Yingying Chen, Assistant Professor, ECE, Stevens Institute of Technology
Date: November 07, 2007 (Wednesday)
Time: 11:00 am (refreshment starts at 10:45 am)
Location: 202 ECEC, NJIT

About the Presenter:

Yingying (Jennifer) Chen is an assistant professor in the Electrical and Computer Engineering Department at Stevens Institute of Technology. Dr. Chen received her Ph.D. from Computer Science Department at Rutgers University. She holds a MS degree in Computer Science from North Carolina State University and a BS in Physics from Nanjing University, China. Her research interests span network and information system security, software engineering and security in distributed systems, as well as wireless and sensor networks. She is a member of Wireless Information Network Laboratory (WINLAB) at Rutgers University. She has won the Best Technological Innovation Award for the Real Time Statistical Bayesian Positioning system at the 3rd International TinyOS Technology Exchange in 2006. Prior to join Stevens Institute of Technology, Dr. Chen was with Bell Laboratories and Optical Networking Group, Lucent Technologies ( Holmdel & Murray Hill, New Jersey). Her work has involved a combination of research and development of new technologies and real network systems. Concurrently, she was an instructor in the Computer Science Department at Rutgers University. She also worked as a system researcher for embedded networks at Alcatel Network Systems (Raleigh, NC) previously.

About the Talk:

Obtaining accurate positions of nodes in wireless and sensor networks is important because the location of wireless devices is a critical input to many high-level services. Such services include healthcare monitoring, wildlife animal habitat tracking, emergency rescue and recovery, location-based access control, and location-aware content delivery. However, the localization infrastructure can be subjected to non-cryptographic attacks which cannot be addressed by traditional security services. Further, location services are only useful if the location information is accurate and trustworthy. Thus it is desirable to explore solutions to detect and eliminate these attacks from the network. In this talk, I first describe a generalized localization model and a representative set of localization algorithms. Then, I present our study on the robustness of these algorithms to signal strength attacks.

For more information contact: Yun Shi shi@njit.edu (973)-596-3501, Alfredo Tan tan@fdu.edu (201) 692-2347, and Hong Man hman@stevens-tech.edu (201)-216-5038.

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Note: All ECE MS thesis defense and Ph.D. dissertation (proposal) defense are counted towards ECE791.