Rabindra K. Barik

FogGIS: Fog Computing for geospatial big data analytics

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 Computing based framework named FogGIS for mining analytics from geospatial data. It has been built a prototype using Intel Edison, an embedded microprocessor. FogGIS has validated by doing preliminary analysis including compression and overlay analysis. Results showed that Fog Computing hold a great promise for analysis of geospatial data. Several open source compression techniques have been used for reducing the transmission to the cloud.

Comparative analysis of SpatialHadoop and GeoSpark for geospatial big data analytics

In this digitalised world where every information is stored, the data a are growing exponentially. It is estimated that data are doubles itself every two years. Geospatial data are one of the prime contributors to the big data scenario. There are numerous tools of the big data analytics. But not all the big data analytics tools are capabilities to handle geospatial big data. In the present paper, it has been discussed about the recent two popular open source geospatial big data analytical tools i.e. SpatialHadoop and GeoSpark which can be used for analysis and process the geospatial big data in efficient manner. It has compared the architectural view of SpatialHadoop and GeoSpark. Through the architectural comparison, it has also summarised the merits and demerits of these tools according the execution times and volume of the data which has been used.

Service Oriented Architecture Based SDI Model for Education Sector in India

Technological and overall economic growth of any country warrants a rapid development of the education sector, which is responsible for producing quality human resources for serving the nation and the human society as a whole. Hence, there is a need to make coordinated efforts to disseminate information about the quality of academic details in a simple but detailed manner by integrating modern technologies. Spatial technologies such as GIS, remote sensing and GPS hold potential to remove some of the bottlenecks that hinder the efficiency of this sector. Further, for Right To Education (RTE) easy to use/ perceive spatial information is required for the decision makers. Hence, there is a need to establish a well organised Spatial Data Infrastructure (SDI) portal where each stakeholder can access, use and exchange spatial information for education sector. The present work reports the development of an efficient interoperable Service Oriented Architecture (SOA) based SDI Model for education sector. The developed SDI Model is distributed, modular and allows the publishing of web service descriptions as well as to submit requests to discover the web service of user’s interests. The Model supports integration of applications and browsers independent Web Map Service (WMS), Web Features Service (WFS), Web Coverage Service (WCS) and Web Catalogue Service (CS-W) for sharing and exchange of geospatial data.

Dynamic Dedicated Server Allocation for Service Oriented Multi-Agent Data Intensive Architecture in Biomedical and Geospatial Cloud

Cloud computing has recently received considerable attention, as a promising approach for delivering Information and Communication Technologies (ICT) services as a utility. In the process of providing these services it is necessary to improve the utilization of data centre resources which are operating in most dynamic workload environments. Datacenters are integral parts of cloud computing. In the datacenter generally hundreds and thousands of virtual servers run at any instance of time, hosting many tasks and at the same time the cloud system keeps receiving the batches of task requests. It provides services and computing through the networks. Service Oriented Architecture (SOA) and agent frameworks renders tools for developing distributed and multi agent systems which can be used for the administration of cloud computing environments which supports the above characteristics. This paper presents a SOQM (Service Oriented QoS Assured and Multi Agent Cloud Computing) architecture which supports QoS assured cloud service provision and request. Biomedical and geospatial data on cloud can be analyzed through SOQM and has allowed the efficient management of the allocation of resources to the different system agents. It has proposed a finite heterogeneous multiple vm model which are dynamically allocated depending on the request from biomedical and geospatial stakeholders. Dynamic Dedicated Server Allocation for Service Oriented Multi-Agent Data Intensive Architecture in Biomedical and Geospatial Cloud

MistGIS: Optimizing Geospatial Data Analysis Using Mist Computing

Geospatial data analysis with the help of cloud and fog computing is one of the emerging areas for processing, storing, and analysis of geospatial data. Mist computing is also one of the paradigms where fog devices help to reduce the latency period and increase throughput for assisting at the near of edge device of the client. It discusses the emergence of mist computing for mining analytics in geospatial big data from geospatial application. This paper developed a mist computing-based framework for mining analytics from geospatial big data. We developed MistGIS framework for Ganga River Management System using mist computing. It built a prototype using Raspberry Pi, an embedded microprocessor. The developed MistGIS framework has validated by doing preliminary analysis including K-means clustering and overlay analysis. The results showed that mist computing can assist the fog and cloud computing hold an immense promise for analysis of big data in geospatial application particularly in the management of Ganga River Basin.

Fog2fog: augmenting scalability in fog computing for health GIS systems

This study considers the situation where computational loads are transferred to edge devices and single edge device is not enough. The co-operative sensing, analysis and transmission between several edge nodes helps in enhancing scalability in Fog computing frameworks. The present study uses the positive case of malaria vector borne disease affected information from 2001-2014 of Maharashtra state, India for performance analysis.

CloudGanga: Cloud Computing Based SDI Model for Ganga River Basin Management in India

The present research paper proposes and develops a Cloud computing based Spatial Data Infrastructure (SDI) Model named as CloudGanga for sharing, analysis and processing of geospatial data particularly in River Ganga Basin management in India. The main purpose of the CloudGanga is to integrate all the geospatial information such as dam location, well location, irrigation project, hydro power project, canal network and central Water Commission gauge stations locations related to River Ganga. CloudGanga can help the decision maker/ planner or common users to get enough information for their further research and studies. The open source software (Quantum GIS) has been used for the development of geospatial database. QGIS Plugin has been linked with Quantum GIS for invoking cloud computing environment. It has also discussed about the various overlay analysis in CloudGanga environment. In the present research, machine learning approaches are also used in a R tool for well locations which are associated with the basin of River Ganga.

A Hybrid GSA-K-Mean Classifier Algorithm to Predict Diabetes Mellitus

Lotsofresearchhasbeencarriedoutgloballytodesignamachineclassifierwhichcouldpredictit fromsomephysicalandbio-medicalparameters.Inthisworkahybridmachinelearningclassifierhas beenproposedtodesignanartificialpredictortocorrectlyclassifydiabeticandnon-diabeticpeople. TheclassifierisanamalgamationofthewidelyusedK-meansalgorithmandGravitationalsearch algorithm(GSA).GSAhasbeenusedasanoptimizationtoolwhichwillcomputethebestcentroids fromthetwoclassesoftrainingdata;thepositiveclass(whoarediabetic)andnegativeclass(who arenon-diabetic).InK-meansalgorithminsteadofusingrandomsamplesasinitialclusterhead,the optimizedcentroidsfromGSAareusedastheclustercenters.Theinherentproblemassociatedwith k-meansalgorithmistheinitialplacementofclustercenters,whichmaycauseconvergencedelay therebydegradingtheoverallperformance.Thisproblemistriedtoovercomebyusingacombined GSAandK-means. KeywoRDS Classification, Diabetes Mellitus, Gravitational Search Algorithm, K-Means Algorithm, Optimization

Development and implementation of SOA based SDI model for tourism information infrastructure management web services

Tourism is one of the prime areas for the economic growth rate of any country, particularly developed countries. Hence, there is an essential need to make efforts to disseminate information about the platform of tourism information details in a simplest way but in detailed manner by integrating modern technologies such as spatial technologies and web technology. Further, for the globalization in tourism sector, it is required easy to use the spatial information for the attraction of Foreign Tourist Arrivals (FTAs) and Foreign Exchange Earnings (FEEs) across the world. Therefore, it is the need to establish a Spatial Data Infrastructure (SDI) Model where each stakeholder can access, use and exchange spatial information in tourism sector. In the present work, it represents the development and implementation of an efficient and interoperable Service Oriented Architecture (SOA) based SDI Model for geospatial web services in tourism sector. The developed SDI Model allows the publishing of web service descriptions as well as to submit requests to discover the web service of users interests. The Model supports the integration of various geospatial web services i.e. Web Features Service (WFS), Web Catalogue Service (CS-W), Web Map Service (WMS) and Web Coverage Service (WCS) in distributed platform. The open source GIS (OSGIS) software has been used for development and implementation of SOA based SDI Model. For creation and storing of spatial and non-spatial tourism database, it has been used Quantum GIS, Map Window GIS and PostGIS. It includes PHP: Hypertext Preprocessor, GeoNetwork, GeoServer and Apache Tomcat for dynamic server side scripting and imparting geospatial web services for sharing and exchange of geospatial data. The temple city, Bhubaneswar, India has been taken as the test case for Tourism Information Infrastructure Management in India.

Fog Assisted Cloud Computing in Era of Big Data and Internet-of-Things: Systems, Architectures, and Applications

This book chapter discusses the concept of edge-assisted cloud computing and its relation to the emerging domain of “Fog-of-things (FoT)”. Such systems employ low-power embedded computers to provide local computation close to clients or cloud. The discussed architectures cover applications in medical, healthcare, wellness and fitness monitoring, geo-information processing, mineral resource management, etc. Cloud computing can get assistance by transferring some of the processing and decision making to the edge either close to client layer or cloud backend. Fog of Things refers to an amalgamation of multiple fog nodes that could communicate with each other with the Internet of Things. The clouds act as the final destination for heavy-weight processing, long-term storage and analysis. We propose application-specific architectures GeoFog and Fog2Fog that are flexible and user-orientated. The fog devices act as intermediate intelligent nodes in such systems where these could decide if further processing is required or not. The preliminary data analysis, signal filtering, data cleaning, feature extraction could be implemented on edge computer leading to a reduction of computational load in the cloud. In several practical cases, such as tele healthcare of patients with Parkinson’s disease, edge computing may decide not to proceed for data transmission to cloud (Barik et al., in 5th IEEE Global Conference on Signal and Information Processing 2017, IEEE, 2017) [4]. Towards the end of this research paper, we cover the idea of translating machine learning such as clustering, decoding deep neural network models etc. on fog devices that could lead to scalable inferences. Fog2Fog communication is discussed with respect to analytical models for power savings. The book chapter concludes by interesting case studies on real world situations and practical data. Future pointers to research directions, challenges and strategies to manage these are discussed as well. We summarize case studies employing proposed architectures in various application areas. The use of edge devices for processing offloads the cloud leading to an enhanced efficiency and performance.