Methodology for Monitoring Complex Systems Using Large Scale Sensor Networks

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This grant provides funding to establish a unified framework for the management of large-scale, embedded sensor networks for the health monitoring of manufacturing enterprises and complex systems operating in stochastic environments. The project will focus on fundamental research issues in the areas of distributed inferencing and decision-making for adaptive sensing and sensor communication. Specifically, it will address the following issues: 1) development of a theoretical basis for managing large number of networked sensors that collectively form an "intelligent" sensing grid, capable of making distributed inferences with guarantees on consistency of data interpretation, and 2) deployment of architectural configuration and parametric design of networkable, miniaturized sensors to physically realize the proposed "intelligent" sensing grid using a novel "agent-on-a-chip" (AOC) firmware.If successful, the results of this research will lead to improvements in two areas. First, the research will lead to a new theory and quantitative guidelines for the architectural design and deployment of networked sensors that are characterized by the ability to perform event-driven, dynamic inferencing and distributed decision-making at the local sensor level. Secondly, it will demonstrate, the feasibility of a new sensor network architecture that will physically embody, bench test, and validate a large number of sensors prototyped based on the state-of-the-art miniaturization technologies. Such hardware and software will be applicable in a wide range of application areas, including manufacturing, transportation, health care, medical devices, environmental control, and homeland security. The broader impact of this research will be in bridging the gap between fundamental research on decision theory, Bayesian networks, and embedded sensing chip design in the academia and real-world applications in a manufacturing environment. On the educational front, the proposed activity will contribute to multidisciplinary training of graduate and undergraduate students in the area of management of large-scale sensor networks.