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.
Here is the link to our group website .
1) PI, 5G-Mobix: 5G for cooperative & connected automated mobility on x-border corridors, funded by European Commission, 11.2018 - 10.2021
2) Primo-5G: Virtual presence in moving objects through 5G, funded by European Commission, 2018 - January 2021
3) PI, CEAMA: Cognitive Engine for Assembly and Maintenance Automation, funded by Business Finland, August 2018 - January 2020.
keywords: workflow extraction, activity recognition, wearable cognitive assistance
4) 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
5) EIT HII Active - Advanced Connectivity Platform for Vertical Segments. September 2017 - December 2018.
Keywords: low-latency, wearable computing, edge computing
1) Intelligent Construction Site, funded by Tekes, October 2016 - September 2018 (joint work with department of civil engineering)
Keywords: indoor tracking, mobile sensing
2) 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
3) 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.
4) 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
Giancarlo Pastor, 2017.09 - , fog/edge computing, compressed sensing, reinforcement learning
Jihye Lee, 2018.09- , UX/UI design in AR/VR
Yuki Sato. 2017.09 - 2018.06. Hand gesture recognition, RFID mapping.
Clayton Frederick Souza Leite (Aalto/COMNET), 2018.11-, AI-driven interaction design in AR/VR
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
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.
Dong, J., Noreikis, M., Xiao, Y., Ylä-Jääski, A. ViNav: A Vision-based Indoor Navigation System for Smartphones. to appear in IEEE Transactions on Mobile Computing.
Zhu, C., Pastor, G., Xiao, Y., Ylä-Jääski, A. Vehicular Fog Computing for Visual Crowdsourcing: Applications, Feasibility and Challenges. to appear in IEEE Communications Magazine.
Noreikis, M., Xiao, Y., Hu, J., Chen, Y. SnapTask: towards efficient visual crowdsourcing for indoor mapping. IEEE ICDCS'18. 11 pages. (acceptance rate: 20.63%)
Zhu, C., Pastor, G., Xiao, Y., Li, Y., Ylä-Jääski. A. Fog Following me: Latency and Quality Balanced Task Allocation in Vehicular Fog Computing. 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. Springer World Wide Web: Internet and Web Information Systems. 2018.
Pham, T., Xiao, Y., Unsupervised Workflow Extraction from First-person Video of Mechanical Assembly. in Proceedings of the 19th 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
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.
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)
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%)
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.
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.
Other publications can be found from Google Scholar http://scholar.google.com/citations?user=ZeRhyWsAAA
Tarkoma, S., Siekkinen M., Lagerspetz, E., and Xiao, Y. "Smartphone energy consumption: modelling and optimization". Cambridge University Press. September 2014. ISBN: 9781107042339.
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.