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.
['Kriti Bhargava', 'Stepan Ivanov']
Collaborative Edge Mining for predicting heat stress in dairy cattle