 Work-in-Progress Session. (camera-ready version).

Dong,J., Xiao, Y., Cui, Y., Ou, z, and Ylä-Jääski, A. "Indoor Tracking using Crowdsourced Maps". ACM/IEEE IPSN'16. (acceptance rate: 19.7%). Here is our demo video.

Zhu, C., Xiao, Yu., Cui, Y., Yang, Z., Xiao, S., and Ylä-Jääski, A. "Dynamic Flow Consolidation for Energy Savings in Green DCNs". IEEE IPCCC 2015. Dec 14-16, 2015. (Best Paper Award)

Hoque, M., Siekkinen, M., Khan, K., Xiao, Y., and Tarkoma, S. "Modeling, Profiling, and Debugging the Energy Consumption of Mobile Devices". ACM Computing Surveys. vol 48, issue 3, article 39 (December 2015), 40 pages.

Dong, J., Xiao, Y., Marius, N., Ou, Z., and Ylä-Jääski, A. "iMoon: Using Smartphones for Image-based Indoor Navigation".ACM SenSys 2015 (acceptance rate: 20%)

Vilen, L., Ou, Z., Xiao,Y., and Ylä-Jääski, A. "The Great Expectations of Smartphone Traffic Scheduling". ISCC 2015.

Dong, J., Xiao, Y., Ou, Z., and Ylä-Jääski, A. "Utilizing Internet Photos for Indoor Mapping and Localization - Opportunities and Challenges". accepted by the First International Workshop on Smart Cities and Urban Informatics 2015 (SmartCity 2015), co-located with Infocom 2015.

Dong, J., Xiao, Y., and Ylä-Jääski, A. "Exploring the Potential of Indoor Mapping and Localization Using Internet Photos". presented in Poster Session, HotMobile 2015.

Satyanarayanan, M., Simoens, P., Xiao,Y., and et al. "Edge Analytics in the Internet of Things". Invited paper. IEEE Pervasive Computing. April-June 2015. 8 pages.

Kämäräinen, T., Siekkinen, M., Xiao, Y., and Ylä-Jääski, A. "Towards Pervasive and Mobile Gaming with Distributed Cloud Infrastructure". NetGames 2014. (Slides)

Xiao, Y., Cui, Y., Savolainen, P., Siekkinen M., Wang A., Yang, L., Ylä-Jääski A. and Tarkoma S. "Modeling Energy Consumption of Data Transmission over Wi-Fi" (Preprint version). Accepted by IEEE Transaction on Mobile Computing. 2013.

Simoens, P., Xiao, Y., Pillai, P., Chen, Z., Ha, K., Satyanarayanan, M. "Scalable Crowd-Sourcing of Video from Mobile Devices"(submission version). ACM Mobisys 2013 (acceptance rate: 16%), Taipei, Taiwan, June 25-28, 2013.

Ding, Y., Han, B., Xiao, Y., Hui, P., Srinivasan A., Kojo M., and Tarkoma S. "Enabling Energy-aware Collaborative Mobile Data Offloading for Smartphones". IEEE Secon 2013, New Orleans, USA, June 24-27, 2013.

Xiao, Y., Simoens, P., Pillai, P., Ha, K., Satyanarayanan, M. "Lowering the Barriers to Large-Scale Mobile Crowdsensing" (submission version) , in Proceedings of the 14th International Workshop on Mobile Computing Systems and Applications (ACM HotMobile 2013), February 2013, Jekyll Island, GA.

Other publications can be found from Google Scholarhttp://scholar.google.com/citations?user=ZeRhyWsAAA

Books

Tarkoma, S., Siekkinen M., Lagerspetz, E., and Xiao, Y. "Smartphone energy consumption: modelling and optimization". Cambridge University Press. September 2014. ISBN: 9781107042339.

Software

1. Crowdsourced Indoor Mapping, Localization and Navigation

A demo video of our image-based indoor mapping, localization and navigation system is now available on YouTube. Link

Our system builds 3D models of indoor environment from unordered 2D images using Structure-from-Motion(SfM) techniques, detects pedestrian paths from smartphone sensor data, and extracts place information from images. Our system includes an Android client and an app server. With the client, users can locate themselves by taking photos from where they are, can search for places by descriptions or images, and can be guided to destinations with textual and visual information.

2. Gigasight: Crowdsourcing of videos from mobile devices (source code is available on GitHub)

I participated the design and development of Gigasight during my research visit to Carnegie Mellon University in year 2012. This work was published in Mobisys 2013.

3. Integration of pervasive public display networks and distributed cloudlet infrastructure

It is a joint work with Lancaster University during my research visit to Carnegie Mellon University in year 2013.

Key techniques: Qemu/KVM, Internet suspend/resume, Node.js + MySql

4. Tools for energy-efficiency analysis

SmartDiet is a proof-of-concept toolkit for analyzing energy usage of Android applications and identifying constraints regarding mobile code offloading. It was mostly written by Aki Saarinen and the source code is available on GitHub. We made a demo of SmartDiet at Sigcomm 2012. The demo description can be found from here.

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