Collaborative Edge Mining for predicting heat stress in dairy cattle

Kriti Bhargava, Stepan Ivanov

    Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

    5 Citations (Scopus)


    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.
    Original languageEnglish
    Title of host publicationIFIP Wireless Days
    PublisherIEEE Computer Society
    Number of pages6
    ISBN (Electronic)9781509024940
    Publication statusPublished - 27 Apr 2016
    EventWireless Days 2016 - Toulouse, France
    Duration: 23 Mar 201625 Mar 2016

    Publication series

    NameIFIP Wireless Days


    ConferenceWireless Days 2016
    Internet address


    • apache storm
    • edge mining
    • heat stress
    • precision farming
    • wireless sensor networks


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