PhD Defense: "Networked Estimation and Communication with Minimalist Models"

Sriram Venkateswaran

December 5th (Monday), 2:00pm
Engineering Science Building (ESB), Room 2001

Advances in miniaturizing sensors and increases in computing capacity have led to networks of sensors being deployed to solve a wide variety of problems. In this talk, we provide two examples to show that we can solve complex problems in such networks even with minimalist observation and communication models.

First, we propose a scheme to maintain synchrony in a Time Division Multiplexed network with minimal overhead. Each node estimates the offset in its clock phase with its neighbors based on the differences between the expected and actual times at which it receives communication packets. Using such estimates, the nodes adjust their clock phases every time they receive a packet and also adjust their clock frequencies on a slower timescale. We provide fundamental theoretical insight by analyzing a simpler “averaged” system and use simulations to demonstrate the efficacy of the algorithm.

Next, we consider the problem of localizing multiple events that are closely spaced in time, based solely on their Times of Arrival (ToAs) at different sensors.The challenge is to identify and group the ToAs belonging to a given event before localizing it. The naive approach of trying all possible groupings suffers from excessive complexity. We design a three-stage algorithm to sidestep such bottlenecks. The key simplification comes from the first stage, where we discretize the times at which events occur to reduce the set of event candidates considerably. However, some of these candidates are “phantoms” (no event occurred there) that arise because we do not know the correct groupings. We refine the estimates in a Bayesian manner and use a variant of the matching problem on a graph to reject the phantoms and group the ToAs appropriately. We use simulations to illustrate the near-optimal localization performance and a real-time demonstration to show that the algorithm is robust and has low complexity.

About Sriram Venkateswaran:

Sriram Venkateswaran received his bachelor's degree in Electrical Engineering from the Indian Institute of Technology Madras in 2006. He received the M.S degree in Electrical and Computer Engineering from the University of California Santa Barbara in 2007. Sriram is currently pursuing a Ph. D. degree in Electrical and Computer Engineering under the guidance of Prof. Upamanyu Madhow. His research interests lie in sensor and communication networks. He interned at Qualcomm Inc., Santa Clara during the summer of 2010.

Hosted by: Professor Upamanyu Madhow