Distributed Decomposed Data Analytics in Fog Enabled IoT Deployments

Abstract

The edge of the network plays a vital role in an Internet of Things (IoT) system, serving as an optimal site to perform an 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 the 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 the 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 the amount of data transferred to the cloud using the proposed fog-based distributed data analytics approach compared with the conventional cloud-based approach. Furthermore, by adopting the proposed distributed approach, we observed a 98% drop in the time taken to arrive at the final result compared with the cloud-centric approach. We also present the results on the quality of analytics solution obtained in both approaches, and they suggest that the fog-based distributed analytics approach can serve as equally as the traditional cloud-centric approach.