Fog computing architecture for scalable processing of geospatial big data
Date of Original Version
Geospatial data analysis using cloud computing platform is one of the promising areas for analysing, retrieving, and processing volumetric data. Fog computing paradigm assists cloud platform where fog devices try to increase the throughput and reduce latency at the edge of the client. In this research paper, the authors discuss two case studies on geospatial data analysis using Fog-assisted cloud computing namely, (1)Ganga River Basin Management System; and (2)Tourism Information Management of India. Both case studies evaluate proposed GeoFog architecture for efficient analysis and management of geospatial big data employing fog computing. The authors developed a prototype of GeoFog architecture using Intel Edison and Raspberry Pi devices. The authors implemented some of the open source compression methods for reducing the data transmission overload in the cloud. Proposed architecture performs data compression and overlay analysis of data. The authors further discussed the improvement in scalability and time analysis using proposed GeoFog architecture and Geospark tool. Discussed results show the merit of fog computing that holds an enormous promise for enhanced analysis of geospatial big data in river Ganga basin and tourism information management scenario.
International Journal of Applied Geospatial Research
Barik, Rabindra K., Rojalina Priyadarshini, Rakesh K. Lenka, Harishchandra Dubey, and Kunal Mankodiya. "Fog computing architecture for scalable processing of geospatial big data." International Journal of Applied Geospatial Research 11, 1 (2020): 1-20. doi:10.4018/IJAGR.2020010101.