FogGIS: Fog Computing for geospatial big data analytics

Barik, Rabindra K.; Dubey, Harishchandra; Samaddar, Arindam; Gupta, R.; Ray, Pravat Kumar

doi:10.1109/upcon.2016.7894725

Cited by 88 publications

(24 citation statements)

References 20 publications

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“…Fog framework added privacy benefit where we process the data locally at Fog devices and send only the analysis results to cloud layer. Figure 1 (b) shows the Conceptual diagram of the fog framework for geospatial health data storage and analysis [4,10,11]. From the above conceptual diagram of cloud and fog framework, it observed that the geospatial data as a key components for data analysis in cloud layer [9,8].…”

Section: Related Work 21 Fog and Cloud Computingmentioning

confidence: 98%

“…Disease phenomena are strongly associated with geospatial and related temporal factors. For tackling these situation, Cloud framework has provided dynamic and real-time way to represent disease information through the maps on common browsers [2] [4]. So for sharing and analysis of health data in secure way, we have to concentrate with various security issues which has discussed in next section.…”

Section: Geohealth Big Datamentioning

confidence: 99%

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SOA-FOG: Secure service-oriented edge computing architecture for smart health big data analytics

Barik

Dubey

Mankodiya

2017

2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

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The smart health paradigms employ Internet-connected wearables for telemonitoring, diagnosis for providing inexpensive healthcare solutions. Fog computing reduces latency and increases throughput by processing data near the body sensor network. In this paper, we proposed a secure serviceorientated edge computing architecture that is validated on recently released public dataset. Results and discussions support the applicability of proposed architecture for smart health applications. We proposed SoA-Fog i.e. a three-tier secure framework for efficient management of health data using fog devices. It discuss the security aspects in client layer, fog layer and the cloud layer. We design the prototype by using win-win spiral model with use case and sequence diagram. Overlay analysis was performed using proposed framework on malaria vector borne disease positive maps of Maharastra state in India from 2011 to 2014. The mobile clients were taken as test case. We performed comparative analysis between proposed secure fog framework and state-of-the art cloud-based framework.

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Section: Related Work 21 Fog and Cloud Computingmentioning

confidence: 98%

Section: Geohealth Big Datamentioning

confidence: 99%

SOA-FOG: Secure service-oriented edge computing architecture for smart health big data analytics

Barik

Dubey

Mankodiya

2017

2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

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“…[49,9]. devices such as smart phones and mobile GIS interfaces [9,67,47,41,12,54]. We consider this technology for integrating it with health GIS for better management of geospatial health data.…”

Section: Fog Computingmentioning

confidence: 99%

GeoFog4Health: a fog-based SDI framework for geospatial health big data analysis

Barik

Dubey

Mankodiya

et al. 2018

J Ambient Intell Human Comput

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Spatial Data Infrastructure (SDI) is an important framework for sharing geospatial big data using the web. Integration of SDI with cloud computing lead to emergence of Cloud-SDI as a tool for transmission, processing and analysis of geospatial data. Fog computing is a paradigm where embedded devices are employed to increase the throughput and reduce latency at the edge of network. In this study, we developed and evaluated a Fog-based SDI framework named GeoFog4Health for mining analytics from geo-health big data. We built a prototype using both Intel Edison and Raspberry Pi for performing a comparative study. We performed a case study on Malaria vector borne disease positive maps of Maharastra state in India. The proposed framework had provision of lossless data compression. Also, overlay analysis of geospatial data could be performed. In addition, we discussed energy saving, cost analysis and scalability of proposed framework for efficient data processing. We compared the performance of proposed framework with state of the art Cloud-SDI in terms of analysis time. Results and discussions showed the efficacy of proposed system for enhanced analysis of geo-health big data generated from a variety of sensing frameworks.

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“…It is not a new thing, since it has been used from decades to communicate information in a better and easier way [25]. A vast amount of data is generated every day, only 20% of this is structed rest is all unstructured data, visualizing data helps in exploring and analyzing data in easier way as it helps in understanding data, finding correlations, finding general trends in data etc.…”

Section: Big Data Visualizationmentioning

confidence: 99%

Fair: A Hadoop-based Hybrid Model for Faculty Information Retrieval System

Gupta¹,

Lenka²,

Barik³

et al. 2017

Preprint

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Abstract:In era of ever-expanding data and knowledge, we lack a centralized system that maps all the faculties to their research works. This problem has not been addressed in the past and it becomes challenging for students to connect with the right faculty of their domain. Since we have so many colleges and faculties this lies in the category of big data problem. In this paper, we present a model which works on the distributed computing environment to tackle big data. The proposed model uses apache spark as an execution engine and hive as database. The results are visualized with the help of Tableau that is connected to Apache Hive to achieve distributed computing.

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FogGIS: Fog Computing for geospatial big data analytics

Abstract: Abstract-Cloud Geographic Information Systems (GIS) has

Cited by 88 publications

References 20 publications

SOA-FOG: Secure service-oriented edge computing architecture for smart health big data analytics

SOA-FOG: Secure service-oriented edge computing architecture for smart health big data analytics

GeoFog4Health: a fog-based SDI framework for geospatial health big data analysis

Fair: A Hadoop-based Hybrid Model for Faculty Information Retrieval System

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