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Workshop on Broadband Advanced Sensor Networks
[ http://www.basenets.org ]

 Co-chairs Dr. Wendi Heinzelman [University of Rochester]
Dr. Bhaskar Krishnamachari
[University of Southern California]
Dr. Holger Karl [University of Paderborn]
Dr. Rick Han [University of Colorado at Boulder]
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Schedule and Technical Program
Invited Paper
October 25, 2004 [Monday] 08:00 - 08:15 AM
Opening Remarks
October 25, 2004 [Monday] 08:15 - 09:30 AM
Enabling Applications in Broadband Sensor Networks
Applications and Design of Heterogeneous and/or Broadband Sensor Networks [PDF]
L. Yuan, C. Gui, C. Chuah, and P. Mohapatra [UC-Davis]

Enabling Applications in Sensor-based Pervasive Environments [PDF]
N. Jiang, C. Schmidt, V. Matossian, and M. Parashar [Rutgers University]

Middleware Guidelines for Future Sensor Networks [PDF]
M. Wolenetz, R. Kumar, J. Shin, and U. Ramachandran [Georgia Tech.]

October 25, 2004 [Monday] 09:30 - 09:50 AM
Coffee Break
October 25, 2004 [Monday] 09:50 - 11:30 AM
Information Processing in Broadband Sensor Networks
A Distributed Filtering Architecture for Multimedia Sensors [PDF]
S. Nath and Y. Ke [CMU], P. Gibbons and B. Karp [Intel Research], S. Seshan [CMU]

Secure and Efficient In-Network Processing for Sensor Networks [PDF]
T. Dimitriou [Athens Information Technology], Dimitris Foteinakis [Intracom S.A.]

Distributed Metric Calibration of Large Camera Networks [PDF]
D. Devarajan and R. Radke [Rensselaer Polytechnic Institute]

Sensor Tasking for Occupancy Reasoning in a Network of Cameras [PDF]
Danny B. Yang, Jaewon Shin, Ali Ozer Ercan, and Leonidas J. Guibas [Stanford University]

October 25, 2004 [Monday] 11:30 AM - 01:30 PM
October 25, 2004 [Monday] 01:30 - 02:30 PM
KEYNOTE: Event Sensing on Distributed Video-Sensor Networks
Prof. Edward Chang, University of California, Santa Barbara

Abstract: Video sensors (video cameras) and wireless broadband networks are becoming ubiquitous features of modern life. The confluence of these two technologies now makes it possible to construct wireless ad hoc networks of multiple video sensors that can be rapidly deployed, dynamically configured, and continuously operated to provide highly-available coverage for environment monitoring and security surveillance. While many extended “eyes” are being installed at an unprecedented pace, the intelligence needed for interpreting video-sensing events by computers is still rather unsophisticated.  In addition, algorithms that can scale well with the number of sensors and high-volume of data are yet to be developed for effectively managing and mining video-sensor data streams. In order to develop a “brain” behind a large number of optical “eyes” to support (semi-) automatic event sensing, statistical methods are essential for improving the two major phases of event sensing: 1) data fusion and 2) event mining.  The data-fusion phase integrates multi-source data to detect and extract motion trajectories from video sources.  The event-mining phase deals with classifying the events as to relevance for the query.  This talk presents recently developed statistical methods in energy-preserving data sampling and filtering, spatio-temporal data fusion, sequence-data mining, change and outlier detection, and multi-resolution and multimodal statistical learning. We will also discuss future directions that research might take.

Biography: Professor Chang received his M.S. in Computer Science and PhD in Electrical Engineering at Stanford University in 1994 and 1999, respectively. Since 2003, he is an Associate Professor of Electrical and Computer Engineering at the University of California, Santa Barbara. His recent research activities are in the areas of image/video databases, machine learning, data mining, and high-performance IO systems. He is particularly interested in application of machine-learning theoretical approaches to fundamental problems in image/video retrieval and sensor-data fusion and mining. Recent research contributions of his group include methods for learning image/video query concepts via active learning, formulating distance functions via dynamic associations and kernel alignment, managing and fusing distributed video-sensor data, and categorizing and indexing high-dimensional image/video information. Professor Chang has served on several conference program committees including ACM SIGMOD, ACM Multimedia, ACM CIKM, SIAM Data Mining, International Conference on Artificial Intelligence, International Conference on Computer Vision, and etc. He co-chaired the first two annual ACM Video Sensor Network Workshop in 2003 and 2004, and will co-chair three major Multimedia conferences in the next two years.  He serves as an Associate Editor for IEEE Transactions on Knowledge and Data Engineering. Professor Chang is a recipient of the IBM Faculty Partnership Award from 2000 to 2002, and the NSF Career Award in 2002. He is a founding member of two startup ventures: VIMA Technologies, which provides image searching and filtering solutions; Proximex, which supports homeland security applications.

October 25, 2004 [Monday] 02:30 - 03:10 PM
Deployment of Broadband Sensor Networks
Low-Power Image Transmission in Wireless Sensor Networks using ScatterWeb Technologies [PDF]
E. Kappe, A. Liers, H. Ritter, and J. Schiller [Freie Universitat Berlin]

Deploying Long-Lived and Cost-effective Hybrid Sensor Networks [PDF]
W. Hu [The University of NSW and National ICT Australia Limited] C. Chou [The University of NSW], S. Jha [The Universitand Sciences University]

October 25, 2004 [Monday] 03:10 - 03:40 PM
Coffee Break
October 25, 2004 [Monday] 03:40 - 05:00 PM
Networking Broadband Sensors
Lifetime-Optimal Data Routing in Wireless Sensor Networks Without Flow Splitting [PDF]
T. Hou and Y. Shi [Virginia Tech], J. Pan [University of Waterloo], S. Midkiff [Virginia Tech]

Adaptive Resource Control Scheme to Alleviate Congestion in Sensor Networks [PDF]
J. Kang, B. Nath, Y. Zhang, and S. Yu [Rutgers University]

Energy-Efficient Clustering Method for Data Gathering in Sensor Networks [PDF]
J. Kamimura, N. Wakamiya, and M. Murata [Osaka University]