# IoT Stream Processing and Analytics in The Fog

## TL;DR

This paper explores Fog computing for stream processing and analytics, analyzing models, architecture, and design challenges to enhance low-latency, resilient services in Fog environments.

## Contribution

It provides a comprehensive analysis of Fog data streaming models, architecture, and design space, addressing unique challenges and leveraging existing techniques.

## Key findings

- Identifies key properties of Fog stream processing applications.
- Analyzes the design space considering system, data, human, and optimization dimensions.
- Highlights challenges and opportunities in adapting Cloud streaming techniques to Fog computing.

## Abstract

The emerging Fog paradigm has been attracting increasing interests from both academia and industry, due to the low-latency, resilient, and cost-effective services it can provide. Many Fog applications such as video mining and event monitoring, rely on data stream processing and analytics, which are very popular in the Cloud, but have not been comprehensively investigated in the context of Fog architecture. In this article, we present the general models and architecture of Fog data streaming, by analyzing the common properties of several typical applications. We also analyze the design space of Fog streaming with the consideration of four essential dimensions (system, data, human, and optimization), where both new design challenges and the issues arise from leveraging existing techniques are investigated, such as Cloud stream processing, computer networks, and mobile computing.