Distributed Decomposed Data Analytics in Fog Enabled IoT Deployments

The edge of the network plays a vital role in an IoT system, serving as an optimal site to perform operation on data before transmitting it over the network. We present the fog specific decomposition of multivariate linear regression as the predictive analytic model in our work using Statistical Query Model and Summation Form. The decomposition method used is not the contribution, but applying the decomposition method to the analytics model to run in a distributed manner in fog enabled IoT deployments is the contribution. What is novel is the decomposition made on a fog based distributed setting. To test the performance, our proposed approach has been applied to a real-world dataset and evaluated using a fog computing testbed. The proposed method avoids sending raw data to the cloud, and offers balanced computation in the infrastructure. The results show an 80% reduction in amount of data transferred to the cloud using the proposed fog based distributed data analytics approach as compared to the conventional cloud based approach. Furthermore, by adopting the proposed distributed approach, we observed a 98% drop in the time taken to arrive to the final result as compared to the cloud centric approach. We also present the results on quality of analytics solution obtained in both approaches, and they suggest that fog based distributed analytics approach can serve as equally as the traditional cloud centric approach

Machine learning based fog computing assisted data-driven approach for early lameness detection in dairy cattle

Timely lameness detection is one of the major and costliest health problems in dairy cattle that farmers and practitioners haven't yet solved adequately. The primary reason behind this is the high initial setup costs, complex equipment and lack of multi-vendor interoperability in currently available solutions. On the other hand, human observation based solutions relying on visual inspections are prone to late detection with possible human error, and are not scalable. This poses a concern with increasing herd sizes, as prolonged or undetected lameness severely compromises cows' health and welfare, and ultimately affects the milk productivity of the farm. To tackle this, we have developed an end-to-end IoT application that leverages advanced machine learning and data analytics techniques to monitor the cattle in real-time and identify lame cattle at an early stage. The proposed approach has been validated on a real world smart dairy farm setup consisting of a dairy herd of 150 cows in Waterford, Ireland. Using long-range pedometers specifically designed for use in dairy cattle, we monitor the activity of each cow in the herd. The accelerometric data from these sensors is aggregated at the fog node to form a time series of behavioral activities, which are further analyzed in the cloud. Our hybrid clustering and classification model identifies each cow as either Active, Normal or Dormant, and further, Lame or Non-Lame. The detected lameness anomalies are further sent to farmer's mobile device by way of push notifications. The results indicate that we can detect lameness 3 days before it can be visually captured by the farmer with an overall accuracy of 87%. This means that the animal can either be isolated or treated immediately to avoid any further effects of lameness. Moreover, with fog based computational assistance in the setup, we see an 84% reduction in amount of data transferred to the cloud as compared to the conventional cloud based approach

SmartHerd Management: A Microservices Based Fog Computing Assisted IoT Platform towards Data Driven Smart Dairy Farming

Internet of things (IoT), fog computing, cloud computing and data driven techniques together offer a great opportunity for verticals such as dairy industry to increase productivity by getting actionable insights to improve farming practices, thereby increasing efficiency and yield. In this paper, we present SmartHerd, a fog computing assisted end-to-end IoT platform for animal behaviour analysis and health monitoring in a dairy farming scenario. The platform follows a microservices oriented design to assist the distributed computing paradigm, and addresses the major issue of constrained Internet connectivity in remote farm locations. We present the implementation of the designed software system in a 6 month mature real-world deployment, wherein the data from wearables on cows is sent to a fog based platform for data classification and analysis, which includes decision making capabilities and provides actionable insights to farmer towards the welfare of animals. With fog based computational assistance in the SmartHerd setup, we see an 84\% reduction in amount of data transferred to the cloud as compared to the conventional cloud based approach