CENS Technical Seminar Series

Enabling wireless sensor networks for robust estimation and real-time control

Presentation Slides (pdf)

Invited Speaker: Prof. Songhwai Oh, CS, University of California, Merced
Date: July 20, 2007
Time: 1:00 PM - 2:00 PM
Venue: CENS Main Conference Room, 3551 Boelter Hall, UCLA

Abstract

There is a growing interest in distributed networked sensing and control systems, such as wireless sensor networks, networked control systems, and multi-agent systems. A distributed networked sensing and control system consists of a number of autonomous agents. Information among these agents is usually shared by wireless communication. However, each agent is resource constrained, e.g., limited processing power, storage capacity, and communication bandwidth. These constraints create measurement inconsistency and communication unreliability and they are the major obstacles in realizing an autonomous distributed networked sensing and control system which is capable of real-time situation understanding and control.

In this talk, I'll describe the main challenges in developing a real-time control system for pursuit-evasion games with the aid of a large scale sensor network. These challenges arise from the inconsistency of sensor measurements due to packet loss, communication delay, and false detections, and from the necessity of optimal coordination of a large number of agents. Novel algorithms based on multiple layers of data fusion and on a real-time hierarchical coordination architecture are proposed and successfully demonstrated in a large-scale outdoor wireless sensor network. In particular, I'll describe a Bayesian framework for multi-target tracking. This Bayesian framework allows a method which is robust against measurement inconsistency and communication unreliability. Since the exact computation of Bayesian estimates is a time-consuming task, I propose an approximate method, called Markov chain Monte Carlo data association (MCMCDA), to efficiently solve the data association problems appearing in multi-target tracking problems.

Biography

Songhwai Oh is an assistant professor of Computer Science in the School of Engineering at the University of California, Merced. His research interests include wireless sensor networks, networked control systems, estimation and learning, and computer vision. He received all his degrees in Electrical Engineering and Computer Sciences at the University of California, Berkeley (B.S. in 1995, M.S. in 2003, and Ph.D. in 2006). In 2007, he was a postdoctoral researcher in Electrical Engineering and Computer Sciences at the University of California, Berkeley. Before his Ph.D. studies, he worked as a senior software engineer at Synopsys, Inc. and a microprocessor design engineer at Intel Corporation.