The goal of this project is to research and develop a prototype, technology-rich infrastructure to support economical continuous monitoring of cattle state of health via remote and wearable devices that interact with a veterinary health information network. Wireless stations, distributed databases, and physiological assessment algorithms will play a key role in this environment. These systems are improving the ability of the livestock industry to react to and predict disease onset and its epidemiological spread, whether from natural or terrorist events. Trend analysis and health prediction lessons learned from this effort have application to human state-of-health prediction and disease spread in human populations.

The researchers are placing Bluetooth-compliant monitoring stations near cattle congregation points, such as feed bunks and watering troughs. These stations upload data from environmental sensors, Bluetooth-enabled devices with global positioning capability worn by the animals, and wearable/remote biomedical sensors. Local algorithms perform rapid data analysis prior to uploading ranch summary data to regional databases, where the data is correlated with information provided by nearby producers. Significant findings are then immediately broadcast to regional veterinarians, producers, and authorities. In order to accomplish this, the research addresses a number of important information technology issues, including scheduling algorithms that adaptively determine where data analysis should occur and which areas require more in-depth analysis; prioritization algorithms for multiple wireless data streams with near-real-time constraints; and area-appropriate data mining techniques to find problem indicators.

The interdisciplinary project is addressing a critical need within the agricultural and national communities: applied research that allows the animal sciences industry to react to and predict disease onset in cattle and its epidemiological spread. Through the development of a distributed software infrastructure, a comprehensive physiological monitoring toolset, and new processing algorithms (e.g., for data storage, fusion, and interpretation), we are helping to improve the financial stability of the livestock industry while simultaneously raising our level of preparedness for epidemiological disasters, whether from natural or terrorist events.

National Science Foundation (NSF)
Division of Computer and Network Systems (CNS)
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D. Helen Gill
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Kansas State University
United States
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