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-based framework named Fog GIS for mining analytics from geospatial data. We built a prototype using Intel Edison, an embedded microprocessor. We validated the FogGIS by doing preliminary analysis. including compression, and overlay analysis. Results showed that Fog computing hold a great promise for analysis of geospatial data. We used several open source compression techniques for reducing the transmission to the cloud.Comment: 6 pages, 4 figures, 1 table, 3rd IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics (09-11 December, 2016) Indian Institute of Technology (Banaras Hindu University) Varanasi, Indi

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

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 service-orientated 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

TCloud: Cloud SDI model for tourism information infrastructure management

This chapter proposes and develops a cloud-computing-based SDI model named as TCloud for sharing, analysis, and processing of spatial data particularly in the Temple City of India, Bhubaneswar. The main purpose of TCloud is to integrate all the spatial information such as tourism sites which include various temples, mosques, churches, monuments, lakes, mountains, rivers, forests, etc. TCloud can help the decision maker or planner or common users to get enough information for their further research and studies. It has used open source GIS quantum GIS for the development of spatial database whereas QGIS plugin has been linked with quantum GIS for invoking cloud computing environment. It has also discussed the various spatial overlay analysis in TCloud environment

Steady-state and laser flash photolysis studies of 1-aziridinyl-1,2-dibenzoylalkenes

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Hybrid mist-cloud systems for large scale geospatial big data analytics and processing: opportunities and challenges

The cloud and fog computing paradigms are developing area for storing, processing, and analysis of geospatial big data. Latest trend is mist computing which boost fog and cloud concepts for computing process where edge devices are used to help increase throughput and reduce latency to support at client edge. The present research article discussed the mist computing emergence for geospatial analysis of data from various geospatial applications. It also created a framework based on mist computing, i.e., MistGIS for analytics in mining domain from geospatial big data. The developed MistGIS platform is used in Tourism Information Infrastructure Management and Faculty Information Retrial System. Tourism Information Infrastructure Management is to assimilate entire geospatial data in context to travel/tourism places constitute of various lakes, mountains, rivers, forests, temples, mosques, churches, monuments, etc. It can aid all the stakeholders or users to acquire sufficient data in subsequent research studies. In this study, it has taken the Temple City of India, Bhubaneswar as the case study. Whereas Faculty Information Retrial System facilitated many functionalities with respect to finding the detail information of faculties according to their research area, contact details, and email ids, etc in all 31 National Institutes of Technology (NITs) in India. The framework is built with the Raspberry Pi microprocessor. The MistGIS platform has been confirmed by prelude analysis which includes cluster and overlay. The outcome show that mist computing assist cloud and fog computing to provide the analysis of geospatial big data

<i>PQ-Mist</i>: Priority Queueing-Assisted Mist–Cloud–Fog System for Geospatial Web Services

The IoT and cloud environment renders enormous quantities of geospatial information. Fog and mist computing is the scaling technology that handles geospatial data and sends it to the cloud storage system through fog/mist nodes. Installing a mist–cloud–fog system reduces latency and throughput. This mist–cloud–fog system has processed different types of geospatial web services, i.e., web coverage service (WCS), web processing services (WPS), web feature services (WFS), and web map services (WMS). There is an urgent requirement to increase the number of computer devices tailored to deliver high-priority jobs for processing these geospatial web services. This paper proposes a priority-queueing assisted mist–cloud–fog system for efficient resource allocation for high- and low-priority tasks. In this study, WFS is treated as high-priority service, whereas WMS is treated as low-priority service. This system dynamically allocates mist nodes and is determined by the load on the system. In addition to that, the assignment of tasks is determined by priority. Not only does this classify high-priority tasks and low-priority tasks, which helps reduce the amount of delay experienced by high-priority jobs, but it also dynamically allocates mist devices within the network depending on the computation load, which helps reduce the amount of power that is consumed by the network. The findings indicate that the proposed system can achieve a significantly lower delay for higher-priority jobs for more significant rates of task arrival when compared with other related schemes. In addition to this, it offers a technique that is both mathematical and analytical for investigating and assessing the performance of the proposed system. The QoS requirements for each device demand are factored into calculating the number of mist nodes deployed to satisfy those requirements