Collaborative Edge Mining for predicting heat stress in dairy cattle

Bhargava, Kriti and Ivanov, Stepan (2016) Collaborative Edge Mining for predicting heat stress in dairy cattle. In: 2016 Wireless Days, WD 2016 :. IFIP Wireless Days . IEEE Computer Society, FRA. ISBN 9781509024940

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Edge Mining (EM), a novel Fog Computing technique, has been proposed to perform data analysis on sensor devices at the edge of Internet of Things (IoT). The approach, however, is limited to analysis conducted by each sensor node in isolation. In this paper, we propose Collaborative Edge Mining (CEM), an extension of the EM technique, wherein multiple sensor devices participate together in on-site data analysis and prediction. Our model detects contextually relevant events by integrating and analysing data arising from different sources and, thereby, lays the foundation of a sensor-based implementation of Apache Storm like framework. We have evaluated our approach with respect to the Linear Spanish Inquisition Protocol for a precision farming application. We illustrate CEM for the estimation of Temperature Humidity Index, an important metric to predict Heat Stress in dairy cattle, and compare its performance to EM. CEM performs well in most cases, especially, latency-sensitive scenarios.

Item Type: Book Section
Additional Information: Funding Information: This work has received support from the Science Foundation Ireland (SFI) and the Agriculture and Food Development Authority, Ireland (TEAGASC) as part of the SFI TEAGASC Future Agri-Food Partnership, in a project (13/IA/1977) titled "Using precision technologies, technology platforms and computational biology to increase the economic and environmental sustainability of pasture based production systems". Publisher Copyright: © 2016 IEEE.
Uncontrolled Keywords: /dk/atira/pure/subjectarea/asjc/1700/1705
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Depositing User: Admin SSL
Date Deposited: 19 Oct 2022 23:17
Last Modified: 13 Jul 2023 05:15

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