A Fog-Based Multi-Purpose Internet of Things Analytics Platform
Theo Zschörnig, Jonah Windolph, Robert Wehlitz, Yann Dumont, Bogdan Franczyk · SN Computer Science · 2022
Abstract Data analytics is an important component for the benefit and growth of the Internet of Things (IoT). The utilization of data generated by a variety of heterogeneous smart devices offers the possibility of gaining meaningful insights into various aspects of the daily lives of end consumers, the environment and weather, but also into value-added processes of business and industry. The potential benefits derived from analyzing IoT data can be further enhanced by advancing developments in streaming and machine learning technologies. A critical factor in the application of these technologies are the underlying analytics architectures. These must overcome a variety of different challenges that are influenced by technical, but also legal or personal constraints and differ in importance and impact depending on the IoT application domain in which such an architecture is to be deployed. Solutions presented by previous research address only a handful of these challenges. An important capability to address the variety of challenges that arise from this situation is the ability to support the hybrid deployment of analytics pipelines at different network layers. Consequently, in this work, we propose an architectural solution that enables hybrid analytics pipeline deployments, addresses the challenges described in previous scientific literature and can be deployed in various IoT application domains. Finally, we experimentally evaluate the proposed solution.