Monitoring Dynamic Spatial Fields Using Responsive Geosensor Networks

Many environmental phenomena (e.g., changes in global levels of atmospheric carbon dioxide) can be modeled as variations of attributes over regions of space and time, called dynamic spatial fields. The goal of this project is to provide efficient ways for sensor networks to monitor such fields, and to report significant changes in them. The focus is on qualitative changes, such as splitting of areas or emergence of holes in a region of study. The approach is to develop qualitative and topological methods to deal with changes. Qualitative properties form a small, discrete space, whereas quantitative values form a large, continuous space, and this enables efficiencies to be gained over traditional quantitative methods. The combinatorial map model of the spatial embedding of the sensor network is rich enough so that for each sensor, its position, and the distances and bearings of neighboring sensors, are easily computed. The sensors are responsive to changes to the spatial field, so that sensors are activated in the vicinity of interesting developments in the field, while sensors are deactivated in quiescent locations. All computation and message passing is local , with no centralized control. Optimization is addressed through use of techniques in qualitative representation and reasoning, and efficient update through a dynamic and responsive underlying spatial framework. Effective deployment of very large arrays of sensors for environmental monitoring has important scientific and societal benefits. The project is integrated with the NSF IGERT program on Sensor Science, Engineering, and Informatics at the University of Maine, which will enhance educational and outreach opportunities. The project Web site (http://www.spatial.maine.edu/~worboys/sensors.html) will be used for broad results dissemination

SEI+II Information Integration Through Events

Many environmental observations are collected at different space and time scales that preclude easy integration of the data and hinder broader understanding of ecosystem dynamics. Ocean Observing Systems provide a specific example of multi-sensor systems observing several variables in different space - time regimes. This project integrates diverse space-time environmental sensor streams based on the conversion of their information content to a common higher-level abstraction: a space-time event data type. The space-time event data type normalizes across the diversity of observation level data to produce a common data type for exploration and analysis. Gulf of Maine Ocean Observing System (GOMOOS) data provide the multivariate time and space-time series from which space-time events are detected and assembled. Event detection employs a combined top down-bottom up approach. The top down component specifies an event ontology while the bottom up component is based on extraction of primitive events (e.g. decreasing, increasing, local maxima and minima sequences) from time and space-time series. Exploration and analysis of the extracted events employs a graphic exploratory environment based on a graphic primitive called an event band and its composition into event band stacks and panels that support investigation of various space-time patterns.The project contributes a new information integration approach based on the concept of an event that can be extended to many domains including socio-economic, financial, legislative, surveillance and health related information. The project will contribute new data mining strategies for event detection in time and space-time series and a set of flexible exploratory tools for examination and development of hypotheses on space-time event patterns and interactions

Decentralized Detection of Topological Events in Evolving Spatial Regions

Qualitative information about topological events, like the merging or splitting of spatial regions, has many important applications in environmental monitoring. Examples of such applications include detecting the emergence of "hot spots" in sea temperature around a coral reef; or the break up and dispersion of an environmental pollution spill. This paper develops and tests an efficient, decentralized spatial algorithm capable of detecting high-level topological events occurring to spatial regions monitored by a wireless sensor network. The algorithm, called INQUIRE, is decentralized because at no point does any single system element possess global knowledge of the entire system state. Instead, INQUIRE relies purely on a sensor node's local knowledge of its own state and the state of its immediate network neighbors. Experimental evaluation of the INQUIRE algorithm demonstrates that our decentralized approach can substantially improve scalability of communication when compared with efficient centralized alternatives

Dynamic Composite Data Physicalization Using Wheeled Micro-Robots

This paper introduces dynamic composite physicalizations, a new class of physical visualizations that use collections of self-propelled objects to represent data. Dynamic composite physicalizations can be used both to give physical form to well-known interactive visualization techniques, and to explore new visualizations and interaction paradigms. We first propose a design space characterizing composite physicalizations based on previous work in the fields of Information Visualization and Human Computer Interaction. We illustrate dynamic composite physicalizations in two scenarios demonstrating potential benefits for collaboration and decision making, as well as new opportunities for physical interaction. We then describe our implementation using wheeled micro-robots capable of locating themselves and sensing user input, before discussing limitations and opportunities for future work

Modeling and manipulating spacetime objects in a true 4D model

The concept of spacetime has long been used in physics to refer to models that integrate 3D space and time as a single 4D continuum. We argue in this paper that it is also advantageous to use this concept in a practical geographic context by realizing a true 4D model, where time is modeled and implemented as a dimension in the same manner as the three spatial dimensions. Within this paper we focus on 4D vector objects, which can be implemented using dimension-independent data structures such as generalized maps. A 4D vector model allows us to create and manipulate models with actual 4D objects and the topological relationships connecting them, all of which have a geometric interpretation and can be constructed, modified, and queried. In this paper we discuss where such a 4D model fits with respect to other spatiotemporal modeling approaches, and we show concretely how higher-dimensional modeling can be used to represent such 4D objects and topological relationships. In addition, we explain how the 4D objects in such a system can be created and manipulated using a small set of implementable operations, which use simple 3D space and 1D time inputs for intuitiveness and which modify the underlying 4D model indirectly