Process Automation in an IoT–Fog–Cloud Ecosystem: A Survey and Taxonomy

TLDR

The rapid growth of IoT sensors creates high‑latency challenges for cloud‑only processing, motivating the need for automated mechanisms in the IoT–Fog–Cloud ecosystem to manage data transfer, heterogeneity, and fog resiliency. This survey designs a taxonomy of automatic components for fog resiliency and outlines how to automate tasks, methods, and processes that remain manual in the ecosystem. The authors review, study, and analyze existing automatic functions, proposing a taxonomy and suggesting automation strategies for the IoT–Fog–Cloud architecture. They demonstrate the automatic functions through research aligned with each identified challenge.

Abstract

The number of IoT sensors and physical objects accommodated on the Internet is increasing day by day, and traditional Cloud Computing would not be able to host IoT data because of its high latency. Being challenged of processing all IoT big data on Cloud facilities, there is not enough study on automating components to deal with the big data and real-time tasks in the IoT–Fog–Cloud ecosystem. For instance, designing automatic data transfer from the fog layer to cloud layer, which contains enormous distributed devices is challenging. Considering fog as the supporting processing layer, dealing with decentralized devices in the IoT and fog layer leads us to think of other automatic mechanisms to manage the existing heterogeneity. The big data and heterogeneity challenges also motivated us to design other automatic components for Fog resiliency, which we address as the third challenge in the ecosystem. Fog resiliency makes the processing of IoT tasks independent to the Cloud layer. This survey aims to review, study, and analyze the automatic functions as a taxonomy to help researchers, who are implementing methods and algorithms for different IoT applications. We demonstrated the automatic functions through our research in accordance to each challenge. The study also discusses and suggests automating the tasks, methods, and processes of the ecosystem that still process the data manually.