Award Abstract #0540420
DDDAS-TMRP: A Generic Multi-scale Modeling Framework for Reactive Observing Systems

NSF Org:	CNS Division of Computer and Network Systems
Initial Amendment Date:	September 16, 2005
Latest Amendment Date:	September 25, 2007
Award Number:	0540420
Award Instrument:	Continuing grant
Program Manager:	Anita J. LaSalle CNS Division of Computer and Network Systems CSE Directorate for Computer & Information Science & Engineering
Start Date:	January 1, 2006
Expires:	December 31, 2010 (Estimated)
Awarded Amount to Date:	$949851
Investigator(s):	Leana Golubchik leana@cs.usc.edu (Principal Investigator) David Caron (Co-Principal Investigator) Ramesh Govindan (Co-Principal Investigator) Gaurav Sukhatme (Co-Principal Investigator) David Kempe (Co-Principal Investigator)
Sponsor:	University of Southern California University Park Los Angeles, CA 90089 213/740-7762
NSF Program(s):	DYNAMIC DATA DRIVEN APPL SYSTS, COMPUTER SYSTEMS
Field Application(s):	0000912 Computer Science
Program Reference Code(s):	HPCC, 9218
Program Element Code(s):	S115, 7481, 7354

ABSTRACT

Observing systems facilitate scientific studies by instrumenting the real world and collecting corresponding measurements, with the aim of detecting and tracking phenomena of interest. In this proposal, we focus on a class of observing systems which are (1) embedded into the environment, (2) consist of stationary and mobile sensors, and (3) react to collected observations by reconfiguring the system and adapting which observations are collected next, these are referred to as Reactive Observing Systems (ROS). The goal of ROS is to help scientists verify or falsify hypotheses with useful samples taken by the stationary and mobile units, as well as to analyze data autonomously to discover interesting trends or alarming conditions.

A wide range of critical environmental monitoring objectives in resource management, environmental protection, and public health all require distributed observing systems. This project will explore ROS in the context of a marine biology application, where the system monitors, e.g., water temperature and light as well as concentrations of micro-organisms and algae in a body of water. Using a hybrid network of stationary and mobile sensors, communicating both via wired and wireless links, the system collects fine-grained measurements of interesting information in near real-time. An example of the use of such a system is the rapid identification of micro-organisms to predict the onset of algal blooms. Such blooms can have devastating economic consequences.

Current technology precludes sampling all possibly relevant data. Therefore there is need to develop approaches for optimizing and controlling the set of samples to be taken at any given time, taking into consideration the application's objectives and system resource constraints. To support such an optimization and control process, a significant part of the framework must be dedicated to the development of models of data, and their automatic validation or adaptation. As part of the validation and adaptation process, the framework must also include a distributed support mechanism for locating data of interest. The methods to be pursued in the project include a multi-scale modeling framework for ROS, that allows applications to construct inter-related models of varying spatio-temporal scope based on collected data. Guided by the models, the reactive elements of the system predict where interesting data and phenomena are likely to be found. In the process of constructing models, the system actively seeks most useful data to improve both, the models and phenomenon detection and tracking. In a feedback cycle, this data acquisition is guided by previous, perhaps less precise, models. Thus, the system to be developed (AMBROsia) enables optimal collection of measurements in a manner that respects system resource constraints, yet improves the overall fidelity of phenomenon detection and tracking. Such a system will aid scientific research by facilitating the testing of scientific hypothesis. It will provide timely predictions of sampling needs, and tracking information for dynamic phenomena. Overall, AMBROSia will facilitate observation, detection, and tracking of scientific phenomena that were previous only partially (or not at all) observable and/or understood.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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