Agent-based Coordination for the Sensor Web

SAC ’10: Proceedings of the 2010 ACM Symposium on Applied Computing, New York, NY, USA

Conor Muldoon, Richard Tynan, Gregory M. P. O. Hare, Michael J. O. Grady

“This paper addresses the problem of coordination within the Sensor Web, where the Sensor Web is defined as an amorphous network of spatially distributed nodes that sense various phenomena in the environment, that are battery powered, and that communicate and coordinate wirelessly. The approach described advocates the use of a multi-agent system, and specifically the use of multi-agent distributed constraint optimisation algorithms. Developing software for low powered sensing devices introduces several problems to be addressed; the most obvious being the limited computational resources available. In this paper we discuss an implementation of ADOPT, a pre-existing algorithm for distributed constraint optimisation, and describe how it has been integrated with a reflective agent platform developed for resource constrained devices, namely Agent Factory Micro Edition (AFME). The usefulness of this work is illustrated through the canonical multi-agent coordination problem, namely graph colouring.”

Using Camera State Transforms for Commuter Network Visualization

GeoViz: Linking Geovisualization with Spatial Analysis and Modeling, 10-11 March 2011, Hamburg, Germany

Yves Chiricota, Michael J. McGuffin, and Martin Simard

“We present a novel approach for visualizing commuter networks, i.e., directed graphs whose nodes (cities and towns) each have a geographic location, and whose edges each have a direction and an associated number of commuters moving between two nodes. Our approach involves using camera state transforms that map the current camera state (position and orientation) to various rendering parameters to achieve a hybrid 2D-3D visualization. As the camera’s angle and distance change, the shapes and transparency levels of nodes and edges morph in response, allowing for a smooth transition between a 2D view (from above) to a 3D view (from the side) that reveals information in different ways.”

Spatio-temporal Analysis of a Plant Disease in a Non-uniform Crop: A Monte Carlo Approach

Journal of Applied Statistics, Volume 38, Issue 1, First published 2011, Pages 175 – 182

Bin Li; R. S. Sanderlin; Rebecca A. Melanson; Qingzhao Yu

“Identification of the type of disease pattern and spread in a field is critical in epidemiological investigations of plant diseases. For example, an aggregation pattern of infected plants suggests that, at the time of observation, the pathogen is spreading from a proximal source. Conversely, a random pattern suggests a lack of spread from a proximal source. Most of the existing methods of spatial pattern analysis work with only one variety of plant at each location and with uniform genetic disease susceptibility across the field. Pecan orchards, used in this study, and other orchard crops are usually composed of different varieties with different levels of susceptibility to disease. A new measure is suggested to characterize the spatio-temporal transmission patterns of disease; a Monte Carlo test procedure is proposed to test whether the transmission of disease is random or aggregated. In addition, we propose a mixed-transmission model, which allows us to quantify the degree of aggregation effect.”