Rabindra Kumar Barik, R. Priyadarshini, R. K. Lenka, Harishchandra Dubey, K. Mankodiya
tlooto Summary
The authors developed a prototype of Geospatial Big Data, a proposed Open Source Source Data Analysis tool, and implemented some methods for employing fog computing.
Abstract
Geospatialdataanalysisusingcloudcomputingplatformisoneofthepromisingareasforanalysing, retrieving,andprocessingvolumetricdata.Fogcomputingparadigmassistscloudplatformwherefog devicestrytoincreasethethroughputandreducelatencyattheedgeoftheclient.Inthisresearchpaper, theauthorsdiscusstwocasestudiesongeospatialdataanalysisusingFog-assistedcloudcomputing namely,(1)GangaRiverBasinManagementSystem;and(2)TourismInformationManagementof India.BothcasestudiesevaluateproposedGeoFogarchitectureforefficientanalysisandmanagement ofgeospatial bigdata employing fog computing.The authorsdevelopedaprototypeofGeoFog architectureusingIntelEdisonandRaspberryPidevices.Theauthors implementedsomeof the opensourcecompressionmethodsforreducingthedatatransmissionoverloadinthecloud.Proposed architectureperformsdatacompressionandoverlayanalysisofdata.Theauthorsfurtherdiscussed theimprovementinscalabilityandtimeanalysisusingproposedGeoFogarchitectureandGeospark tool.Discussedresultsshowthemeritoffogcomputingthatholdsanenormouspromiseforenhanced analysisofgeospatialbigdatainriverGangabasinandtourisminformationmanagementscenario. KeywoRDS Cloud Computing, Geospatial Big Data, Geospatial Data, K-Means, Open Source GIS, Overlay Analysis, River, Tourism, Visualization
Citation format
BARIK, Rabindra Kumar, et al. Fog computing architecture for scalable processing of geospatial big data. International Journal of Applied Geospatial Research, 2020, 11: 1–20.