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Assistant Professor Department of Computer
Science and Engineering
Office: 108 Dodge Hall of Engineering Building |
Current Projects
Wireless Security. As the increasingly pervasive wireless networks make it even easier to conduct attacks for new and rapidly evolving adversaries, the ubiquity of wireless imposes new challenges on classical security measures such as cryptography. Thus, there is an urgent need to seek security solutions that can defend against attacks across the current heterogeneous mixes of wireless technologies. One of the promising ideas is to exploit the wireless channel physical layer information for providing trustworthy and reliable future wireless networks. In this project, we exploit the wireless channel physical layer information to assist cryptographic-based methods to solve fundamental security problems such as detecting identity-based attacks and providing location-aware secure access of network resources..
Driver Phone Use Detection. Cell phone distractions have been a factor in high-profile accidents and are associated with a large number of automobile accidents. This project addresses the fundamental problem of distinguishing between a driver and passenger using a mobile phone, which is the critical input to enable numerous safety and interface enhancements for the driver distraction problem. We are building a detection system that leverages the existing car stereo infrastructure, e.g., the speakers and Bluetooth network. Our solution seeks to address major challenges including the complex multipath environment presented in the small confided space inside a car, minimizing interference between the speakers, and any sounds emitted should be unobtrusive to minimize distraction. This project has received the Best Paper Award in ACM MobiCom 2011.
Press Release: MIT Technology Review, The Wall Street Journal, CNET News, Yahoo News, Autoblog, Stevens News...
Passive Intrusion Learning in Pervasive Wireless Environments. This project seeks to develop effective and scalable multi-modal passive intrusion learning techniques that have the capability to detect and track device-free moving objects in pervasive wireless environments through adaptive learning. In contrast to traditional techniques, which require pre-deployment of specialized hardware, and thus not easily deployed for unscheduled tasks and may not be scalable, this project leads to new insights into intrusion learning by mining on wireless environmental data, as well as leading to new approaches to device-free wireless localization, which can be used to assist a broad array of applications, e.g., identification of people trapped in a fire building during emergency evacuation.
Smartphone Indoor Localization. Mobile apps, especially those location based ones, are changing the way people work and live every day, and many such apps have to deal with an indoor environment, e.g., shopping malls and airports. In many such environments, the availability of indoor location information can be used to help individuals (directions, just-in-time coupons/promotions) and organizations (passenger flow distribution in airports, customer shopping/movements' pattern in malls). All these apps would require a practical, robust and efficient smartphone indoor localization solution. We are studying a practical and energy efficient indoor localization solution leveraging multiple sensing modalities enabled by smartphones.
Defending against Identity-Based Attacks in Wireless
Networks
Yingying Chen and Jie Yang
Securing Cyber-Physical Critical
Infrastructure: Foundations and Challenges, Sajal Das,
Krishna Kant and Nan Zhang (eds.), Morgan Kauffman, chapter
2-4, Jan., 2012.
On the Performance of Wireless Indoor
Localization Using Received Signal Strength
Jie Yang, Yingying Chen, R. P. Martin, and M. Gruteser
Position
Location - Theory, Practice and Advances: A Handbook for Engineers and
Academics, Seyed A. (Reza) Zekavat, Mike Buehrer (eds.), Wiley-IEEE,
chapter 12, 2011.
Impact of Anchor Placement and
Anchor Selection on Localization Accuracy
Yingying Chen, Jie Yang, Wade Trappe and R. P. Martin
Position Location
- Theory, Practice and Advances: A Handbook for Engineers and Academics,
Seyed A. (Reza) Zekavat, Mike Buehrer (eds.), Wiley-IEEE, chapter 13, 2011.
A
Reinforcement Learning Based Framework for Prediction of
Near Likely Nodes in Data-Centric Mobile Wireless Networks
Yingying Chen, Wang Hui,
Xiuyuan Zheng and Jie Yang,
EURASIP Journal on Wireless Communications and Networking
(JWCN),
vol. 2010 (319275),
,
Jun. 2010.
Fast object recognition using local
scale-invariant features
Zhiqian Zhou, Bo Wang, Jie Yang, and Ji Lu
Optical Technique, vol. 34, no. 5, pp.
742 – 745, 2008.
