An interconnected IoT-inspired network architecture for data visualization in remote sensing domain

Abstract

Geospatial Data Analytics (GDA) is a futuristic platform for analyzing and processing volumetric data in remote sensing and GIS applications. GDA utilizes the Internet of Spatial Things (IoST), mist, fog, and cloud computing architecture as a backend tool for analyzing and processing big geospatial data. This paper introduces utilizing these interconnected network architectures of cloud, fog, and mist to process a large volume of geospatial data. Also, the paper presents a flexible, interconnected distributed network system, i.e., IoST-mist-fog-cloud GIS architecture, to analyze and manage geospatial data. The proposed system helps cloud platforms when MIST devices are trying to cut down on latency and boost throughput at the edge of the IoST tier. It also performs the geospatial crime data visualization of the total number of stolen vehicles from 2001 to 2011 from all the states of India as a case study by using the proposed model. It explains the mathematical and analytical queueing model of the proposed system. In addition, it performs a performance evaluation and experimental findings on the proposed architecture and uses graphs to represent the various arithmetic outcomes. The experimentation result proves the proposed interconnected network architecture's efficacy in terms of reliability and efficiency.

Keywords

• Cloud GIS architecture
• Internet of spatial things
• Geospatial data
• Fog computing
• Mist computing
• Queueing model