# FogGIS: Fog Computing for Geospatial Big Data Analytics

## TL;DR

This paper introduces Fog GIS, a fog computing framework for geospatial data analysis that reduces latency and transmission costs by processing data at the edge using a prototype built on Intel Edison.

## Contribution

It presents a novel Fog GIS framework leveraging fog computing for efficient geospatial data analytics, including prototype implementation and preliminary validation.

## Key findings

- Fog computing enhances geospatial data analysis performance.
- Open source compression techniques effectively reduce data transmission.
- Preliminary analysis confirms FogGIS's potential for geospatial data processing.

## Abstract

Cloud Geographic Information Systems (GIS) has emerged as a tool for analysis, processing and transmission of geospatial data. The Fog computing is a paradigm where Fog devices help to increase throughput and reduce latency at the edge of the client. This paper developed a Fog-based framework named Fog GIS for mining analytics from geospatial data. We built a prototype using Intel Edison, an embedded microprocessor. We validated the FogGIS by doing preliminary analysis. including compression, and overlay analysis. Results showed that Fog computing hold a great promise for analysis of geospatial data. We used several open source compression techniques for reducing the transmission to the cloud.

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Source: https://tomesphere.com/paper/1701.02601