Estimation Problems in Sense and Respond Systems

Author: Capponi, Agostino

Year: 2006

Degree: Master's thesis

Advisor: Chandy, K. Mani

Committee Member: Unknown, Unknown

Option: Computer Science

DOI: 10.7907/YZF9-ZN10

Abstract

In this thesis we study problems arising in the design of sense and respond systems and present analytical solutions to them as well as results from experiments dealing with real systems. Sense and respond systems employ sensors and other sources of data to sense what is happening in their environments, process the obtained information, and respond appropriately. A goal of the processing stage is to reconstruct the best possible estimate of the state of the environment using messages received from sensors. Due to the large number of messages that need to be processed, it is desirable to have algorithms that can incrementally process the received measurements and recover the state. The state estimation process becomes more problematic if measurements obtained from the sensors are noisy or they are sent at unpredictable times. First, we study models of state estimation and present algorithms that can incrementally compute accurate linear state estimates of the surrounding environment. Second, we define a framework called predicate signaling that allows us to make tradeoffs between message generation rates and the quality of the state estimate through specification of suitable predicates. We show how predicate signaling generalizes commonly used signaling schemes and present a detailed analysis based on stochastic processes to evaluate schemes based on predicate signaling.

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