Fog2Fog: Augmenting Scalability in Fog Computing for Health GIS Systems

Barik, Rabindra K.; Dubey, Harishchandra; Sasane, Sapana Ashok; Misra, Chinmaya; Constant, Nicholas; Mankodiya, Kunal

doi:10.1109/chase.2017.83

Cited by 30 publications

(7 citation statements)

References 5 publications

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“…[85]. When the QGIS plugins were considered (the same search was refined to "QGIS"), 93.8% of the previous publications were found, meaning that GIS desktop applications are mainly developed under QGIS open-source software, for instance related to health diseases [90], which also involves environmental variables, forestry [19], groundwater vulnerability [20], erosion risk/coastal management [21,91], soil consumption [92,93], meteorology [13], land use [94], Bayesian spatial models [12], geospatial data analysis and processing [95], estimation of tree parameters [18,96] and remote sensing [16,17]. The three major research areas were: environmental sciences, water resources and physical geography.…”

Section: Gis Open-source Desktop Applicationsmentioning

confidence: 99%

GIS Open-Source Plugins Development: A 10-Year Bibliometric Analysis on Scientific Literature

Duarte

Teodoro

2021

Geomatics

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Background: The advent of Geographical Information Systems (GIS) has changed the way people think and interact with the world. The main objectives of this paper are: (i) to provide an overview of 10 years (2010–2020) regarding the creation/development of GIS open-source applications; and (ii) to evaluate the GIS open-source plugins for environmental science. Methods: In the first objective, we evaluate the publications regarding the development of GIS open-source geospatial software in the last 10 years, considering desktop, web GIS and mobile applications, so that we can analyze the impact of this type of application for different research areas. In the second objective, we analyze the development of GIS open-source applications in the field of environmental sciences (with more focus on QGIS plugins) in the last 10 years and discuss the applicability and usability of these GIS solutions in different environmental domains. A bibliometric analysis was performed using Web of Science database and VOSViewer software. Conclusions: We concluded that, in general, the development of GIS open-source applications has increased in the last 10 years, especially GIS mobile applications, since the big data and Internet of Things (IoT) era, which was expected given the new advanced technologies available in every area, especially in GIS.

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Section: Gis Open-source Desktop Applicationsmentioning

confidence: 99%

GIS Open-Source Plugins Development: A 10-Year Bibliometric Analysis on Scientific Literature

Duarte

Teodoro

2021

Geomatics

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“…Social apps is the natural platform for GeoAI data collection, transmission and functional applications. However, the traditional model based on central institution storage, verification and authorization has restrictions on the access of patients and doctors to databases [30], [31], which consumes a lot of resources and time for authority review and data verification.…”

Section: Related Workmentioning

confidence: 99%

GeoAI-based Epidemic Control with Geo-Social Data Sharing on Blockchain

Peng

Bai

Xiong

et al. 2021

2020 IEEE International Conference on E-Health Networking, Application &Amp; Services (HEALTHCOM)

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Epidemics especially those caused by major contagious diseases have entailed huge losses in human history. The fights have thus never stopped to prevent pandemics. Due to its acute outbreak, is generally susceptible to the population regardless of ages, so strict quarantine of the infections becomes the most effective means for the epidemic control, which has been proved in the prevention of other contagious diseases such as SARS and H1N1. The key strategy widely used to find infected and suspected patients is still the epidemiological tracking of confirmed cases. However, this may fail to identify infections especially when patients do not show any symptoms. Therefore, the approach to rapid, effective, and simple infection identification is essential to prevent the spread of a contagious disease. This paper proposes to leverage a social apps and Geospatial artificial intelligence (GeoAI) with Blockchain to effectively identify infections with privacy concern. Since people widely use social apps, a large scale of social data with geospatial information could be easily collected and kept on Blockchain with privacy preservation, which thus provides a framework of decentralized, tamper-proof, and privacy-preserved information sharing. With the support of GeoAI, which analyzes the spatial distribution of diseases from the shared data, we could study the influence factors based on spatial propagation of contagious diseases for infection identification. Since WeChat is widely used in China, we take COVID-19 as an example to use the experiments on real-life datasets demonstrate the effectiveness of our method, and provide insight into epidemic control in terms of geo-social data sharing.

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“…In [21], [45]- [47], [51] the fog layer is used to perform local data processing to analyze the health data while in [44], [50] the fog layer can only perform local data processing depending on the requirement of the health application. Meanwhile, in [48], the fog layer is used to perform both local processing of the health data and filtering the analyzed data before uploading to the cloud for further analysis to reduce the redundancy. In addition, the cloud is usually used to hold the analyzed health data for either permanent storage [46] or both permanent storage and long term analysis [21], [44], [45], [47]- [51].…”

Section: The Proposed Fog-based Health Monitoring Systemmentioning

confidence: 99%

Energy Efficient Fog-Based Healthcare Monitoring Infrastructure

et al. 2020

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Recent advances in mobile technologies and cloud computing services have inspired the development of cloud-based real-time health monitoring systems. However, the transfer of health-related data to the cloud contributes to the burden on the networking infrastructures, leading to high latency and increased power consumption. Fog computing is introduced to relieve this burden by bringing services to the users' proximity. This study proposes a new fog computing architecture for health monitoring applications based on a Gigabit Passive Optical Network (GPON) access network. An Energy-Efficient Fog Computing (EEFC) model is developed using Mixed Integer Linear Programming (MILP) to optimize the number and location of fog devices at the network edge to process and analyze the health data for energy-efficient fog computing. The performance of the EEFC model at low data rates and high data rates health applications is studied. The outcome of the study reveals that a total energy saving of 36% and 52% are attained via processing and analysis the health data at the fog in comparison to conventional processing and analysis at the central cloud for low data rate application and high data rate application, respectively. We also developed a real-time heuristic; Energy Optimized Fog Computing (EOFC) heuristic, with energy consumption performance approaching the EEFC model. Furthermore, we examined the energy efficiency improvements under different scenarios of devices idle power consumption and traffic volume.

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Fog2Fog: Augmenting Scalability in Fog Computing for Health GIS Systems

Cited by 30 publications

References 5 publications

GIS Open-Source Plugins Development: A 10-Year Bibliometric Analysis on Scientific Literature

GIS Open-Source Plugins Development: A 10-Year Bibliometric Analysis on Scientific Literature

GeoAI-based Epidemic Control with Geo-Social Data Sharing on Blockchain

Energy Efficient Fog-Based Healthcare Monitoring Infrastructure

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