Illustrative Abstracts: External Grants

Data is the lifeblood of science. Researchers need the widest possible access to data from all sources to explore, experiment, test, and evaluate theories; and, ultimately, to increase understanding of our world. Data access, especially with the advent of the digital age, is crucial to the future development of science and society.

Researchers across a spectrum of disciplines—spanning infectious disease tracking, emergency services, cartography, digital communication, and many other fields—depend on locationally-referenced data from many sources to advance human knowledge. Those sources currently include national, state, and some local governments, as well as large commercial companies. Fortunately, there are many initiatives underway to make this government and commercial information accessible to present and future generations.

However, none of these initiatives addresses access to a significant body of “invisible” non-textual geographic data that exists on the local hard drives of individual researchers, schools, non-profit groups, private associations, small companies, and other non-governmental organizations.

At the University of Maine, Fogler Library, in partnership with faculty from the Department of Spatial Information Science and Engineering, is proposing the creation of Commons of Geographic Data (CGD) System software. This project will research and design an open source software system that will provide libraries and their users, as well as creators of locally generated non-textual geographic data, with (1) reliable knowledge of intellectual property ownership and use conditions for the data; (2) easy generation of standards-based metadata; (3) reliable data provenance in a digital environment across many possible re-uses; and (4) peer-generated indicators of quality and suitability for use of the data.

The CGD System, using open-source and open-access technology, will enable libraries to remove technical and legal barriers facing individual researchers, local government agencies, nonprofit organizations, field scientists, and individual citizens who wish to contribute, access, and use locally generated non-textual geographic information. Through libraries, this project will help free up currently unavailable “invisible” information generated by non-federal local sources, and make that data available to the widest possible range of potential users. The CGD System will create an infrastructure that can be used by anyone who wishes to contribute geographic data but does not wish to become a specialist in geographic metadata generation or intellectual property management.

The CGD System will build upon existing efforts in disparate domains and will create a new, integrated, easy-to-use software-based process that will enable libraries to collect, organize, and make accessible non-textual geographic data generated by non-specialists in a simple, intuitive automated “one-stop” manner. No system exists at present that simultaneously addresses all of these critical library concerns.

The Commons of Geographic Data System creates a tool that will enable libraries to efficiently collect, organize, and make readily available currently “invisible” geographic non-textual data, both today and in the Semantic Web environment of the future.

This project explores data management methods for geosensor networks, i.e. large collections of very small, battery-driven sensor nodes deployed in the geographic environment that measure the temporal and spatial variations of physical quantities such as temperature or ozone levels. An important  task of such geosensor networks is to collect, analyze and  estimate information about continuous phenomena under observation such as a toxic cloud close to a chemical plant in real-time and in an energy-efficient way. The main thrust of this project is the integration of spatial data analysis techniques with in-network data query execution in sensor networks. The project investigates novel algorithms such as incremental, in-network kriging that redefines a traditional, highly computationally-intensive spatial data estimation method for a distributed, collaborative and incremental processing between tiny, energy and bandwidth constrained sensor nodes. This work includes the modeling of location and sensing characteristics of sensor devices with regard to observed phenomena, the support of temporal-spatial estimation queries, and a focus on in-network data aggregation  algorithms for complex spatial estimation queries. Combining high-level data query interfaces with advanced spatial analysis methods will allow domain scientists to use sensor networks effectively in environmental observation.

Advances in hardware and systems software provide the capability for large numbers of small, low-cost MEMS devices with
limited on-board processing and wireless communication capabilities to be placed in the field. Environmental monitoring
is one of the major application area for geosensor networks, sensor networks embedded in  geographic regions. The
goal is the observation, monitoring and analysis of environmental phenomena such as wildfires, flooding, and detection and tracking of toxic spills. Issues such as energy and communication constraints have up to now prevented the full potential of such networks to be realized. This proposal is concerned with the responsiveness of such geosensor networks to changes in dynamic spatial fields. Imagine a geosensor network detecting levels of carbon dioxide pollution at various places on a region of the Earth. Because of energy and communication constraints, only a small proportion of the sensor nodes can be active at any one time. As CO₂ levels change (maybe the region of dangerously high levels is splitting into two connected parts), the sensor network needs to be responsive to the change, for example, reconfiguring itself by activating/deactivating sensor nodes,  to capture the detail of the field of CO₂ where is is most needed (i.e., where the split is taking place). The proposal directly addresses the collaborative system topic of problem solving in highly distributed, sensor-based information networks, by the provision of a framework for optimizing and updating information flow between sensor node

The University of Maine is a proven leader in integrating cutting-edge sensor science, engineering, and informatics research into high school, undergraduate, and graduate education. Faculty, students, and alumni are developing novel sensor applications for homeland security, healthcare, the environment, energy, agriculture, food safety, transportation, manufacturing, mapping, and other areas. As an indicator of their success, faculty and alumni continue to spin off numerous sensor-related companies. The Internet and information technologies, coupled with miniaturization techniques and new approaches to informatics, are propelling sensor technology to a threshold of major growth. As sensing becomes ubiquitous, there is a critical need to manage, integrate and analyze diverse and even conflicting sensor data streams. A challenging gulf persists between the raw and massive amounts of data produced by even a single sensor and the ability of collaborating sensors to generate integrated information that is useful for decision-making. This gulf occurs in part when the researchers developing the sensors and sensor systems are unaware of the needs of those who must analyze and respond to the data. Similarly, those involved on the informatics side are often unfamiliar with the potential and challenges of new sensor materials, devices, and platforms. New approaches to graduate education are needed to bridge this gulf.

The primary goal of this IGERT program is to train Ph.D. scientists and engineers in the multidisciplinary area of sensor systems ranging from the design and networking of sensors to the interpretation of complex sensor data. IGERT fellows will develop a systems view that embraces sensor science, engineering, and informatics and emphasizes a high degree of professionalism, marked by leadership skills, the ability to contribute effectively within an interdisciplinary team, and appreciation for the complex social and ethical ramifications of ubiquitous sensing. IGERT fellows will complete an innovative sequence of courses, theses, and other activities to develop knowledge and skills in (i) sensor materials and devices (ii) sensor signal conditioning and networks and (iii) the integration and transformation of raw sensor data streams into knowledge.

Twenty faculty members from 5 departments (Spatial Information Science and Engineering, Electrical and Computer Engineering, Chemical and Biological Engineering, Chemistry, and Physics) will train 20 IGERT fellows over 5 years. While collaborations exist among researchers studying new materials and sensing modalities, collaboration with researchers in signal conditioning, networking, and informatics is far less common even within the same domain, i.e., homeland security. This IGERT program will connect the detection and meaningful interpretation of a ìsensedî event. Innovative aspects include: (i) highly interdisciplinary summer symposia with internationally-recognized scholars, policy-makers, and industry leaders, (ii) a new, interdisciplinary, certificate program that includes a new, team-building design course and entrepreneurship courses (iii) interdisciplinary research experiences that pair fellows with at least two advisors representing diverse aspects of sensor science and engineering (e.g. materials characterization and data fusion), and (iv.) extensive interaction with high school students and teachers. An external advisory board will provide program oversight.

The intellectual merit of this IGERT program rests in its novel emphasis on sensing at scales from nano to global and its innovative approach to cross-training graduate students on integrated sensing systems from event detection to spatio-temporal information analysis including the social issues relating to sensor systems.

Broader impacts include integration of IGERT activities with UMaine’s NSF-funded GK-12: Sensors! and RET Site: Sensors! (affecting at least 40 high schools, many of which are located in poor and isolated regions); entrepreneurship training that better prepares Ph.D. students to meet the sensor-related needs of industry; incorporation of ethical and public policy dimension of sensing; increased research collaborations among some of the University’s major research units including the Laboratory for Surface Science and Technology and the National Center for Geographic Information and Analysis.

Note: Other grant abstracts that would need to be added by faculty from their individual files include:

• Smart Maps - NGA
• Automated Image Based Linear Feature Extraction and Updating - NGA
• Spatio-Temporal Analysis for Environmental Health - NIH
• Framework for Spatio-temporal Analysis of Geospatial Data using distributed Motion Imagery-NGA
• Digital Government – Time Varying Geospatial Datasets- NSF
• GeoGrid for Next Generation Geospatial Information NSF- ITR
• Matching Levels of Detail in Descriptions and Depictions of geographic space

There are many challenges facing analysts who must fit complex pieces of geospatially-related information together in a meaningful way. Geospatial data collected from multiple and heterogeneous sources require methods for integrating and de-conflicting the data. This proposal focuses on the topic of spatio-temporal data fusion. We propose a novel approach to next generation data fusion where geospatial objects are linked together with events and temporal aspects to produce an integrated reasoning framework that incorporates static and dynamic object views over multiple levels of detail. This approach has the advantage of allowing domain experts, such as image analysts or GIS specialists, to use software tools that exploit available classifications or categories of data to construct integrated spatio-temporal granularity spaces that are more comprehensive than the original separate base classifications and useful for guiding generalizations that derive from the combined data sets. The ability to combining time, space, objects, and events over multiple granularities will extend an analyst’s knowledge base and the proposed software development will provide a suite of tools for supporting spatio-temporal data fusion in a graphical environment.