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.

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.